Task Matching and Scheduling in Heterogeneous Computing Environments Using a Genetic-Algorithm-Based Approach

In a distributed heterogeneous computing system, the resources have different capabilities and tasks have different requirements. To maximize the performance of the system, it is essential to assign the resources to tasks (match) and order the execution of tasks on each resource (schedule) to exploit the heterogeneity of the resources and tasks. Dynamic mapping (defined as matching and scheduling) is performed when the arrival of tasks is not known a priori. In the heterogeneous environment considered in this study, tasks arrive randomly, tasks are independent (i.e., no inter-task communication), and tasks have priorities and multiple soft deadlines. The value of a task is calculated based on the priority of the task and the completion time of the task with respect to its deadlines. The goal of a dynamic mapping heuristic in this research is to maximize the value accrued of completed tasks in a given interval of time. This research proposes, evaluates, and compares eight dynamic mapping heuristics. Two static mapping schemes (all arrival information of tasks are known) are designed also for comparison. The performance of the best heuristics is 84% of a calculated upper bound for the scenarios considered.

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“…The GA method shown here is based on [28] and [32]. The general GA starts by generating an initial population and evaluating the population.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Dynamically mapping tasks with priorities and multiple deadlines in a heterogeneous environment

Kim

Shivle

Siegel

et al. 2007

Journal of Parallel and Distributed Computing

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“…A machine can simultaneously handle one outgoing data transmission and one incoming data reception. Similar to the study in [27], we assume that:…”

Section: Simulation Setupmentioning

confidence: 99%

Mapping subtasks with multiple versions on an ad hoc grid

et al. 2005

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An ad hoc grid is a heterogeneous computing system composed of mobile devices. Each computing resource is constrained in battery energy. The problem being studied is to assign statically computing resources to the subtasks of an application that has an execution time constraint, when the resources are oversubscribed. All subtasks must be executed; to accommodate this in an oversubscribed environment, each subtask has two versions: the primary or full version, and the secondary or degraded version. The secondary version utilizes only 10% of the resources that the primary version requires, and produces only 10% of the data output for the subsequent children subtasks. Thus, the degraded version (secondary version) represents a reduced capability of lesser overall value, while consuming fewer resources. The goal is to assign resources so that the application meets an execution time constraint and the battery energy constraint while minimizing the number of degraded versions used. Five resource allocation heuristics to derive near-optimal solutions to this problem are presented, evaluated, and compared.

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“…Because of its key importance on performance, the task scheduling problem in general has been extensively studied and various heuristics have been proposed in the literature [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] . These heuristics are classified into a variety of categories such as list scheduling algorithms, clustering algorithms, guided random search methods and task duplication based algorithms.…”

Section: Introdutionmentioning

confidence: 99%

“…Genetic algorithms [10][11][12][13] are of the most widely studied guided random search techniques for the task scheduling problem. Among these algorithms, the task matching and scheduling algorithm using a genetic approach [10] , Problem-Space Genetic Algorithm (PSGA) [11] and Push-Pull [12] are proposed for heterogeneous processors and an incremental genetic algorithm (GA) [13] for the homogeneous processors.…”

Section: Introdutionmentioning

confidence: 99%

“…Among these algorithms, the task matching and scheduling algorithm using a genetic approach [10] , Problem-Space Genetic Algorithm (PSGA) [11] and Push-Pull [12] are proposed for heterogeneous processors and an incremental genetic algorithm (GA) [13] for the homogeneous processors. Although genetic algorithms provide good quality schedules, their execution times are significantly higher than other alternatives.…”

Section: Introdutionmentioning

confidence: 99%

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Low Complexity Performance Effective Task Scheduling Algorithm for Heterogeneous Computing Environments

Ilavarasan¹,

Thambidurai²

2007

J. of Computer Science

153

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Abstract:A heterogeneous computing environment is a suite of heterogeneous processors interconnected by high-speed networks, thereby promising high speed processing of computationally intensive applications with diverse computing needs. Scheduling of an application modeled by Directed Acyclic Graph (DAG) is a key issue when aiming at high performance in this kind of environment. The problem is generally addressed in terms of task scheduling, where tasks are the schedulable units of a program. The task scheduling problems have been shown to be NP-complete in general as well as several restricted cases. In this study we present a simple scheduling algorithm based on list scheduling, namely, low complexity Performance Effective Task Scheduling (PETS) algorithm for heterogeneous computing systems with complexity O (e) (p+ log v), which provides effective results for applications represented by DAGs. The analysis and experiments based on both randomly generated graphs and graphs of some real applications show that the PETS algorithm substantially outperforms the existing scheduling algorithms such as Heterogeneous Earliest Finish Time (HEFT), Critical-Path-On a Processor (CPOP) and Levelized Min Time (LMT), in terms of schedule length ratio, speedup, efficiency, running time and frequency of best results.Key words: DAG, task graph, task scheduling, heterogeneous computing system, schedule length, speedup, efficiency INTRODUTIONA growing emphasis on concurrent processing of jobs has lead to an increased acceptance of heterogeneous computing environments and the availability of a network of processors makes a costeffective utilization of underlying parallelism for applications like weather modeling, image processing, real-time and distributed database systems. A wellknown strategy behind efficient execution of a huge application on a heterogeneous computing environment is to partition it into multiple independent tasks and schedule such tasks over a set of available processors. A task-partitioning algorithm takes care of efficiently dividing an application into tasks of appropriate grain size and an abstract model of such a partitioned application can be represented by a Directed Acyclic Graph (DAG). Each task of a DAG corresponds to a sequence of operations and a directed edge represents the precedence constraints between the tasks. Each task can be executed on a processor and the directed edge shows transfer of relevant data from one processor to another. Task scheduling can be performed at compile-time or at run-time. When the characteristics of an application, which includes execution times of tasks on different processors, the data size of the communication between tasks and the task dependencies, are known a priori, it is represented with a static model. The objective of task scheduling is to map the tasks on the processors and order their execution so that task precedence requirements are satisfied and a minimum overall completion time is obtained. The problem of scheduling of tasks with required precedence rela...

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Task Matching and Scheduling in Heterogeneous Computing Environments Using a Genetic-Algorithm-Based Approach

Cited by 329 publications

References 23 publications

Dynamically mapping tasks with priorities and multiple deadlines in a heterogeneous environment

Dynamically mapping tasks with priorities and multiple deadlines in a heterogeneous environment

Mapping subtasks with multiple versions on an ad hoc grid

Low Complexity Performance Effective Task Scheduling Algorithm for Heterogeneous Computing Environments

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