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Joint Customer/Provider Evolutionary Multi-objective Utility Maximization in Cloud Data Center Networks
Iranian Journal of Science and Technology, Transactions of Electrical Engineering ( IF 2.4 ) Pub Date : 2020-10-04 , DOI: 10.1007/s40998-020-00381-x
Pejman Goudarzi , Mehdi Hosseinpour , Muhammad R. Ahmadi

Cloud computing has found an important role in cost-effective and elastic network resource allocation community. Most researchers and practitioners have focused on cloud provider side for efficient resource allocation to end-users and to warrant their service-level agreements (SLAs). Due to heterogeneous user demands which request services from cloud data center networks, simultaneous satisfaction of all users to their pre-defined SLA level while making the cloud provider and all of the data centers to be cost-effective in terms of the financial income and energy-efficiency is a challenging issue. To address this problem, in the current work, the efficient joint provider/customer efficient resource allocation has been first formulated as a multi-objective optimization problem with convex constraint set in terms of virtual machine (VM) parameters (e.g., storage, CPU core, memory, etc.). Then, an evolutionary solution based on cooperative co-evolution algorithm has been proposed that can conduct a divide-and-conquer methodology for solving the complex optimization problem. Numerical results show the prominent feature of the method in terms of VM usage effectiveness and overall joint network cost/user SLA satisfaction.

中文翻译:

云数据中心网络中的联合客户/提供商进化多目标效用最大化

云计算在具有成本效益和弹性的网络资源分配社区中发挥了重要作用。大多数研究人员和从业人员都将重点放在云提供商方面,以便向最终用户有效分配资源并保证他们的服务级别协议 (SLA)。由于异构用户需求向云数据中心网络请求服务,所有用户同时满足其预先定义的 SLA 水平,同时使云提供商和所有数据中心在财务收入和能源方面具有成本效益- 效率是一个具有挑战性的问题。为了解决这个问题,在目前的工作中,高效的联合提供者/客户高效资源分配首先被表述为一个多目标优化问题,在虚拟机 (VM) 参数(例如,存储、CPU 内核、内存等)方面设置了凸约束。然后,提出了一种基于协同协同进化算法的进化解,该算法可以采用分而治之的方法来解决复杂的优化问题。数值结果显示了该方法在 VM 使用效率和整体联合网络成本/用户 SLA 满意度方面的突出特点。
更新日期:2020-10-04
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