A Load Balancing Algorithm for Resource Allocation in Cloud Computing
作者： Seyedmajid Mousavi , Amir Mosavi , Annamária R. Varkonyi-Koczy
DOI: 10.1007/978-3-319-67459-9_36
关键词:
摘要: Utilizing dynamic resource allocation for load balancing is considered as an important optimization process of task scheduling in cloud computing. A poor policy may overload certain virtual machines while remaining are idle. Accordingly, this paper proposes a hybrid algorithm with combination Teaching-Learning-Based Optimization (TLBO) and Grey Wolves algorithms (GWO), which can well contribute maximizing the throughput using balanced across overcome problem trap into local optimum. The benchmarked on eleven test functions comparative study conducted to verify results particle swarm (PSO), Biogeography-based (BBO), GWO. To evaluate performance proposed balancing, simulated experimental presented.
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