绿色云数据中心中具有带宽保证的时间感知虚拟机放置和路由

Aissan Dalvandi, G. Mohan, K. Chua
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引用次数: 20

摘要

由于共享资源导致的网络性能变化是云采用的主要障碍。因此,云提供商能否成功吸引更多的租户取决于他们提供带宽保证的能力。数据中心的电源效率对于支持更多的租户已经变得至关重要。在本文中,我们解决了时间感知vm放置和路由(TVPR)的问题,其中每个租户在给定的持续时间内请求指定数量的服务器资源(vm)和网络资源(带宽)。TVPR问题通过找到正确的服务器集来映射虚拟机并路由其流量,从而为尽可能多的租户分配所需的资源,从而最小化总功耗。我们提出了一个基于多组件利用率的功率模型,根据组件(服务器和交换机)的资源利用率来确定数据中心的总功耗。在此基础上提出了一个混合整数线性规划(MILP)优化问题,并证明了它是np完备的。由于TVPR问题在计算上是禁止的,我们开发了一个快速和可扩展的启发式算法。为了证明我们提出的算法的效率,我们将其性能与使用CPLEX解决小型数据中心的MILP问题所获得的数值结果进行了比较。然后,我们通过仿真结果证明了所提出算法在大型数据中心的功耗和接受率方面的有效性。
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Time-Aware VM-Placement and Routing with Bandwidth Guarantees in Green Cloud Data Centers
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.
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