Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints
IEEE Transactions on Parallel and Distributed Systems
Dynamic consolidation of Virtual Machines (VMs) is an effective way to improve the utilization of resources and energy efficiency in Cloud data centers. Determining when it is best to reallocate VMs from an overloaded host is an aspect of dynamic VM consolidation that directly influences the resource utilization and Quality of Service (QoS) delivered by the system. The influence on the QoS is explained by the fact that server overloads cause resource shortages and performance degradation of
... degradation of applications. Current solutions to the problem of host overload detection are generally heuristic-based, or rely on statistical analysis of historical data. The limitations of these approaches are that they lead to sub-optimal results and do not allow explicit specification of a QoS goal. We propose a novel approach that for any known stationary workload and a given state configuration optimally solves the problem of host overload detection by maximizing the mean inter-migration time under the specified QoS goal based on a Markov chain model. We heuristically adapt the algorithm to handle unknown non-stationary workloads using the Multisize Sliding Window workload estimation technique. Through simulations with real-world workload traces from more than a thousand PlanetLab VMs, we show that our approach outperforms the best benchmark algorithm and provides approximately 88% of the performance of the optimal offline algorithm.