|Authors||N. Durrani, F. Zahid and J. Shamsi|
|Title||PAVM: A Framework for Policy-Aware Virtual Machine Management|
|Publication Type||Journal Article|
|Year of Publication||2016|
|Journal||International Journal of Network Management|
|Publisher||John Wiley & Sons|
The problem of efficient placement of Virtual Machines (VMs) in cloud computing infrastructure is well studied in the literature. VM placement decision involves selecting a physical machine in the data center to host a specific VM. This decision could play a pivotal role in yielding high efficiency for both the cloud and its users. Also, reallocation of virtual machines could be performed through migrations to achieve goals like higher server consolidation or power saving. VM placement and re-allocation decisions may consider affinities such as memory-sharing, CPU processing, disk-sharing and network bandwidth requirements between virtual machines defined in multiple dimensions. Considering the NP-hard complexity associated with computing an optimal solution for this VM placement decision problem, existing research employs heuristic-based techniques to compute an efficient solution. However, most of these approaches are restricted to only a single attribute at a time. That is, a given technique of using heuristics to compute VM placement considers only a single attribute, while completely ignoring the impact of other dimensions of placing VMs. While this approach may improve the efficiency with respect to the affinity attribute in consideration, it may yield degraded performance with respect to other affinities. In addition, the criteria for determining VMplacement efficiency may vary for different applications. Hence the overall goal of achieving VM placement efficiency becomes difficult and challenging. We are motivated by this challenging problem of efficient VM placement and propose PAVM (Policy-Aware Virtual Machine Management), a generic framework that can be used for efficient virtual machine management in a cloud computing platform based on the service provider defined policies to achieve the desired system wide goals. This involves efficient means to profile different virtual machine affinities and to use profiled information effectively by intelligent and efficient virtual machine migrations at runtime considering multiple attributes at a time. By conducting extensive evaluation through simulation and real experiments which involve VM affinities on the basis of network and memory, we confirmed that the PAVM architecture is capable of improving the efficiency of a cloud system. We elaborate the architecture of a PAVM system, describe its implementation and present details of our experiments.