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

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 particular, re...

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Vydáno v:	Journal of big data Ročník 7; číslo 1; s. 1 - 25
Hlavní autoři:	Seethalakshmi, V., Govindasamy, V., Akila, V.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Cham Springer International Publishing 10.07.2020 Springer Nature B.V SpringerOpen
Témata:	Advanced Soft Computing Methodologies and Applications in Social Media Big Data Analytics Algorithms Big Data Communications Engineering Computational Science and Engineering Computer networks Computer Science Computer simulation Consumers Data Mining and Knowledge Discovery Data processing Database Management Decision making Descent Distributed processing Dynamic characteristics Energy consumption Energy usage Foraging behavior Gradient Descent Algorithm Heterogeneous Environment Heuristic Heuristic methods Information Storage and Retrieval Load balancing Mathematical Applications in Computer Science Networks Optimal Resource Allocation Optimization Resource allocation Resource efficiency Resource management Resource scheduling Resources Scheduling Simulation Social behavior Social factors Spider Monkey Optimization (SMO) Task scheduling Heterogeneous Environment Load balancing Optimal Resource Allocation Gradient Descent Algorithm Spider Monkey Optimization (SMO)
ISSN:	2196-1115, 2196-1115
On-line přístup:	Získat plný text
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