Hybrid gradient descent spider monkey optimization (HGDSMO) algorithm for efficient resource scheduling for big data processing in heterogenous environment

Hybrid gradient descent spider monkey optimization (HGDSMO) algorithm for efficient resource scheduling for big data processing in heterogenous environment

Abstract Big Data constructed based on the advancement of distributed computing and virtualization is considered as the current emerging trends in Data Analytics. It is used for supporting potential utilization of computing resources focusing on, on-demand services and resource scalability. In parti...

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Bibliographic Details
Main Authors: V. Seethalakshmi, V. Govindasamy, V. Akila
Format: Article
Language:English
Published: SpringerOpen 2020-07-01
Series:Journal of Big Data
Subjects:
Heterogeneous Environment
Optimal Resource Allocation
Gradient Descent Algorithm
Spider Monkey Optimization (SMO)
Load balancing
Online Access:http://link.springer.com/article/10.1186/s40537-020-00321-w

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http://link.springer.com/article/10.1186/s40537-020-00321-w

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