A GPU Based Approach for Solving the Workflow Scheduling Problem

A GPU Based Approach for Solving the Workflow Scheduling Problem

Mohammed Benhammouda (EEDIS Laboratory, Djillali Liabes University at Sidi Bel Abbes, Sidi Bel Abbès, Algeria) and Mimoun Malki (LabRI-SBA Laboratory, Ecole Supérieure en Informatique de Sidi Bel Abbes, Sidi Bel Abbès, Algeria)
Copyright: © 2019 |Pages: 12
DOI: 10.4018/IJIRR.2019100101
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Cloud computing is considered a new way to use on-demand computing resources. When executing a workflow process in such an environment, task scheduling, a well-known NP-hard problem is a very important step. Many heuristic algorithms have been proposed to solve this problem. In this article, the authors present a GPU-based approach for solving the workflow scheduling problem. The main idea of the approach is to implement a massively parallel version of the simulated annealing algorithm, in an asynchronous way where no information is exchanged among parallel runs. The proposed approach, called PSA algorithm, is against another well-established scheduling HEFT heuristic. Experiments with randomly generated graphs show a much better performance from the proposed approach.
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2. Scheduling Problem Modeling

Task scheduling problem is one of the most important steps when using cloud computing environment capabilities to execute a workflow. A workflow can be represented by a Directed Acyclic Graph (DAG), G = (V, E), as shown in Figure 1, where V is the set v nodes, vi in V represents a workflow task, which contains a known number of instructions that must be executed on the same processor. E represents the set of communication edges between tasks; each edge e(i, j) ∈ E represents the task-dependency constraint which means that task ni should be executed before task nj can be started. A computation cost matrix W matrix complements the DAG, this matrix is a v × p, where v is the number of tasks and p is the number of processors. wij gives the estimated time to execute task vi on machine pj. Each edge e(i, j) ∈ E is associated with a nonnegative weight cij representing the communication cost between the tasks vi and vj (Topcuoglu et al., 2002).

In a given task graph, a task without any parent is called an entry task and a task without any child is called an exit task. A task graph may have more than one entry task and more than one exit task.

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