Grey Wolf Optimizer based Resource Allocation and Optimization Algorithm in Cloud Computing Environment
Keywords:
Resource Allocation, Optimization, Cloud Computing.Abstract
The Internet of Things (IoT) operates as a decentralized network where various devices connect to the internet for communication. This intricate structure consists of multiple resources, gateways, and cluster heads. Effectively managing IoT resource allocation and scheduling tasks within this environment poses a significant challenge. The allocation and scheduling processes play a crucial role in establishing connections between IoT resources and gateways, ensuring optimized resource distribution at gateways. Given the potential for heavy traffic at individual gateways, manual resource allocation and scheduling are impractical, leading to increased overhead. To address this issue, our research proposes a hybrid approach aimed at optimizing resources and minimizing transmission costs. The approach leverages the Grey Wolf Optimizer (GWO) algorithm, inspired by the hunting behavior of grey wolves. Through experimentation on various benchmark functions, the hybrid GWO demonstrates satisfactory results, showcasing its potential for enhancing IoT resource management.
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