DNA computing and meta-heuristic-based algorithm for big data task scheduling in cloud computing

Visalaxi Gandhimathinathan, Muthukumaravel Alagesan


With the advent of cloud computing, there is a need to enhance both the methods and algorithms of big data workloads for task scheduling. Due to the global spread of services with changing task load circumstances and different cloud client demands, big data task scheduling in cloud systems is a time-consuming process. The proposed approach emphasises the necessity for efficient big data task scheduling in the cloud computing, which exacerbate data processing. Virtual machines frequently utilise all three types of physical resources: CPU, memory, and storage. Big data task scheduling is one of the most important implications of cloud computing application resource management, and this research work meticulously offers a task scheduling technique for advancing cloud computing.


Bat sonar algorithm; Big data task scheduling; Cloud computing; DNA computing; Virtual machine

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DOI: http://doi.org/10.11591/ijeecs.v35.i2.pp1131-1138


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The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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