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Cloud Application Development, , Morgan Kauffman, USA, , McGraw Hill, India, , China Machine Press, China, , 9.1, , 1

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Unit 3 : Introduction to Task Programming, , Manjrasoft, IBM Power Systems, , • Characterizing a Task, • Computing Categories, • Framework for Task Computing, • Task-Based Application Models, • Work Flow Applications with Task Dependencies, • Developing Applications with the Task Model, • Controlling Task Execution, Task Libraries, Web, Services Integration, • Developing Parameter Sweep Applications, , 2

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Unit 3 - Objectives, , Manjrasoft, IBM Power Systems, , After completing this unit you should be able to, • High-Throughput Computing: Task Computing, • Characterizing a task, • Computing Categories, • Frameworks for Task Computing, • Task based Application Models, • Aneka Task-Based Programming, • Developing Applications with the Task Model, • Task Libraries, • Web Services Integration, • Developing Parameter Sweep Applications, • Managing Workflows, • Summary, 3

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Unit 3 - Objectives, , Manjrasoft, IBM Power Systems, , After completing this unit you should be able to, • High-Throughput Computing: Task Computing, • Characterizing a task, • Computing Categories, • Frameworks for Task Computing, • Task based Application Models, • Aneka Task-Based Programming, • Developing Applications with the Task Model, • Task Libraries, • Web Services Integration, • Developing Parameter Sweep Applications, • Managing Workflows, • Summary, 4

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High-Throughput Computing : Task Programming, , Manjrasoft, IBM Power Systems, , • Task computing is a wide area of distributed system, programming encompassing several different models of, architecting distributed applications, which, eventually, are, based on the same fundamental abstraction: the task., , • A task generally represents a program, which might require, input files and produce output files as a result of its execution., , • Applications are then constituted by a collection of tasks., • These are submitted for execution and their output data is, collected at the end of their execution., , • The way in which tasks are generated, the order in which they, are executed, or whether they need to exchange data,, differentiate the application models falling under the umbrella of, task programming., 5

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Task Computing, , Manjrasoft, IBM Power Systems, , • Organizing an application in terms of tasks is the most, intuitive and common practice for developing parallel and, distributed computing applications., , • A task identifies one or more operations that produce a, distinct output and that can be isolated as a single logical, unit., • In practice, a task is represented as a distinct unit of code,, or a program, that can be separated and executed in a, remote runtime environment., , • Programs are the most common option for representing, tasks, especially in the field of scientific computing, which, has leveraged distributed computing for its computational, needs., 6

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Task Computing Scenario, , Manjrasoft, IBM Power Systems, , • Multi-threaded programming is mainly concerned with, providing a support for parallelism within a single machine., , • Task computing provides distribution by harnessing the, compute power of several computing nodes., , • Hence, the presence of a distributed infrastructure is explicit, in this model. Historically, the infrastructures that have been, leveraged to execute tasks are clusters, supercomputers,, and computing Grids., • Now Clouds have emerged as an attractive solution to obtain, a huge computing power on-demand for the execution of, distributed applications. In order to achieve it, a suitable, middleware is needed., 7

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Task Computing Scenario, , Manjrasoft, IBM Power Systems, , Access, Node(s), Tasks, , Middleware, , Remote, Environment, Local, Node, Computing, Nodes, , 8

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Task Computing Scenario, , Manjrasoft, IBM Power Systems, , • The middleware is a software layer that enables the coordinated use, •, , •, •, •, , of multiple resources, which are drawn from a Data Center or, geographically distributed networked computers., A user submits the collection of tasks to the access point(s) of the, middleware, which will take care of scheduling and monitoring the, execution of tasks., Each computing resource provides an appropriate runtime, environment, which may vary from implementation to implementation, (a simple shell, a sandboxed environment, or a virtual machine)., Task submission is normally done by using the APIs provided by the, middleware, whether they are a web or a programming language, interface., Appropriate APIs are also provided to monitor task status and collect, their results upon the completion., 9

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Task Based Computing Scenario, , Manjrasoft, IBM Power Systems, , • As task abstraction is general, there exist different models of, distributed applications falling under the umbrella of task, computing., , • Despite this, it is possible to identify a set of common, operations that the middleware needs to support the creation, and execution of task based applications. They are:, –, –, –, –, –, , Coordination and scheduling of tasks for execution on a set of remote nodes., Movement of programs to remote nodes and management of their dependencies., Creation of an environment for execution of tasks on the remote nodes., Monitoring the task’s execution and inform the user about its status., Access to the output produced by the task., , • Models for task computing may differ in the way tasks are, scheduled which in turn depends on whether tasks are, interrelated or they need to communicate amongst themselves., 10

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Unit 3 - Objectives, , Manjrasoft, IBM Power Systems, , After completing this unit you should be able to, • High-Throughput Computing: Task Computing, • Characterizing a task, • Computing Categories, • Frameworks for Task Computing, • Task based Application Models, • Aneka Task-Based Programming, • Developing Applications with the Task Model, • Task Libraries, • Web Services Integration, • Developing Parameter Sweep Applications, • Managing Workflows, • Summary, 11

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Characterizing a Task, , Manjrasoft, IBM Power Systems, , • A task is a general abstraction identifying a program or a, combination of programs that constitute a computing unit of, a distributed application with a tangible output., , • It represents a component of an application that can be, logically isolated and executed separately. Distributed, applications are composed of tasks, whose collective, execution and interrelations define the nature of the, applications., • A task can be represented by different elements:, – A shell script composing together the execution of several, applications., – A single program., – A unit of code (a Java/C++/.NET class) that executes within the, context of a specific runtime environment., 12

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Characterizing a Task (Contd…), , Manjrasoft, IBM Power Systems, , • A task is generally characterized by input files, executable code (programs,, shell scripts, etc.), and output files., , • In many cases the common runtime environment in which tasks executes, is, represented by the operating system or an equivalent sandboxed, environment., , • A task may also need specific software appliances on the remote execution, nodes in addition to the library dependencies that can be transferred to the, node., , • Some distributed applications may have additional constraints., • For example, distributed computing frameworks that present the abstraction, of task at programming level, by means of a class to inherit or an interface, to implement, might require additional constraints (i.e. compliance to the, inheritance rules) but also a richer set of features that can be exploited by, developers., , • According to the specific model of application, tasks might have, dependencies., 13

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Unit 3 - Objectives, , Manjrasoft, IBM Power Systems, , After completing this unit you should be able to, • High-Throughput Computing: Task Computing, • Characterizing a task, • Computing Categories, • Frameworks for Task Computing, • Task based Application Models, • Aneka Task-Based Programming, • Developing Applications with the Task Model, • Task Libraries, • Web Services Integration, • Developing Parameter Sweep Applications, • Managing Workflows, • Summary, 14

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Computing Categories, , Manjrasoft, IBM Power Systems, , • According to the specific nature of the problem, different, categories for task computing have been proposed over, time., , • These categories do not enforce any specific application, model but provide an overall view of the characteristics of, the problems., • They implicitly impose requirements on the infrastructure, and the middleware., • Applications falling in this category are:, – High-Performance Computing (HPC), – High-Throughput Computing (HTC), – Many Tasks Computing (MTC)., 15

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High Performance Computing (HPC), , Manjrasoft, IBM Power Systems, , • High Performance Computing is the use of distributed computing facilities, for solving problems that need large computing power., , • Historically, supercomputers and clusters are specifically designed to, support HPC applications that are developed to solve grand challenging, problems in science and engineering., , • The general profile of HPC applications is constituted by a large collection of, compute intensive tasks which needs to be processed in a short period of, time., , • It is common to have parallel and tightly coupled tasks, which require low, latency interconnection network to minimize the data exchange time., , • The metrics to evaluate HPC systems are FLOPS (now Tera-FLOPS or, even Peta-FLOPS), , • Flops identify the number of floating point operations per second that a, computing system can perform., , 16

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Unit 3 - Objectives, , Manjrasoft, IBM Power Systems, , After completing this unit you should be able to, • High-Throughput Computing: Task Computing, • Characterizing a task, • Computing Categories, • Frameworks for Task Computing, • Task based Application Models, • Aneka Task-Based Programming, • Developing Applications with the Task Model, • Task Libraries, • Web Services Integration, • Developing Parameter Sweep Applications, • Managing Workflows, • Summary, 17

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High Throughput Computing (HTC), , Manjrasoft, IBM Power Systems, , • High Throughput Computing is the use of distributed computing facilities for, applications requiring large computing power over a long period of time., , • HTC systems need to be robust and reliably operate over a long time scale., • Traditionally, computing Grids composed of heterogeneous resources, (clusters, workstations, and volunteer desktop machines), have been used, to support HTC., , • The general profile of HTC applications is that are made up of a large, number of tasks, whose execution can last for a considerable amount of, time (i.e. weeks or months)., , • Classical examples of such applications are scientific simulations or, statistical analyses., , • It is quite common to have independent tasks that can be scheduled in, distributed resources as they do not need to communicate., , • HTC systems measure their performance in terms of jobs completed per, month., 18

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Many Tasks Computing (MTC), , Manjrasoft, IBM Power Systems, , • Many Tasks Computing model started receiving attention, recently and it covers a wide variety of applications., , • It aims to bridge the gap between HPC and HTC., • MTC is similar to High Throughput Computing, but it, concentrates on the use of many computing resources over, a short period of time to accomplish many computational, tasks., , • In brief, MTC denotes high-performance computations, comprising multiple distinct activities, coupled via file system, operations., , 19

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Many Tasks Computing (MTC), , Manjrasoft, IBM Power Systems, , • MTC is the heterogeneity of tasks that might be of considerably, different nature:, – tasks may be small or large, single processor or multi-processor,, compute-intensive or data-intensive, static or dynamic,, homogeneous or heterogeneous., • The general profile of MTC applications include loosely coupled, applications that are generally communication-intensive but not, naturally expressed using message passing interface commonly, found in HPC, drawing attention to the many computations that are, heterogeneous but not embarrassingly parallel., • Given the large amount of tasks commonly composing MTC, applications, any distributed facility with a large availability of, computing elements is able to support MTC., • Such facilities include: supercomputers, large clusters, and, emerging Cloud infrastructures., 20

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Frameworks for Task Computing, , Manjrasoft, IBM Power Systems, , • There are several frameworks that can be used to support, the execution of task-based applications on distributed, computing resources including Clouds., , • Some popular software systems supporting task computing, framework are:, – Condor, – Globus Toolkit, – Sun Grid Engine (SGE), – BOINC, – Nimrod/G, – Aneka., , 21

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Frameworks for Task Computing, , Manjrasoft, IBM Power Systems, , • Architecture of all these systems is similar to the general, reference architecture depicted in Task computing scenario., , • They consist of two main components: a scheduling node, (one or more) and worker nodes., , • The organization of the components of the system may vary., For example, multiple scheduling nodes can be organized in, hierarchical structures., • This configuration is quite common in case of the, middleware for computing Grids, which harness a variety of, distributed resources from one or more organizations or, sites., • Each of these sites may have their own scheduling engine,, especially if the system contributes to the Grid but also, serves local users., 22

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Frameworks for Task Computing, , Manjrasoft, IBM Power Systems, , • A classic example is the cluster setup where the system might feature an installation, of Condor or SGE for batch job submission; these services are generally used, locally to the site, but the cluster can be integrated into a larger Grid where metaschedulers such as GRAM (Globus Resource Allocation Manager) can dispatch a, collection of jobs to the cluster., • These nodes have indexing services that allow users to identify the available, resources in the system, its current status, and the available schedulers., , • For worker nodes, they generally provide a sandboxed environment where tasks are, executed on behalf of a specific user or within a given security context limiting the, operations that can be performed by programs such as file system access., • File staging is also a fundamental feature supported by these systems. Clusters are, normally equipped with shared file systems and parallel I/O facilities., , • Grids provide users with different staging facilities such as credential access to, •, , remote worker nodes or automated staging services that transparently move files, from user local machine to remote nodes., Globus Resource Allocation Manager is a software component of the Globus Toolkit, that is in charge of locating, submitting, monitoring, and cancelling jobs in Grid, computing systems., 23

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Frameworks for Task Computing, , Manjrasoft, IBM Power Systems, , • Condor is probably the most widely used and long-lived middleware for managing, clusters, idle workstations, and a collection of clusters., • Condor-G is a version of Condor supporting the integration with Grid computing, resources, such as those managed by Globus., • Condor supports common features of batch queuing systems along with the capability of, check-pointing of jobs and management of overload nodes., , • It provides a powerful job-resource matching mechanisms, which schedule jobs only on, resources having the appropriate runtime environment., • It can handle both serial and parallel jobs on a wide variety of resources., • It is used by hundreds of organizations in industry, government, and academia to, manage infrastructures ranging from a handful to a well over thousands of workstations., • Sun Grid Engine (SGE), now Oracle Grid Engine, is a middleware for workload and, distributed resource management. Initially developed to support the execution of jobs on, clusters,, , • Sun Grid Engine integrated additional capabilities and now is able to manage, heterogeneous resources and constitutes a middleware for Grid computing., • It supports the execution of parallel, serial, interactive, and parametric jobs and features, advanced scheduling capabilities such as budget-based and group-based scheduling,, scheduling applications that have deadlines, custom policies, and advance reservation., 24

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Frameworks for Task Computing, , Manjrasoft, IBM Power Systems, , • The Globus Toolkit is a collection of technologies enabling Grid, Computing. It provides a comprehensive set of tools for sharing, computing powers, databases, and other services across corporate,, institutional, and geographic boundaries without sacrificing local, autonomy., • The toolkit features software services, libraries, and tools for, resource monitoring, discovery, and management and also security, and file management., • The Globus Toolkit addresses core issues of Grid computing: the, management of a distributed environment composed by, heterogeneous resources spanning across different organizations, with all this condition implies in terms of security and interoperation., • In order to provide a valid support for Grid computing in such, scenario the toolkit defines a collection of interfaces and protocol for, interoperation that enable different system to integrate with each, other and expose resources outside their boundaries., 25

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Frameworks for Task Computing, , Manjrasoft, IBM Power Systems, , • Nimrod/G is a tool for automated modeling and execution of, •, •, , •, •, •, , parameter sweep applications (parameter studies) over global, computational Grids., It provides a simple declarative parametric modeling language for, expressing parametric experiments., A domain expert can easily create a plan for a parametric experiment, and use the Nimrod/G system to deploy jobs on distributed resources, for execution., It has been used for a very wide range of applications over the years,, ranging from quantum chemistry to policy and environmental impact., Moreover, it uses novel resource management and scheduling, algorithms based on economic principles., Specifically, it supports deadline and budget constrained scheduling, of applications on distributed Grid resources to minimize the, execution cost and at the same deliver results in timely manner., 26

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Frameworks for Task Computing, , Manjrasoft, IBM Power Systems, , • BOINC (Berkley Open Infrastructure for Network Computing) is framework for, volunteer and Grid computing., • It allows turning desktop machines into volunteer computing nodes that are, leveraged to run jobs when such machines become inactive. BOINC is composed by, two main components: the BOINC server and the BOINC client., , • The former is the central node keeping track of all the available resources and, , •, , scheduling jobs, while the latter is the software component that is deployed on, desktop machines and that creates the BOINC execution environment for job, submission., Given the volatility of BOINC clients, BOINC supports job check-pointing and, duplication. Even if mostly focused on volunteer computing, BOINC systems can be, easily setup to provide a more stable support for job execution by creating, computing Grids with dedicated machines., , • In order to leverage BOINC it is necessary to create an application project. When, •, , installing BOINC clients, users can decide the application project to which they want, to donate the CPU cycles of their computer., Currently, there are several projects, ranging from medicine to astronomy and, cryptography, are running on the BOINC infrastructure., 27

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Unit 3 - Objectives, , Manjrasoft, IBM Power Systems, , After completing this unit you should be able to, • High-Throughput Computing: Task Computing, • Characterizing a task, • Computing Categories, • Frameworks for Task Computing, • Task based Application Models, • Aneka Task-Based Programming, • Developing Applications with the Task Model, • Task Libraries, • Web Services Integration, • Developing Parameter Sweep Applications, • Managing Workflows, • Summary, 28

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Task-based Application Models, , Manjrasoft, IBM Power Systems, , • There exist several models that are based on the concept of, task as the fundamental unit for composing distributed, applications., , • What makes them different is the way in which tasks are, generated, the relations they have with each other, the, presence of dependencies, or other conditions., • for example a specific set of services in the runtime, environment, that have to be met., • Here, we quickly review the most common and popular models, based on the concept of task., – Embarrassingly Parallel Applications, – Parameter Sweep Applications, – MPI Applications, 29

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Embarrassingly Parallel Applications, , Manjrasoft, IBM Power Systems, , • Embarrassingly parallel applications constitute the most simple and intuitive, category of distributed applications., • As already discussed in the previous chapter, embarrassingly parallel applications, are constituted by a collection of tasks that are independent from each other and, that can be executed in any order., , • The tasks might be of the same type or of different nature and they do not need to, •, , communicate among themselves., This category of applications is supported by the majority of the frameworks for, distributed computing., , • Since tasks do not need to communicate, there is a lot of freedom for the way in, which they are scheduled., • Tasks can be executed in any order and there is no specific requirement for tasks to, be executed at the same time., , • Therefore, scheduling of these applications is simplified and mostly concerned with, the optimal mapping of tasks to available resources., • Frameworks and tools supporting embarrassingly parallel applications are the, Globus Toolkit, BOINC, and Aneka., 30

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Embarrassingly Parallel Applications, , Manjrasoft, IBM Power Systems, , • There are several problems that can be modeled as embarrassingly, , •, •, •, •, •, , parallel., These include image and video rendering, evolutionary optimization,, model forecasting, etc., In case of image and video rendering the task is represented by the, rendering of a pixel (more likely a portion of the image) or a frame, respectively., For evolutionary optimization meta heuristics, a task is identified by a, single run of the algorithm with a given parameter set., The same applies to model forecasting applications., In general, scientific applications constitute a considerable source of, embarrassingly parallel applications, even though they mostly fall, into the more specific category of parameter sweep applications., , 31

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Parameter Sweep Applications, , Manjrasoft, IBM Power Systems, , • Parameter sweep applications are a specific class of embarrassingly, •, •, •, •, , parallel applications whose tasks are identical in their nature and, differ only by the specific parameters used to execute them., Parameter sweep applications are identified by a template task and a, set of parameters., The template task defines the operations that will be performed on, the remote node for the execution of tasks., The template task is parametric and the parameter set identifies the, combination of variables whose assignments specialize the template, task into a specific instance., The combination of parameters together with their range of, admissible values identifies the multi-dimensional domain of the, application and each point in this domain identifies a task instance., , 32

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Parameter Sweep Applications, , Manjrasoft, IBM Power Systems, , • Any distributed computing framework providing support for, , •, •, •, , •, , embarrassingly parallel applications can also support the execution, of parameter sweep applications, since the tasks composing the, application can be executed independently from each other., The only difference is that the tasks that will be executed are, generated by iterating over all the possible and admissible, combinations of parameters., This operation can be performed frameworks natively or tools that, are part of the distributed computing middleware., For example, Nimrod/G is natively designed to support the execution, of parameter sweep applications., Aneka provides client-based tools for visually composing a template, task, define parameters, and iterate over all the possible, combinations of such parameters., 33

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Parameter Sweep Applications, , Manjrasoft, IBM Power Systems, , • There is a plethora of applications that fall into this category., , •, , Mostly, they come from the scientific computing domain:, evolutionary optimization algorithms, weather forecasting, models, computational fluid dynamics applications, Monte, Carlo methods, and many others., For example, in the case of evolutionary algorithms it is, possible to identify the domain of the applications as a, combination of the relevant parameters of the algorithm., , • For genetic algorithms these might be the number of, individuals of the population used by the optimizer and the, number of generations for which to run the optimizer., • The following example in pseudo-code demonstrates how to, generate the tasks for case previously discussed., 34

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Parameter Sweep Applications, , Manjrasoft, IBM Power Systems, , • The algorithm sketched in the example defines a bidimensional domain composed by discrete variables and, then iterated over each combination of individuals and, generations to generate all the tasks composing the, application., , • In this case 20 tasks are generated., • The function generate_task is specific to the application and, creates the task instance by substituting the values of indiv, and generation to the corresponding variables in the, template definition., • The function submit_task is specific to the middleware used, and performs the actual task submission., 36

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Parameter Sweep Applications, , Manjrasoft, IBM Power Systems, , • A template task is in general a composition of operations concerning the, execution of legacy applications with the appropriate parameters and set of, file system operations for moving data., , • Therefore, frameworks natively supporting the execution of parameter, sweep applications often provide a set of useful commands that are used in, to manipulate or to operate with files., , • Template task is often expressed as single file that composes together the, commands provided. The commonly available commands are:, , – Execute: executes a program on the remote node., – Copy: copies a file to/from the remote node., – Substitute: substitute the parameter values with their placeholders inside, a file., , – Delete: deletes a file., • All these commands can operate with parameter that are, substituted with their actual values for each task instance., 37

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Parameter Sweep Applications, , Manjrasoft, IBM Power Systems, , • The Aneka Parameter Sweep file defines the template task for executing the, BLAST application., , • The file is an XML document containing several sections, the most important, sections are sharedFiles, parameters, and task. The first contains the, definition of the parameters that will customize the template task. Two, different types of parameters are defined: a single value and a range, parameter., , • The second section contains the files that are required to execute the task,, while the third one specifies the operations that characterize the template, task., , • The task has a collection of input and output files whose local and remote, paths are defined, and a collection of commands., , • In the case presented a simple execute command is shown. With respect to, the previous example there is no need to explicitly move the files to the, remote destination but this operation is automatically performed by Aneka., , 38

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MPI Applications, , Manjrasoft, IBM Power Systems, , • MPI (Message Passing Interface) is a specification for, developing parallel programs which communicate by, exchanging messages. Compared to earlier models, MPI, introduces the constraint of communication which involves, that MPI tasks need to run at the same time., , • MPI has originated as an attempt to create a common, ground from the several distributed shared memory and, message passing infrastructures available for distributed, computing., , • Nowadays, it has become a de-facto standard for developing, portable and efficient message passing HPC applications., Interface specification have been defined and implemented, for C/C++ and FORTRAN., 40

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MPI Applications, , Manjrasoft, IBM Power Systems, , • MPI provides developers with a set of routines that:, – Manage the distributed environment where MPI programs are, executed., – Provide facilities for point to point communication., – Provide facilities for group communication., – Provide support for data structure definition and memory allocation., – Provide basic support for synchronization with blocking calls., , • The general reference architecture is depicted. A distributed, application in MPI is composed by a collection of MPI, processes which are executed in parallel in a distributed, infrastructure supporting MPI (most likely a cluster or nodes, leased form Clouds)., , 41

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MPI reference Scenario, , Manjrasoft, IBM Power Systems, , MPI_COMM_WORLD, , 2, 0, , 8, , 1, , 10, , 9, , 7, , 6, , 4, , 3, , 0, , 7, , 4, 11, , Group A, , 3, , 1, , Communicator A, , 6, , Group B, , 6, , 5, , 1, 4, , 3, , 10, , 2, , 9, , 8, , 5, , 1, , 2, , 0, , 11, , 5, , 2, 4, , 0, 3, , Communicator B, , 42

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MPI Applications, , Manjrasoft, IBM Power Systems, , • MPI applications that share the same MPI runtime are by, default part of a global group called MPI_COMM_WORLD., , • Within this group all the distributed processes have a unique, •, •, , •, , identifier that allows the MPI runtime to localize and address, them., It is possible to create specific groups as subsets of this global, group for example for isolating all the MPI processes that, belong to the same application., Each MPI process is assigned a rank within the group he, belongs to., The rank is a unique identifier that allows processes to, communicate with each other within a group. Communication is, made possible by means of a communicator component that, can be defined for each group., 43

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MPI Program Structure, , Header, Section, , Manjrasoft, IBM Power Systems, , MPI Include File, Prototypes declaration, , Do Work, , Serial Code, , MPI Environment Initialization, , Code Section, , Do Work and Message Passing, , Parallel Code, , MPI Environment Shutdown, , Do Work, , Serial Code, , 44

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MPI Applications, , Manjrasoft, IBM Power Systems, , • In order to create an MPI application it is necessary to define the code for, the MPI process that will be executed in parallel., , • . The section of code that is executed in parallel is clearly identified by two, operations that setup the MPI environment and shut it down respectively. In, the code section defined within these two operations it is possible to use all, the MPI functions to send or receive messages in either asynchronous or, synchronous mode., , • The diagram might suggest the MPI might allow the definition of completely, symmetrical applications since the portion of code executed in each node is, the same., , • In reality, it is possible to implement distributed applications based on, complex communication patterns by differentiating the operations performed, by each node according to the rank of the program, which is known at, runtime., , • A common model used in MPI is the master-worker model, where one MPI, process (usually the one with rank 0) coordinates the execution of others, that perform the same task., 45

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MPI Applications, , Manjrasoft, IBM Power Systems, , • Once the program has been defined in one of the, available MPI implementation, it is compiled with a, modified version of the compiler for the language., • This compiler introduces additional code in order to, properly and to manage the MPI runtime., • The output of the compilation process can be run as a, distributed application by using a specific tool, provided with the MPI implementation., , 46

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MPI Applications, , Manjrasoft, IBM Power Systems, , • A general installation supporting the execution of the MPI application is composed, by a cluster., • In this scenario MPI is normally installed in the shared file system and an MPI, daemon is started on each node of the cluster in order to coordinate the parallel, execution of MPI applications., , • Once the environment is set up, it is possible to run parallel applications by using the, •, , tools provided with the MPI implementation and specify several options such as the, number of nodes to use to run the application., At present there are several MPI implementations that can be leveraged to develop, distributed applications, and the MPI specifications have currently reached version 2., , • One of the most popular MPI software environments (http://www.mcs.anl.gov/mpi/) is, •, , developed by the Argonne National Laboratory, USA., MPI has gained a lot of success as a parallel and distributed programming model for, CPU intensive mathematical computations such as linear systems solvers, matrix, computations, finite element computations, linear algebra, and numerical, simulations., , 47

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Workflow Applications with Task Dependencies, , Manjrasoft, IBM Power Systems, , • Workflow applications are characterized by a, collection of tasks that do exhibit dependencies, among them., • Such dependencies, which are mostly data, dependencies (i.e. the output of one task is a, prerequisite of another task), determine the way in, which the applications are scheduled and also where, they are scheduled., • Concerns in this case are related to provide a feasible, sequencing of tasks and how to optimize the, placement of tasks so that the movement of data is, minimized., 48

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What is a Workflow?, , Manjrasoft, IBM Power Systems, , • The term workflow has a long tradition among the business, community, where the term is used to describe a, composition of services that all together accomplish a, business process., , • As defined by the Workflow Management Coalition a, workflow is the automation of a business process, in whole, or part, during which documents, information, or tasks are, passed from one participant (a resources; human or, machine) to another for action, according to a set of, procedural rules., , • The concept of workflow as a structured execution of tasks,, which have dependencies on each other, has demonstrated, to be useful to express many scientific experiments, and, gave birth to the idea of scientific workflow., 49

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What is a Workflow? Contd…, , Manjrasoft, IBM Power Systems, , • Many scientific experiments are composed as a combination of, •, , •, , •, •, , problem solving components, which, connected in a particular order,, define the specific nature of the experiment., When such experiments exhibit a natural parallelism and need to, execute a large amount of operations, or deal with huge quantities of, data, it makes sense to execute them on a distributed infrastructure., In the case of scientific workflows, the process is identified by an, application to run, the elements that are passed among participants, are mostly tasks and data, and the participants are mostly computing, or storage nodes., The set of procedural rules are defined by a workflow definition, scheme which guides the scheduling of the application., A scientific workflow generally involves data management, analysis,, simulation and middleware supporting the execution of workflow., 50

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What is a Workflow? (Contd…), , Manjrasoft, IBM Power Systems, , • A scientific workflow is generally expressed by a directed, acyclic graph (DAG) defining the dependencies among tasks or, operations., , • The nodes on the DAG represent the tasks to be executed in a, workflow application, while the arcs connecting the nodes, identify the dependencies among tasks and the data paths that, connect the tasks., • The most common dependency that is realized through a DAG, is data dependency, which means that the output files of a task, (or some of them) constitute the input files of another task., • This dependency is represented as an arc originating from the, node identifying the first task and terminating in the node, identifying the second task., 51

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Sample Montage Workflow, , Manjrasoft, IBM Power Systems, , transfer the template header, transfer the image file, re-projection of the image, , calculating the difference, , fit a common plane, , background modeling, , background correction, , adding images to the final mosaic, , register the mosaic in RLS, , 52

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Workflow Technologies, , Manjrasoft, IBM Power Systems, , • Business oriented computing workflows are defined as an, composition of services and there exist specific languages and, standards for the definition of workflows, such as BPEL, (Business Process Execution Language)., , • in the case of scientific computing there is no common ground, for defining workflows but several solutions and workflow, languages coexist., , • Despite such differences it is possible to identify an abstract, reference model for a Workflow Management System., , • Design tools allow users to visually compose a workflow, application. This specification is normally stored in the form of, an XML document, based on a specific workflow language, and, constitutes the input of the workflow engine, which controls the, execution of the workflow by leveraging a distributed, 53, infrastructure.

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Workflow Technologies, , Manjrasoft, IBM Power Systems, , • In most of the cases, workflow engine is a client side, component that might interact directly with resources or with, one or several middleware for executing the workflow., , • Some frameworks can natively support the execution of, workflow applications by providing a scheduler capable of, directly processing the workflow specification., • Some of the most relevant technologies for designing and, executing workflow-based applications are:, – Kepler, – DAGMan, – Cloudbus Workflow Management System, – Offspring., 54

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Abstract Model of Workflow System, , Manjrasoft, IBM Power Systems, , Users, Workflow, Design &, Definition, Build Time, , Workflow Application Modeling & Definition Tools, Information, Services, , Workflow, Specification, , Run Time, , Resource Info Service, Application Info Service, , Workflow Enactment, Engine, Workflow, Execution &, Control, , Interaction with, Distributed, Resources, , Task + Data Scheduler, Data, Movement, , Dependency, Manager, , Data, Provenance, , Runtime, Manager, , Interaction with, Information Services, , Language, Interpreter, Fault, Management, , Middleware Supporting Clusters, Grids, and, Clouds, , Distributed Computing Resources, 55

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Kepler, , Manjrasoft, IBM Power Systems, , • Kepler is an open source scientific workflow engine built from the, collaboration of several research projects., , • The system is based on the Ptolemy II system which provides a solid, platform for developing dataflow-oriented workflows., , • Kepler provides a design environment based on the concept of actors,, which are reusable and independent blocks of computation such as Web, Services, database calls, etc., , • The connection between actors is made with ports., • An actor consumes data from the input ports and writes data/results to the, output ports., , • Novelty of Kepler is in its ability of separating the flow of data among, components from the coordination logic that is used to execute workflow., , • Thus for the same workflow, it supports different models such as, synchronous and asynchronous models. The workflow specification is, expressed using a proprietary XML language., 56

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DAGMan (Directed Acyclic Graph Manager), , Manjrasoft, IBM Power Systems, , • DAGMan is part of the Condor project and constitutes an, extension to the Condor scheduler to handle job interdependencies., , • Condor finds machines for the execution of programs, but, does not support the scheduling of jobs in a specific, sequence., • Therefore, DAGMan acts as a meta-scheduler for Condor by, submitting the jobs to the scheduler in the appropriate order., • The input of DAGMan is a simple text file that contains the, information about the jobs, pointers to their job submission, files, and the dependencies among jobs., , 57

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Cloudbus Workflow Management System(WfMS), , Manjrasoft, IBM Power Systems, , • Cloudbus Workflow Management System (WfMS) is a middleware platform built for, managing large application workflows on distributed computing platforms such as, Grids and Clouds., • It comprises of software tools that help end-users to compose, schedule, execute,, and monitor workflow applications through a web-based portal., , • The portal provides the capability of uploading workflows or defining new ones with, a graphical editor. In order to execute workflows WfMS relies on the Gridbus Broker, that is Grid/Cloud resource broker supporting the execution of applications with, Quality of Service attributes over a heterogeneous distributed computing, infrastructure including Linux based clusters, Globus, and Amazon EC2., WfMS uses a proprietary XML language for the specification of workflows., , •, • Offspring supports the integration with any distributed computing middleware that, , can manage simple bag of tasks application. It provides a native integration with, Aneka and also supports a simulated distribution engine for testing strategies during, the development. As it allows the definition of workflows in the form of plug-ins, it, does not use any XML specification., , 58

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Unit 3 - Objectives, , Manjrasoft, IBM Power Systems, , After completing this unit you should be able to, • High-Throughput Computing: Task Computing, • Characterizing a task, • Computing Categories, • Frameworks for Task Computing, • Task based Application Models, • Aneka Task-Based Programming, • Developing Applications with the Task Model, • Task Libraries, • Web Services Integration, • Developing Parameter Sweep Applications, • Managing Workflows, • Summary, 59

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Aneka Task-Based Programming, , Manjrasoft, IBM Power Systems, , • Aneka provides support for all the flavors of Task-based, programming by means of the Task Programming Model,, which constitutes the basic support given by the framework, for supporting the execution of bag of tasks applications., , • Task programming is realized through the abstraction of the, Aneka.Tasks.ITask., , • By using this abstraction as a basis support for execution of, legacy applications, parameter sweep applications, and, workflows have been integrated into the framework., • In this section, we introduce the fundamental concepts of the, model and provide examples on how to develop applications, for all the previously discussed application models., 60

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Task Programming Model, , Manjrasoft, IBM Power Systems, , • The task programming model provides a very intuitive, abstraction for quickly developing distributed applications on, top of Aneka. It provides a minimum set of APIs that are, mostly centered on the Aneka.Tasks.ITask interface., , • This interface together with the services supporting the, execution of tasks in the middleware constitutes the core, feature of the model., , • Developers create distributed applications in terms of ITask, instances whose collective execution describe a running, application., , • These tasks, together with all the required dependencies, (data files and libraries) are grouped together and managed, through the AnekaApplication class, which is specialized to, support the execution of tasks., 61

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Task Based Programming Model, , Manjrasoft, IBM Power Systems, , • Two other components constitute the client side view of a, task based application: AnekaTask and TaskManager., , • The former constitute the runtime wrapper used by Aneka to, represent a task within the middleware, while the latter is the, underlying component that interacts with Aneka, submits the, tasks, monitor their execution, and collect the results., , • In the middleware, four services coordinate their activities in, order to execute task based applications. These are:, MembershipCatalogue, TaskScheduler, ExecutionService,, and StorageService. MembershipCatalogue constitutes the, main access point of the Cloud and acts as service directory, used to locate the TaskScheduler service that is in charge of, managing the execution of task based applications., 62

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Task Based Programming Model, , Manjrasoft, IBM Power Systems, , • Its main responsibility is to allocate task instances to resources featuring the, ExecutionService for task execution and monitoring task state. If the, application requires the data transfer support in the form of data files, input, or output files, an available StorageService will be used as a staging facility, for the application., , • The features provided by the task model are completed by a Web Service, that allows any client to submit the execution of tasks to Aneka. The, procedure for submitting tasks through the web services is the same as the, one done by using the framework APIs., , • The user creates an application on Aneka and submits tasks within the, context of this application., , • The web service limits the type of tasks that can be submitted. Only a, limited collection of tasks is available for submission; despite that, these, tasks cover the functionality commonly found in other distributed computing, systems., , 64

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Unit 3 - Objectives, , Manjrasoft, IBM Power Systems, , After completing this unit you should be able to, • High-Throughput Computing: Task Computing, • Characterizing a task, • Computing Categories, • Frameworks for Task Computing, • Task based Application Models, • Aneka Task-Based Programming, • Developing Applications with the Task Model, • Task Libraries, • Web Services Integration, • Developing Parameter Sweep Applications, • Managing Workflows, • Summary, 65

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Developing Applications with the Task Model, , Manjrasoft, IBM Power Systems, , • Execution of task-based applications involves several, components. The development of such application is limited, to the following operations:, – Defining classes implementing the ITask interface., – Creating a properly configured AnekaApplication instance., – Creating ITask instances and wrapping them into AnekaTask, instances., – Execute the application and wait for its completion., , • Moreover, from a design point of view, the process of, defining a task application ultimately reduces to the definition, of the classes that implement ITask, which will be those that, contribute to form the workload generated by the application., , 66

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ITask and Aneka Task, • Almost all the client side, , •, , Manjrasoft, IBM Power Systems, , namespace Aneka.Tasks, , features for developing task {, based application with Aneka, ///<summary>, are, contained, in, the, ///Interface ITask. Defines the, Aneka.Tasks, namespace interface for implementing a task., (Aneka.Tasks.dll)., ///</summary>, public interface ITask, The, most, important, {, component for designing, ///<summary>, tasks is the ITask interface,, ///Executes the sine function., which is defined below., , • This interface exposes only, , ///</summary>, , public void Execute();, one method: Execute. The, }, method is invoked in order to, execute the task on the }, remote node., , 67

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ITask Interface, • The ITask interface provides a, programming, approach, for, developing native tasks, which, means tasks implemented in any of, the, supported, programming, languages of .NET framework., , • The restrictions on implementing, task classes are minimal, other than, implementing the ITask interface,, they need to be serializable since, task instances are created and, moved over the network., , • a simple implementation of a task, class that computes the Gaussian, distribution for a given point x is, given aside, , Manjrasoft, IBM Power Systems, , // File: GaussTask.cs, using System;, , using Aneka.Tasks;, namespace GaussSample, {, /// <summary> /// Class GaussTask. Implements the ITask, interface for computing the Gauss function. /// </summary>, [Serializable], public class GaussTask : ITask, {, /// <summary> /// Input value. /// </summary>, private double x;, /// <summary> /// Gets the input value of the Gauss function, /// </summary>, public double X { get { return this.x; } set { this.x = value; } }, /// <summary> /// Result value /// </summary>, private double y;, /// <summary> /// Gets the result value of the Gauss function., /// </summary>, public double Y { get { return this.y; } set { this.y = value; } }, /// <summary> /// Executes the Gauss function ///, </summary>, public void Execute(), {, this.y = Math.Exp(-this.x*this.x);, }, }, }, , 68

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Wrapping an ITask into an Aneka instance, , Manjrasoft, IBM Power Systems, , • ITask provides minimum restrictions // create a Gauss task and wraps it, on how to implement a task class, and, decouples, the, specific, operation of the task from the, runtime wrapper classes., It is, required for managing tasks within, Aneka. This role is performed by the, AnekaTask class that represents, the task instance in accordance with, the Aneka application model APIs., This, class, extends, the, Aneka.Entity.WorkUnit class and, provides the feature for embedding, ITask, instances.AnekaTask, is, mostly used internally and for end, users it provides facilities for, specifying input and output files for, the task., , into an AnekaTaskinstance, GaussTaskgauss = new, GaussTask();, AnekaTask task =, newAnekaTask(gauss);, // add one input and one output, files, task.AddFile("input.txt",, FileDataType.Input,, FileAttributes.Local);, task.AddFile("result.txt",, FileDataType.Output,, FileAttributes.Local);, , 69

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Controlling Task Execution, , Manjrasoft, IBM Power Systems, , • Task classes and AnekaTask define the computation logic of, a task based application, while the AnekaApplication class, provides the basic feature for implementing the coordination, logic of the application., , • AnekaApplication is a generic class that can be specialized, for supporting different programming models, in the case of, the Task Programming it assumes the form of, AnekaApplication<AnekaTask, TaskManager>. For other, programming models the class provides the following, operations:, – Static and dynamic task submission., – Application state and task state monitoring., – Event-based notification of tasks completion or failure., 70

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Controlling Task Execution, • By composing these features all together, it is possible to define the logic that is, required to implement a specific task, application. Static submission is a very, common pattern in the case of task-based, applications and it involves the creation of, all the tasks that need to be executed in, one loop and their submission as a single, bag., • More complex task submission strategies, are then required for implementing, workflow based applications, where the, execution of tasks is determined by, dependencies among them., • In this case a dynamic submission of, tasks is a more efficient technique and, involves the submission of tasks as a, result of the event-based notification, mechanism, implemented, in, the, AnekaApplication class., , Manjrasoft, IBM Power Systems, , // get an instance of the Configuration class from, file, Configuration conf =, Configuration.GetConfiguration("conf.xml");, , // specify that the submission of task is static (all, at once), conf.SingleSubmission = true;, AnekaApplication<AnekaTask, TaskManager> app, =, new AnekaApplication<Task,TaskManager>(conf);, for(int i=0; i<400; i++), {, , GaussTaskgauss = new GaussTask();, gauss.X = i;, AnekaTask task = new AnekaTask(gauss);, // add the task to the bag of work units to submit, app.AddWorkunit(task);, , }, // submit the entire bag, , app.SubmitExecution();, , 71

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Gaussian Application Demonstration, , Manjrasoft, IBM Power Systems, , • From Mastering in Cloud computing Text book., , 73

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File Management, , Manjrasoft, IBM Power Systems, , • Task based application normally deal with files in order to perform their, operations., , • As already discusses files may constitute input data for tasks, may contain, the result of a computation, or may represent executable code or library, dependencies., , • Therefore, providing support for file transfer for task based application is, essential., , • Aneka provides built-in capabilities for file management in a distributed, infrastructure and the Task Programming Model transparently leverages, these capabilities., , • Any model based on the WorkUnit and the ApplicationBase classes has, built-in support for file management., , • It is possible to provide input files that are common to all the WorkUnit, instances, through the ApplicationBase.SharedFiles collection, and instance, specific input and output files by leveraging the WorkUnit.InputFiles and, WorkUnit.OutputFiles collections., 74

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File Management, , Manjrasoft, IBM Power Systems, , • A fundamental component for the management of files, is the FileData class, which constitutes the logic, representation of physical file, defined in the, Aneka.Data.Entity namespace (Aneka.Data.dll). A, FileData instance provides information about a file:, – Its nature: whether it is a shared file, an input file, or an, output file., , – Its path both in the local and in the remote file system, including a different name., – A collection of attributes that provides other information (such, as the final destination of the file or whether the file is, transient or not, etc.)., 75

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File Management, , Manjrasoft, IBM Power Systems, , • By using the FileData class the user specifies the file, dependencies of tasks and the application and the Aneka, APIs will automatically transfer them to and from the Cloud, when needed. Aneka allows specifying both local and remote, files stored on FTP servers or Amazon S3., , • A FileData instance is identified by three elements: an, owner, a name, and a type., , • The owner identifies, by means of the corresponding id,, which is the computing element that needs the file:, application instance or work unit., , • The type specifies whether file is shared, input or output file., The name represents the name of the corresponding, physical file., 76

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File Dependencies Management, , Manjrasoft, IBM Power Systems, , • Demonstration from Mastering in Cloud computing Text book, , 77

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General Interaction flow for file Management, , Manjrasoft, IBM Power Systems, , • Once the application is submitted the shared files are staged into the Aneka Cloud., • If the file is local it will be searched into the directory pointed by Configuration.Workspace, if the, •, •, •, •, , •, , file is remote the specific configuration settings mapped by the FileData.StorageBucketId, property will be used to access the remote server and stage in the file., If there is any failure in staging input files, the application is terminated with an error., For each of the tasks belonging to the application the corresponding input files are staged into, the Aneka Cloud as done for shared files., Once the task is dispatched for execution to a remote node, the runtime will transfer all the, shared files of the application and the input files of the task into the working directory of the, task and eventually get renamed if the FileData.VirtualPath property is not null., After the execution of the task the runtime will look for all the files that have been added into, the WorkUnit.OutputFiles collection. If not null, the value of the FileData.VirtualPath property, will be used to locate the files, otherwise the FileData.FileName property will be the reference., All the files that do not contain the FileAttributes.Optional attribute need to be present;, otherwise the execution of the task is classified as a failure., Despite the successful execution or the failure of a task, the runtime tries to collect and move, to their respective destinations all the files that are found. Files that contain the, FileAttributes.Local attributed are moved to the local machine from where the application is, saved and stored in the directory pointed by Configuration.Workspace. Files that have a, StorageBucketId property set will be staged out to the corresponding remote server., 78

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Unit 3 - Objectives, , Manjrasoft, IBM Power Systems, , After completing this unit you should be able to, • High-Throughput Computing: Task Computing, • Characterizing a task, • Computing Categories, • Frameworks for Task Computing, • Task based Application Models, • Aneka Task-Based Programming, • Developing Applications with the Task Model, • Task Libraries, • Web Services Integration, • Developing Parameter Sweep Applications, • Managing Workflows, • Summary, 80

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Task Libraries, , Manjrasoft, IBM Power Systems, , • Aneka provides a set of ready to use tasks for performing the most basic, operations for remote file management. These tasks are part of the, Aneka.Tasks.BaseTasks namespace, which is part of the Aneka.Tasks.dll, library. The following operations are implemented:, – File copy. The LocalCopyTask performs the copy of a file on the remote node; it takes as input a file and produces a copy, of it under a different name or path., , – Legacy application execution. The ExecuteTask allows executing external and legacy applications by using the, , –, , System.Diagnostics.Process class. It requires the location of the executable file to run and it is also possible to specify, command line parameters. ExecuteTask also collect the standard error and standard output produced by the execution of, the application., Substitute operation. The SubstituteTask performs a search and replace operation within a given file by saving the, resulting file under a different name. It is possible to specify a collection of string-based name-value pairs representing the, strings to search together with their corresponding replacements., , – File deletion. The DeleteTask deletes a file that is accessible through the file system on the remote node., – Timed delay. The WaitTask introduces a timed delay. This task can be used in several scenarios, for example it can be, –, , used for profiling or simply simulation of the execution. Also, it can also be used to introduce a pause between the, executions of two applications if needed., Task composition. The CompositeTask implements the composite pattern and allows expressing a task as a composition, of multiple tasks that are executed in sequence. This task is very useful to perform complex tasks involving the combination, of operations implemented in other tasks., , – In software engineering, the composite pattern is a software design pattern, which allows expressing a combination of, components as a single component. The advantage of using such pattern resides in creating a software infrastructure that, allows forwarding the execution of an operation to a group of objects by treating it as single unit and in completely, transparent manner., 81

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Unit 3 - Objectives, , Manjrasoft, IBM Power Systems, , After completing this unit you should be able to, • High-Throughput Computing: Task Computing, • Characterizing a task, • Computing Categories, • Frameworks for Task Computing, • Task based Application Models, • Aneka Task-Based Programming, • Developing Applications with the Task Model, • Task Libraries, • Web Services Integration, • Developing Parameter Sweep Applications, • Managing Workflows, • Summary, 82

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Web Services Integration, • Aneka provides integration with other technologies and, Manjrasoft, IBM Power Systems, , applications by means of Web Services, which allow some of, the services hosted in the Cloud to be accessible in platform, independent fashion., • Among these, the task submission web service allows third, party applications to submit tasks as it happens in traditional, Computing Grids., • The task submission web service is an additional component, that can be deployed in any ASP.NET web server and that, exposes a simple interface for job submission, which is, compliant with the Aneka application model., • The task web service provides an interface that is more, compliant with the traditional way fostered by Grid Computing., Therefore, the new concept of job, which is a collection of, predefined tasks, is introduced.., 83

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Web Services Integration, , Manjrasoft, IBM Power Systems, , • Jobs can be composed by putting together the tasks defined in the basic, task library. Operations supported through the web service interface are the, following:, , – Local file copy on the remote node., – File deletion., – Legacy application execution through the common shell services., – Parameter substitution., , • It is also possible specify input and output files for each job. The only, restriction in this case is that both input and output files need to reside in, remote FTP servers., , • This enables Aneka to automatically stage the files from these servers, without the user intervention., , • Traditional grid technologies such as the Gridbus Broker and the Workflow, Engine can make use of task web service to submit their tasks for execution, on Cloud nodes managed by Aneka., , 85

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Unit 3 - Objectives, , Manjrasoft, IBM Power Systems, , After completing this unit you should be able to, • High-Throughput Computing: Task Computing, • Characterizing a task, • Computing Categories, • Frameworks for Task Computing, • Task based Application Models, • Aneka Task-Based Programming, • Developing Applications with the Task Model, • Task Libraries, • Web Services Integration, • Developing Parameter Sweep Applications, • Managing Workflows, • Summary, 86

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Developing Parameter Sweep Application, , Manjrasoft, IBM Power Systems, , • Aneka, , integrates support for parameter sweeping, applications on top of the Task Model by means of a, collection of client components allowing developers to, quickly prototype applications either through programming, APIs or graphical user interfaces., , • The set of abstractions and tools supporting the, development of parameter sweep applications constitutes, the Parameter Sweep Model (PSM)., , 87

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Job Object Model, , Manjrasoft, IBM Power Systems, , • The model is organized into several namespaces under the, common root Aneka.PSM. More precisely:, – Aneka.PSM.Core (Aneka.PSM.Core.dll) contains the base classes for, defining a template task and the client components managing the, generation of tasks given the set of parameters., – Aneka.PSM.Workench, (Aneka.PSM.Workbench.exe), and, Aneka.PSM.Wizard (Aneka.PSM.Wizard.dll) contain the user interface, support for designing and monitoring parameter sweep applications., Mostly they contain the classes and components required by the, Design Explorer, which is the main GUI for developing parameter, sweep applications., – Aneka.PSM.Console (Aneka.PSM.Console.exe) contains the, components and classes supporting the execution of parameter sweep, applications in console mode., , • These namespace define the support for developing and, controlling parameter sweep application on top of Aneka., , 88

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Object Model, , Manjrasoft, IBM Power Systems, , • The fundamental elements of the Parameter Sweep Model, are defined in the Aneka.PSM.Core namespace. This model, introduces, the, concept, of, job, (Aneka.PSM.Core.PSMJobInfo),, which, identifies, a, parameter sweep application., , • A job comprises file dependencies, parameter definitions, together with their admissible domain, and the definition of, the template task., , • PSM object Model is shown in the next slide…, , 89

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Job Object Model, , Manjrasoft, IBM Power Systems, , 90

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Object Model, , Manjrasoft, IBM Power Systems, , • The root component for application design is the PSMJobInfo class, which contains information, about shared files, input and output files (PSMFileInfo). In accordance with the Aneka, application model, shared files are common to all the instances of the template task, while input, and output files are specific to a given task instance. Therefore, these ones can be expressed, as a function of the parameters. Currently, it is possible to specify five different types of, parameters:, – Constant parameter (PSMSingleParameterInfo): this parameter identifies a specific value, that is set at design time and will not change during the execution of the application., – Range parameter (PSMRangeParameterInfo): this parameter allows defining a range of, allowed values, which might be integer or real. The parameter identifies a domain composed, by discrete values and requires the specification of a lower bound, an upper bound, and a, step for the generation of all the admissible values., – Random parameter (PSMRandomParameterInfo): this parameter allows the generation of a, random value in between a given range defined by a lower and an upper bound. The value, generated is real., – Enumeration parameter (PSMEnumParameterInfo): this parameter allows specifying a, discrete set of values of any type. It is useful to specify discrete sets that are not based on, numeric values., – System parameter (PSMSystemParameterInfo): this parameter allows mapping a specific, value that will be substituted at runtime while the task instance is executed on the remote, node., 91

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Object Model, , Manjrasoft, IBM Power Systems, , • Other than these parameters, the object model reserves special parameters, that are used to identify specific values of the PSM object model, such as, the task identifier and other data., , • Parameters have access to the execution environment by means of an, execution context (PSMContext) that is responsible for providing default and, runtime values. The task template is defined as a collection of commands, (PSMCommandInfo), which replicate and extend the features available in, the base task library. The available commands for composing the task, template perform the following operations:, , – Local file copy on the remote node (PSMCopyCommandInfo)., – Remote file deletion (PSMDeleteCommandInfo)., – Execution of programs through the shell (PSMExecuteCommandInfo)., – Environment, variable, setting, on, the, remote, node, (PSMEnvironmentCommandInfo)., , – String pattern replacement within files (PSMSubstituteCommandInfo)., 92

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Parameter Sweep Model, , Manjrasoft, IBM Power Systems, , • A parameter sweep application is executed by means of a job, , •, , manager (IJobManager), which interfaces the developer with, the underlying APIs of the Task Model. Figure 7.12 shows the, relation between the PSM APIs, with a specific reference to the, job manager, and the Task Model APIs., Through the IJobManager interface it is possible to specify user, credentials and configuration for interacting with the Aneka, middleware. The implementation of IJobManager will then, create a corresponding Aneka application instance and, leverage the Task Model API to submit all the task instances, generated from the template task., , • The interface also exposes facilities for controlling and, monitoring the execution of the parameter sweep application,, as well as support for registering the statistics about the, application., 94

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Development and Monitoring Tools, , Manjrasoft, IBM Power Systems, , • The core libraries allow developer to directly program parameter sweep applications, and embed them into other applications. Additional tools simplify design and, development of parameter sweep applications by providing support for visual design, of the applications and interactive and non-interactive application execution. These, tools are the Aneka Design Explorer and the Aneka PSM Console., • The Aneka Design Explorer is an integrated visual environment for quickly, prototyping parameter sweep applications, executing them, and monitoring their, status. It provides a simple wizard helping user to visually define any aspect of, parameter sweep applications such as file dependencies and result files,, parameters, and template tasks. The environment also provides a collection of, components that help users to monitor application execution, aggregate statistics, about application execution, detailed task transition monitoring, and extensive, access to application logs., , • The Aneka PSM Console is a command line utility designed to run parameter sweep, applications in non-interactive mode. The console offers a simplified interface for, running applications with the essential features for monitoring their execution. With, respect to the Design Explorer, the console offers less support for keeping and, visualizing aggregate statistics, but exposes the same data in a more simplified, textual form., 95

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Unit 3 - Objectives, , Manjrasoft, IBM Power Systems, , After completing this unit you should be able to, • High-Throughput Computing: Task Computing, • Characterizing a task, • Computing Categories, • Frameworks for Task Computing, • Task based Application Models, • Aneka Task-Based Programming, • Developing Applications with the Task Model, • Task Libraries, • Web Services Integration, • Developing Parameter Sweep Applications, • Managing Workflows, • Summary, 96

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Managing Workflows, , Manjrasoft, IBM Power Systems, , • Support for workflow in Aneka is not native, but obtained with plug•, •, , •, , •, , ins that allows client-based workflow managers to submit tasks to, Aneka., Currently, two different workflow managers can leverage Aneka for, task execution: the Workflow Engine and Offspring., The former leverages the task submission web service exposed by, Aneka while the latter directly interacts with the Aneka programming, APIs., The Workflow Engine plug-in for Aneka constitutes an example of the, integration capabilities offered by the framework, which allows client, applications developed with any technology and language to, leverage Aneka for task execution., The integration developed for Offspring constitutes another example, on how it is possible to construct another programming model on top, of the existing APIs available in the framework., 97

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Offspring Architecture, , Manjrasoft, IBM Power Systems, , • The system is composed by two types of components: plug-in and, distribution engine., , • The former is used to enrich the environment of features while the latter, represent the access to the distributed computing infrastructure leveraged, for task execution., , • Among the available plug-ins, the AutoPlugin provides facilities for the, definition of workflows in terms of strategies., , • A strategy generates the tasks that are submitted for execution and also, defines the logic, in terms of sequencing, coordination, and dependencies,, used to submit the task through the engine., , • A specific component, the StrategyController, decouples the strategies from, the distribution engine; therefore, strategies can be defined independently, from the specific middleware used., , • The connection with Aneka is realized through the AnekaEngine, which, implements the operations of IDistributionEngine for the Aneka middleware, and relies on the services exposed by the Task Model programming APIs., 99

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Offspring Architecture contd…, , Manjrasoft, IBM Power Systems, , • The systems allows for the execution of dynamic workflows,, whose structure is defined as the workflow executes., , • Two different types of tasks can be defined: native tasks and, legacy tasks. Native tasks are completely implemented in, managed code., , • While legacy tasks manage file dependencies and wrap all, the data necessary for the execution of legacy programs on, a remote node., • Also, a strategy may define shared file dependencies that, are necessary to all the tasks generated by the workflow., The dependencies among tasks are implicitly defined by the, execution of the strategy by the StrategyController and the, events fired by the distributed engine., 100

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Workflow coordination, , Manjrasoft, IBM Power Systems, , • Two main execution threads control the execution of a strategy., • A control thread manages the execution of the strategy whereas a monitoring thread collects, •, , •, •, •, •, , the feedback from the distribution engine and allows for the dynamic reaction of the strategy to, the execution of tasks previously submitted., From the workflow developer’s point of view, the logic is quite simple. The execution of a, strategy is composed by three macro steps: setup, execution, and finalization., The first step involves the setup of the strategy and the application mapping it., Correspondingly, the finalization step is in charge of releasing all the internal resources, allocated by the strategy and shutting down the application., The core of the workflow execution resides in the execution step, which is broken down into a, set of iterations. During each of the iterations a collection of tasks is submitted, these tasks do, not have dependencies from each other and can be executed in parallel., As soon as a task complete or fails the strategy is queried in order to see whether a new set of, tasks need to be executed. In this way dependencies among tasks are implemented., If there are more tasks to be executed, they are submitted and the controller waits for a, feedback from the engine, otherwise an iteration of the strategy is completed. At the end of, each iteration, the controller checks whether the strategy has completed the execution and in, this case, the finalization step is performed., , 101

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Workflow Coordination, , Manjrasoft, IBM Power Systems, , StrategyController, Control Thread, (Strategy, Management), , Monitoring Thread, (Feedback from Distribution Engine), , Init Application, , Task, Feedback, , Init Strategy, Task, Failed?, , Submit Tasks, , No, Invoke, OnSuccess, , Wait, No, Yes, , Yes, Invoke OnFailed, New Task?, , Complet, e or, Stop?, , Yes, No, , Release Strategy, , Signal, Shutdown, Application, , Yes, , Submit, , Iteration, Complete?, , No, , 102

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Summary, , Manjrasoft, IBM Power Systems, , • we have introduced the task based programming and provided an overview of the, technologies supporting the development of distributed applications based on the, concept of tasks. Task based programming constitutes the most intuitive approach, for distributing the computation of an application over a set of nodes., • Traditionally, task based programming model has been successfully used in the, development of distributed applications in many areas., , • We identified three major computing categories where task model can be utilized., •, , High Performance Computing (HPC) refers to the use of distributed computing, facilities for solving problems needing large computing power., Common HPC applications feature a large collection of compute intensive tasks, whose duration is relatively short. High Throughput Computing (HTC) identifies, scenarios where distributed computing facilities are used to support the execution of, applications needing large computing power for a long period of time. Tasks may not, be numerous, but have a long duration and infrastructure reliability becomes, fundamental. Many Task Computing (MTC) is the latest emergent trend and, identifies a heterogeneous set of applications and requirements for applications,, which fill the gap between HPC and HTC., , 103

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Summary, , Manjrasoft, IBM Power Systems, , • We have briefly reviewed common models related to task programming., • Embarrassingly parallel applications are composed of a collection of tasks which do, not relate to each other, can be executed in any order, and do not require coallocation., , • Parameter sweep applications are a special instance of embarrassingly parallel, model., • We have finally introduced the Task Model and the services implemented in Aneka, supporting task based programming, as a practical example of a framework enabling, the development and the execution of distributed applications based on tasks., , • Parameter sweep applications are realized through the Parameter Sweep Model, (PSM), which is characterized by a collection of client side components providing, different, and more suitable, interface for this kind of applications., • Workflow applications are not natively supported by Aneka, but integration with other, technologies allows leveraging Aneka for workflow execution. For example, a plug-in, using the Aneka task submission web service allows the Workflow Engine to use, Aneka as a backend for workflow execution., , • The Aneka distribution engine implemented in Offspring provides another example, on how it is possible to quickly prototype another programming model (in this case a, workflow based model) by leveraging the base APIs of Task Model., 104