Dynamic Threshold Setting for VM Migration

Abstract

Cloud data centres require efficient management of resources and robust methods that consider SLA violations, node utilisation and simplify the adaptation decision making process. Thus resource management should be ideally solved in an online manner. To address this, approaches have been presented in the literature to set thresholds that trigger VM migration. One challenge with these approaches is they typically use node metrics (e.g., CPU and memory) as an indicator of VM performance and do not factor in VM performance metrics when setting the CPU migration threshold. A hypothesis is that migrating VMs without factoring in VM performance metrics, e.g., response time can lead to either early or delayed migration of VMs. We present an approach to discover the CPU utilization level for VM migration dynamically. This approach monitors VM response time and discovers the CPU threshold where response time would increase beyond a defined SLA level and uses this threshold for VM migration. We use reinforcement learning (RL) to learn when it is rewarding to migrate a VM. The RL reward function drives a policy towards high CPU utilisation and attaches a penalty to overachieving SLAs. We use simulation to evaluate the approach and compare it to 4 heuristics: Static, Interquartile Range, Median Absolute Deviation, Local Regression. The results show a significant reduction in SLA violations and an increase in CPU utilization of active nodes.

The Virtual Machine Migration Strategy Based on Dynamic Threshold

“…The proposed work used cosine similarity and bandwidth utilization as additional utilities to improve current performance QoS. Hummaida et al [10] proposed a method for dynamically discovering CPU utilization during virtual machine migration. This approach monitors VM response time, identifies CPU thresholds when response time exceeds defined SLA levels, and uses these thresholds for VM migration.…”