A simulated evolution approach to task matching and scheduling in heterogeneous computing environments

Barada, Hassan; Sait, Sadiq M.; Baig, N.

doi:10.1016/s0952-1976(02)00088-x

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…SimE has been applied to a variety of optimization problems such as very large scale integration (VLSI) [2], [3], [4], [5], field programmable gate array (FPGA) design [6], signal processing [7], the driver scheduling problem [8], the set covering problem [9], operand data type problem [10], task matching and scheduling [11], routing in computer networks [12], and LAN topology design [13], [14]. However, no recent attempt has been made to investigate the effect of the evaluation step on the quality of the solution, and what should be the structure or characteristics of the evaluation function.…”

Section: Discussionmentioning

confidence: 99%

Assessment of the “Evaluation” function in the simulated evolution algorithm

Khan

Engelbrecht

2011

2011 Seventh International Conference on Natural Computation

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Simulated Evolution is a stochastic evolutionary search strategy. The algorithm repeatedly executes evaluation, selection, and allocation steps in a sequence, until certain stopping conditions are met. The evaluation step assesses the quality of each individual element of a solution with respect to a single or multiple attributes. The outcome of this step, combined with the results of the selection step, play an important role during the allocation step, where the existing solution is perturbed to generate a new solution. This paper attempts to study the effect of the evaluation step on the quality of the solutions produced by the allocation step. Results suggest that, in general, the Unified And-Or (UAO) operator based evaluation scheme performs significantly well compared to other evaluation approaches.

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Section: Discussionmentioning

confidence: 99%

Assessment of the “Evaluation” function in the simulated evolution algorithm

Khan

Engelbrecht

2011

2011 Seventh International Conference on Natural Computation

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“…Most of them applied the algorithm on a set of independent tasks. On the other hand evolutionary algorithms has showed a great success in scheduling task graphs as in [14,16], however they never implemented any fault tolerant technique or reliability analysis for the schedule.…”

Section: Fault Tolerant Heterogeneous Mpsoc Scheduling Using Simumentioning

confidence: 99%

Fault tolerant heterogeneous MPSOC schedule length minimization based on platform reliability

Youness

Omar

Moness

2013

2013 Second International Japan-Egypt Conference on Electronics, Communications and Computers (JEC-ECC)

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Fault tolerant scheduling has been a subject of great concern recently, considering heterogeneous multiprocessor systems that may contain low reliability processors in their systems. Task replication is an established technique to achieve fault tolerance; however it has a negative influence on schedule length. Moreover increasing system reliability always has a negative impact on schedule length. In this paper we devised a new method for optimizing schedule length and maximizing system reliability simultaneously using simulated annealing. Schedule length is investigated in case of fault free operation and in the presence of a processor fault and then the schedule length is averaged based on the probability of the schedule success and failure, then this average schedule length is optimized to the minimum to give the lowest possible makespan in both cases. Results show that our algorithm is able to maximize the system reliability without degrading schedule length, in fact increasing system reliability decrease the averaged schedule length and hence the system overall performance in all cases.

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An enhanced genetic algorithm with new operators for task scheduling in heterogeneous computing systems

Akbari

Rashidi

Alizadeh

2017

Engineering Applications of Artificial Intelligence

102

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A simulated evolution approach to task matching and scheduling in heterogeneous computing environments

Cited by 3 publications

References 14 publications

Assessment of the “Evaluation” function in the simulated evolution algorithm

Assessment of the “Evaluation” function in the simulated evolution algorithm

Fault tolerant heterogeneous MPSOC schedule length minimization based on platform reliability

An enhanced genetic algorithm with new operators for task scheduling in heterogeneous computing systems

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A simulated evolution approach to task matching and scheduling in heterogeneous computing environments

Cited by 3 publications

References 14 publications

Assessment of the &#x201C;Evaluation&#x201D; function in the simulated evolution algorithm

Assessment of the &#x201C;Evaluation&#x201D; function in the simulated evolution algorithm

Fault tolerant heterogeneous MPSOC schedule length minimization based on platform reliability

An enhanced genetic algorithm with new operators for task scheduling in heterogeneous computing systems

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Product

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Assessment of the “Evaluation” function in the simulated evolution algorithm

Assessment of the “Evaluation” function in the simulated evolution algorithm