Advanced Cloud based Task Scheduling Architecture to Optimize Performance in Datacenter

De, Mou; Kundu, Anirban; Guha, Sutirtha Kr.

doi:10.1109/icce50343.2020.9290581

Cited by 1 publication

(1 citation statement)

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“…Due to the increase of objective functions, the optimal solution set is sometimes too large, which leads to the problem of message overflow when solving multiobjective problems with this method [19]. (b) Multiobjective evolutionary algorithms based on performance indicators [20]. When using the performance index as the reference information for the evolution of the algorithm, the calculation of the performance index is too complicated, resulting in a long running time [21].…”

Section: Multiobjective Problemmentioning

confidence: 99%

Swarm Intelligence Algorithms for Optimal Scheduling for Cloud-Based Fuzzy Systems

AlSuwaidan

Khan

Almakki

et al. 2022

Mathematical Problems in Engineering

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A fuzzy cloud resource scheduling model with time-cost constraints is built using fuzzy triangular numbers to represent uncertain task execution time. Task scheduling reduces total time and cost spent on a project. It connects virtual machines and functions. Particle swarm optimization (HPO) is used to plan cloud resources (HSOA). The approach uses orthogonal particle swarm initialization to increase the quality of the initial particle exploration, rerandomization to regulate the particle search range, and real-time updating of inertia weights to control particle speed. The suggested problem model and optimization approach are evaluated using random simulation data provided by the CloudSim simulation platform. Less overall execution time and a lower cost are shown to have fast convergence and solution capabilities in experiments.

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Section: Multiobjective Problemmentioning

confidence: 99%