Abstract
Application of cloud computing is rising substantially due to its capability to deliver scalable computational power. System attempts to allocate a maximum number of resources in a manner that ensures that all the service level agreements (SLAs) are maintained. Virtualization is considered as a core technology of cloud computing. Virtual machine (VM) instances allow cloud providers to utilize datacenter resources more efficiently. Moreover, by using dynamic VM consolidation using live migration, VMs can be placed according to their current resource requirements on the minimal number of physical nodes and consequently maintaining SLAs. Accordingly, non optimized and inefficient VMs consolidation may lead to performance degradation. Therefore, to ensure acceptable quality of service (QoS) and SLA, a machine learning technique with modified kernel for VMs live migrations based on adaptive prediction of utilization thresholds is presented. The efficiency of the proposed technique is validated with different workload patterns from Planet Lab servers.
Highlights
Resource optimization has been improved significantly by virtualization
Our objective is to provide a machine learning and statistical based predictive model to predict Virtual machine (VM) migration and maintaining the service level agreements (SLAs)
TPR is defined as VM migration being correctly classified due to high utilizations
Summary
Resource optimization has been improved significantly by virtualization It introduced isolation between application and the physical resource [1], it allows live virtual machines (VMs) to seamlessly move between physical hosts. This allows service providers to host high availability applications and to better commit to their level of service governed by a service level agreement (SLA). These interruptions can cause performance degradation which varies between applications [3,4]. CPU utilization, inter VM bandwidth utilization and memory utilization will be used as potential classifiers
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