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Time-Aware VM-Placement and Routing with Bandwidth Guarantees in Green Cloud Data Centers

机译:绿色云数据中心中的带宽保证的时间感知VM-PLATEMENT和路由

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Variation in network performance due to the shared resources is a key obstacle for cloud adoption. Thus, the success of cloud providers to attract more tenants depends on their ability to provide bandwidth guarantees. Power efficiency in data centers has become critically important for supporting larger number of tenants. In this paper, we address the problem of time-aware VM-placement and routing (TVPR), where each tenant requests for a specified amount of server resources (VMs) and network resource (bandwidth) for a given duration. The TVPR problem allocates the required resources for as many tenants as possible by finding the right set of servers to map their VMs and routing their traffic so as to minimize the total power consumption. We propose a multi-component utilization-based power model to determine the total power consumption of a data center according to the resource utilization of the components (servers and switches). We then develop a mixed integer linear programming (MILP) optimization problem formulation based on the proposed power model and prove it to be N P-complete. Since the TVPR problem is computationally prohibitive, we develop a fast and scalable heuristic algorithm. To demonstrate the efficiency of our proposed algorithm, we compare its performance with the numerical results obtained by solving the MILP problem using CPLEX, for a small data center. We then demonstrate the effectiveness of the proposed algorithm in terms of power consumption and acceptance ratio for large data centers through simulation results.
机译:由于共享资源,网络性能的变化是云采用的关键障碍。因此,云提供商吸引更多租户的成功取决于他们提供带宽保证的能力。数据中心的功率效率对支持更多数量的租户来说是至关重要的。在本文中,我们解决了时间感知VM-PLATEMENT和路由(TVPR)的问题,其中每个租户用于给定持续时间的指定量的服务器资源(VM)和网络资源(带宽)。 TVPR问题可以通过查找为映射其VM和路由其流量的正确的服务器组来为尽可能多的租户分配所需的资源,以便最小化总功耗。我们提出了一种基于多组分利用的功率模型,以确定数据中心的总功耗根据组件的资源(服务器和交换机)。然后,我们基于所提出的电力模型,开发混合整数线性编程(MILP)优化问题配方,并证明它是N P-Tricep。由于TVPR问题在计算上禁止,我们开发了一种快速且可扩展的启发式算法。为了展示我们所提出的算法的效率,我们将其性能与通过使用CPLEX解决小型数据中心来解决MILP问题而获得的数值结果。然后,我们通过模拟结果展示了在大数据中心的功耗和接受比方面所提出的算法的有效性。

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