Application-Aware Resource Allocation for SDN-based Cloud Datacenters
Application-Aware Resource Allocation for SDN-based Cloud Datacenters
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DOI：
10.1109/cloudcom-asia.2013.44
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发表时间：
2013-12
期刊：
影响因子：
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通讯作者：
W. Hong;Kuochen Wang;Yi-Huai Hsu
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文献类型：
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作者：
W. Hong;Kuochen Wang;Yi-Huai Hsu
在云计算中心中，由于资源需求的频繁变化，如何在满足不同类型应用的服务水平协议（SLA）的同时，有效地分配和管理资源是一个重要的研究课题。在本文中，我们提出了一个应用程序感知的资源分配（App-RA）计划，预测资源需求，并分配适当数量的虚拟机（VM）为每个应用程序在基于SDN的云计算中心。据我们所知，拟议的App-RA是第一个应用程序感知的资源分配方案，适应于所有类型的应用程序。App-RA可以满足SLA，有效分配资源，并降低云计算中心中每个应用的功耗。建议App-RA采用基于神经网络的预测器来预测应用程序的资源需求（CPU，内存，GPU，磁盘I/O和带宽）。在建议的App-RA中，我们设计了两种算法，分配适当数量的虚拟机，并使用VM分配阈值，以避免SLA违反五种不同类型的应用程序。此外，我们采用了一个基于SDN的OpenFlow网络与CICQ交换机适当调度数据包的不同类型的应用程序在网络层。最后，仿真结果表明，所提出的App-RA的功耗仅为9.21%，比最佳情况下（Oracle）和功耗的EAACVA，这是一个代表性的非图形应用程序的资源分配方法，是104.58%，比App-RA。此外，所提出的App-RA的SLA违规率对于所有应用都小于4%。
In cloud datacenters, since resource requirements change frequently, how to assign and manage resources efficiently while meeting service level agreements (SLAs) of different types of applications is an important research issue. In this paper, we propose an Application-aware Resource Allocation (App-RA) scheme to predict resource requirements and allocate an appropriate number of virtual machines (VMs) for each application in SDN-based cloud datacenters. To the best of our knowledge, the proposed App-RA is the first application-aware resource allocation scheme that adapts to all types of applications. The App-RA can meet SLAs, allocate resources efficiently, and reduce power consumption for each application in cloud datacenters. The proposed App-RA adopts the neural network based predictor to forecast the requirements of resources (CPU, Memory, GPU, Disk I/O and bandwidth) for an application. In the proposed App-RA, we have designed two algorithms which allocate appropriate numbers of virtual machines and use the VM allocation threshold to avoid SLA violations for five different types of applications. In addition, we adopt an SDN-based OpenFlow network with CICQ switches to appropriately schedule packets for different types of application in the network layer. Finally, simulation results show that the power consumption of the proposed App-RA is only 9.21% higher than that of the best case (oracle) and the power consumption of EAACVA, which is a representative resource allocation method for non-graphic applications, is 104.58% worse than that of App-RA. Furthermore, the SLA violation rate of the proposed App-RA is less than 4% for all applications.