Network policy aware placement of tasks for elastic applications in IaaS-cloud environment

Abstract

Using cloud computing as a base, new technologies like data analytics, Internet of Things, machine learning etc., have emerged. Applications that use these technologies, depend on cloud datacenters (DC) for their computing power. Performance of these applications depends on dynamic resource provisioning by DC, as there is unpredictability of rate at which data arrives for immediate processing. Cloud service providers implement this dynamism in Infrastructure-as-a-Service (IaaS) environment, using elastic virtual machines (VM). Placing these VMs onto same physical machines (PM) and/or on the network neighborhood machines is believed to increase application performance as the network latency is minimal. Deploying sub-optimal VM placement schemes creates unwanted cross network traffic resulting in poor application performance and increases the DC operating cost. This paper formulates the policy and elastic aware placement (PEAP) as an optimization problem, with additional constraints such as fixed PM, balanced PM and co-location VMs. Further, we propose PEAP algorithm which considers individual requests demanding for one or more VMs as a whole for placement along with the life-time of requests. Proposed algorithm gives optimal VM placements for increased application performance and DC efficacy. CloudSimPlus based experiments demonstrate that as compared to first fit decreasing (FFD). First fit increasing (FFI) and first come first serve (FCFS) algorithms, the proposed technique leads to reduced resource fragmentation and resource migrations. PEAP achieves placement of all the elastic VMs together with reduced network cost, thereby increasing the application performance.