Dynamic Tasks Scheduling Algorithm for Distributed Computing Systems under Fuzzy Environment

Kumar, Harendra; Chauhan, Nutan Kumari; Yadav, Pradeep K.

doi:10.4018/ijfsa.2016100104

Cited by 12 publications

(3 citation statements)

References 12 publications

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“…Dynamic tasks scheduling algorithm for distributed computing systems under fuzzy environment was presented by (Kumar et al, 2016). (Sajja, 2021) presented examples and applications on fuzzy logic based systems.…”

Section: Introductionmentioning

confidence: 99%

Computationally Simple and Efficient Method for Solving Real-Life Mixed Intuitionistic Fuzzy 3D Assignment Problems

Kumar

2023

International Journal of Software Science and Computational Intelligence

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This article addresses the 3-dimensional mixed intuitionistic fuzzy assignment problems (3D-MIFAPs). In this article, firstly, the author formulates an assignment problem (AP) and assumes the parameters are in uncertainty with hesitation. Secondly, based on the nature of the parameter the author defines various types of solid assignment problem (SAP) in uncertain environment. Thirdly, to solve 3D-MIFAP the PSK method for finding an optimal solution of fully intuitionistic fuzzy assignment problem (FIFAP) is extended by the author. Fourthly, the author presents the proofs of the proposed theorems and corollary. Fifthly, the proposed approach is illustrated with three numerical examples and the optimal objective value of 3D-MIFAP is obtained in the form of intuitionistic fuzzy number and the solution is checked with MATLAB and their coding are also given by the author. Sixthly, the author presents the comparison results and their graphical representation, merits and demerits of the proposed and existing methods and finally the author presents conclusion and future research directions.

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Section: Introductionmentioning

confidence: 99%

Computationally Simple and Efficient Method for Solving Real-Life Mixed Intuitionistic Fuzzy 3D Assignment Problems

Kumar

2023

International Journal of Software Science and Computational Intelligence

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“…To solve the problem, genetic algorithm design is used. The study done by Kumar et al [5] provides a new mathematical model for allocating distributed jobs to several processors in order to obtain the best cost and system dependability. The cost of phase-wise execution, the cost of each task's residence on separate processors, the cost of inter-task communication, and the cost of each task's relocation have all been viewed as a fuzzy figure that is more realistic and accurate.…”

Section: Introductionmentioning

confidence: 99%

Algorithm fuzzy scheduling (AFS) for realtime jobs on multiprocessor systems

Holagundi¹,

Ashwathsetty²,

Basthikodi

2022

IJEECS

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The computing in Real-time is rapidly focusing much developments in technologies so that the real-time jobs are to be scheduled and executed on computing systems in particular time frame. The scheduling and load balancing techniques in distributed systems face numerous challenges because of lack of centralized strategy to dispatch the jobs in multiprocessors systems. In this work, we propose an Algorithm Fuzzy Scheduling (AFS) for real-time jobs that includes of Arrival time, Deadline and Computation time as the scheduling parameters of input. The approach AFS is analyzed and compared with Existing Fuzzy Algorithm (EFA) model for evaluation of performances from the outcome of the simulation. The jobs are scheduled on multiprocessor at higher system load by making use of fuzzy mechanisms in the algorithms. The experimental results prove that the proposed AFS achieves a better performance comparatively to EFA at various system load factors with respect to mean turnaroundtime, mean response time and count of missed deadlines. This is the initial phase of the algorithm, that will be enhanced to consider a greater number of parameters to be associated with jobs for better decision making and to investigate the scope for algorithm level parallelism.

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“…Pour et al [16] considered the processing time as a trapezoidal fuzzy numbers and find an optimum sequence in such a way that the completing time of jobs to be minimized. Kumar et al [20] have developed a tasks allocation model considering cost for each task as a fuzzy number which is more realistic and general in nature. Vinoj and Tijo [17] used genetic algorithm for the solution of the flow shop scheduling problem with the objective of minimizing mean flow time and P. Kumar et al [18] proposed a promising genetic algorithm with a random population generation in terms of makespan.…”

Section: Introductionmentioning

confidence: 99%

An effective new hybrid optimization algorithm for solving flow shop scheduling problems

Kumar¹,

Kumar²,

Sharma³

2018

Malaya J. Mat.

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A flow shop is a production system in which machines are arranged in the order in which operations are performed on jobs. The flow shop is characterized by a flow of work that is unidirectional. In this paper, a new hybrid optimization (NHO) algorithm combining branch and bound (B&B) technique with genetic algorithm (GA) is proposed for solving flow shop scheduling problem (FSSP). Triangular and trapezoidal fuzzy numbers are used to represent processing times of jobs on each machines which are more realistic and general in nature. The present algorithm is divided into two phases. In the first phase, an initial schedule is constructed by using branch and bound technique. The processing times have been defuzzified into crisp one. The second phase finds the best schedule of the jobs by genetic algorithm for fuzzy processing times. The performance of a genetic algorithm depends very much on the selection of the proper genetic operators. In this paper, partially matched crossover operator for crossover and shift mutation operator for mutation are used. Numerous examples are illustrated to explain the proposed approach. Finally, the experimental results show the suitability and efficiency of the present NHO algorithm for optimal flow shop scheduling problem.

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Dynamic Tasks Scheduling Algorithm for Distributed Computing Systems under Fuzzy Environment

Cited by 12 publications

References 12 publications

Computationally Simple and Efficient Method for Solving Real-Life Mixed Intuitionistic Fuzzy 3D Assignment Problems

Computationally Simple and Efficient Method for Solving Real-Life Mixed Intuitionistic Fuzzy 3D Assignment Problems

Algorithm fuzzy scheduling (AFS) for realtime jobs on multiprocessor systems

An effective new hybrid optimization algorithm for solving flow shop scheduling problems

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