Optimizing scheduling in cloud computing using the Cuckoo optimization algorithm

Rashidi, Kasra

doi:10.22075/ijnaa.2024.35006.5225

Optimizing scheduling in cloud computing using the Cuckoo optimization algorithm

Articles in Press

Document Type : Research Paper

Author

Kasra Rashidi

Department of Computer, Isfahan University, Isfahan, Iran

10.22075/ijnaa.2024.35006.5225

Abstract

The cuckoo optimisation algorithm is a meta-exploratory optimization algorithm used to solve nonlinear continuous optimization problems. Optimization is to find a way of fulfilling a task in the best manner. Principally, optimization refers to changing a series of primary information and using problem information to achieve more appropriate responses. Recently, graphics processors have been proposed as a multi-purpose computational device due to low cost, parallel architecture and improved access provided by programming environments such as the CUDA framework. The Master-Slave Model is one of the parallelizing models of optimization algorithms. In the present paper, shared memory, reduction operations and other factors affecting GPUS have been employed using CUDA and the Master-Slave model with a fine-grain technique to increase the efficiency of the parallel algorithm. In this work, the implementation time of parallel and serial algorithms has been evaluated using a benchmark function. The experiments' results revealed an increase in speed-up of the parallel algorithm compared to the serial algorithm.

Keywords

optimizer algorithm

graphic processing units (GPU)

parallel and series algorithms

cloud computing

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