Dynamic Tasks Scheduling Model for Performance Evaluation of a Distributed Computing System through Artificial Neural Network

Singh, M. P.; Yadav, Pradeep K.; Kumar, Harendra; Agarwal, Babita

doi:10.1007/978-81-322-0487-9_31

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…In static allocation once a task is assigned to a processor, it remains there statically during the execution of a distributed program. Many approaches have been reported for solving the Static tasks assignment problem in a DRTS [1][2][3][4][5][6][7]. While in dynamic allocation a module can be reassigned during program execution [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A Heuristic Model for Tasks Scheduling in Heterogeneous Distributed Real Time System under Fuzzy Environment

Kumar¹

2015

IJCA

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The development of distributed real time system (DRTS) has lead to there use in several applications including information processing, fluid flow, weather modeling, database systems, real-time high-speed simulation of dynamical systems, and image processing. Reliability analysis of these processing elements and communication links is one of the important parameter to get the system efficiency. We can improve the system performance (i.e. system cost, system reliability and processor utilization etc.) by scheduling the tasks to the processors properly in DRTS. In this paper, a new tasks allocation model has been developed with fuzzy execution times , and fuzzy inter tasks communication times , .The times has been defuzzified into crisp one by using Robust Ranking Method [RRM], Centre of Maxima Method [CoM] and Weight of Center of Area Method [CoA].The effect of inter processor distances on the tasks allocation has been considered while developing the model. Numerical examples show that the model presented in this paper is suitable for arbitrary number of processors with random program structure and more realistic and general in nature.

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Section: Introductionmentioning

confidence: 99%

A Heuristic Model for Tasks Scheduling in Heterogeneous Distributed Real Time System under Fuzzy Environment

Kumar¹

2015

IJCA

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“…A dynamic task allocation method is used here to solve task allocation problem and it allocates the tasks to optimize overall task allocation cost. Some of the other related task allocation methods have been reported in literature such asTask Assignment [1, 10,19], task scheduling [2, 3], Distributed Processing Environment [4], Task Allocation [5,7,9,11,13], data communication in Mobile Computing [6], Dynamic Task Allocation [8], Dynamic Tasks Scheduling [12,16,20,22], Dynamic Optimization Algorithm [14], dynamic programming [15], Dynamic Reassignment [17], Dynamic Load Balancing [18] and Dynamic Task Assignment [21].This research solve here a task allocation problem dynamically in DCS by allocating available processors in such a way that overall load should be balanced so system overloading can be avoided by using the proper utilization of processors.…”

Section: Introductionmentioning

confidence: 99%

Cost Optimization of Distributed Computing System with Dynamic Re-Allocation

Khan¹,

Govil²,

Dwivedi³

2015

IJCA

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A Distributed Computing System (DCS) is a combination of application and system programs that exchanges data across a number of independent terminals connected by a communication network. Cost optimization in DCS can be achieve by optimize the performance of DCS. In task allocation two types of approaches are available and these are dynamic and static. Dynamic approach of task allocation is much better as compare to static, since it makes the best use of available computational resources in DCS. Task allocation problem can be describe as 'm' number tasks are required to execute on 'n' number of processors where number tasks (m) is always greater than number of processors (n) (m>n). This research offers a cost optimization algorithm with dynamic reallocation of tasks to allocate the 'm' number of tasks on 'n' number of processors in DCS and their execution completes in k number of phases. Proposed algorithm is tested in MATLAB environment and it is noticed that obtained results are better as compared to past algorithms. Cost optimization dynamic model present in this research is helpful in performance optimization of DCS and also reduce the cost of task allocation in DCS.

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Performance enhancement of distributed network system by phase-wise dynamic task allocation

Khan

Govil

Agarwal

2014

2014 International Conference on Parallel, Distributed and Grid Computing

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Dynamic Tasks Scheduling Model for Performance Evaluation of a Distributed Computing System through Artificial Neural Network

Cited by 4 publications

References 8 publications

A Heuristic Model for Tasks Scheduling in Heterogeneous Distributed Real Time System under Fuzzy Environment

A Heuristic Model for Tasks Scheduling in Heterogeneous Distributed Real Time System under Fuzzy Environment

Cost Optimization of Distributed Computing System with Dynamic Re-Allocation

Performance enhancement of distributed network system by phase-wise dynamic task allocation

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