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Combined use of coral reefs optimization and reinforcement learning for improving resource utilization and load balancing in cloud environments

机译：珊瑚礁综合使用优化和加固学习改善云环境中资源利用率和负载平衡

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Resource management is the process of task scheduling and resource provisioning to provide requirements of cloud users. Since cloud resources are often heterogeneous, task scheduling and resource provisioning are major challenges in this area. Various methods have been introduced to improve resource utilization and thus increase the efficiency of cloud computing. Existing methods can be divided into several categories, including mathematical and statistical methods, heuristic- and meta-heuristic-based methods, and machine-learning-based methods. Since the resource management problem is NP-complete, several optimization methods have been also exploited in this area. Coral reefs algorithm is an evolutionary method that has showed appropriate convergence and response time for some problems, and thus is used in this paper to combine with reinforcement learning to improve efficiency of resource management in cloud environments. The proposed method of this paper consists of two phases. The initial allocation of resources to ready-to-perform tasks is done using the coral reefs algorithm in the first phase. The tasks are considered as corals and the resources are considered reefs in this method. The second phase utilizes reinforcement learning to avoid falling into the local optima and to make optimal use of resources using a long-term approach. The proposed model of this paper, called MO-CRAML, introduces a new hybrid algorithm for improving utilization and load balancing of cloud resources using the combination of coral reefs optimization algorithm and reinforcement learning. The results of the experiments show that the proposed algorithm has better performance in cloud resource utilization and load balancing in comparison with some other important methods of the literature.
机译：资源管理是任务调度和资源配置的过程，以提供云用户的要求。由于云资源通常是异构的，因此任务调度和资源供应是该领域的主要挑战。已经引入了各种方法来提高资源利用，从而提高云计算的效率。现有方法可分为几个类别，包括数学和统计方法，启发式和基于元启发式的方法，以及基于机器学习的方法。由于资源管理问题是NP-Trice，因此该区域也已利用了几种优化方法。珊瑚礁算法是一种进化方法，显示出一些问题的适当收敛和响应时间，因此在本文中使用了与加强学习相结合，以提高云环境中资源管理效率。本文的拟议方法包括两个阶段。使用第一阶段中的珊瑚礁算法完成对即刻执行任务的初始资源分配。任务被视为珊瑚，资源被认为是这种方法中的珊瑚礁。第二阶段利用增强学习，避免落入本地最佳，并使用长期方法实现资源的最佳利用。本文的拟议模型称为MO-CRAML，介绍了一种新的混合算法，用于使用珊瑚礁优化算法和增强学习的组合来提高云资源的利用率和负载平衡。实验结果表明，与文献的其他一些重要方法相比，该算法在云资源利用率和负载平衡方面具有更好的性能。

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