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An improved Grey Wolf Optimizer (iGWO) for Load Balancing in Cloud Computing Environment

机译：改进的灰狼优化器（iGWO），用于云计算环境中的负载平衡

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摘要

Load balancing in any system aims to optimize throughput, resource use, imbalance load, response time, overutilization of resources, etc. An efficient load balancing framework in cloud computing environment with such features may improve overall system performance, resource availability and fulfillment of SLAs. Nature-inspired metaheuristic algorithms are getting more popularity day by day due to their simplicity, flexibility and ease implementation. The success and challenges of these algorithms are based on their specific control parameter selection and tuning. A relatively new algorithm motivated by the social hierarchy and hunting behavior of grey wolves is Grey Wolf Optimizer (GWO), which is having least dependency on the control parameters. In the basic GWO, 50% of the iterations are reserved for exploration and others for exploitation. The perfect balance between exploration and exploitation is overlooked in GWO. The impact of perfect balance between two guarantees a near optimal solution. To get over this problem, an improved GWO (iGWO) is proposed in this paper, which focuses on the required meaningful balance between exploration and exploitation that leads to an optimal performance of the algorithm. Simulation results based on exploitation and exploration benchmark functions and the problem of load balancing in cloud demonstrate the effectiveness, efficiency, and stability of iGWO compared with the classical GWO, HS, ABC and PSO algorithms.
机译：任何系统中的负载平衡旨在优化吞吐量，资源使用，负载不平衡，响应时间，资源过度利用等。在云计算环境中具有此类功能的高效负载平衡框架可以提高整体系统性能，资源可用性和SLA的实现。自然启发的元启发式算法由于其简单性，灵活性和易实现性而日益流行。这些算法的成功和挑战基于其特定的控制参数选择和调整。受灰狼的社会等级和狩猎行为激励的相对较新的算法是“灰狼优化器”（GWO），它对控制参数的依赖性最小。在基本的GWO中，50％的迭代保留给勘探，而其他的则保留给开发。 GWO忽略了勘探与开发之间的完美平衡。两者之间完美平衡的影响保证了接近最佳的解决方案。为了解决这个问题，本文提出了一种改进的GWO（iGWO），其重点是探索和开发之间所需的有意义的平衡，从而导致算法的最佳性能。与传统的GWO，HS，ABC和PSO算法相比，基于开发和探索基准功能的仿真结果以及云中的负载平衡问题证明了iGWO的有效性，效率和稳定性。

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