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.

资源管理是任务调度和资源调配以提供云用户需求的过程。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率，从而提高云计算的效率。现有方法可以分为几类，包括数学和统计方法，基于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完全的，因此在该领域还开发了几种优化方法。珊瑚礁算法是一种进化方法，已针对某些问题显示出适当的收敛性和响应时间，因此，本文将其与强化学习相结合，以提高云环境中的资源管理效率。本文提出的方法包括两个阶段。在第一阶段，使用珊瑚礁算法将资源初始分配给准备执行的任务。在此方法中，任务被视为珊瑚，资源被视为礁石。第二阶段利用强化学习来避免陷入局部最优状态，并使用长期方法来最佳利用资源。本文提出的模型称为MO-CRAML，它引入了一种新的混合算法，该方法结合了珊瑚礁优化算法和强化学习，可以提高云资源的利用率和负载平衡。