GLAP: Distributed Dynamic Workload Consolidation through Gossip-Based Learning

Abstract

Dynamic virtual machine consolidation (DVMC) using live migration is one of the most promising solutions to reduce energy consumption in cloud data centers. Distributed DVMC often aggressively consolidates virtual machines (VMs) at the high expense of Service Level Agreement (SLA) of customers due to virtual machines (VMs) workload fluctuations. To alleviate this, static and adaptive threshold algorithms were proposed. However, the former is unable to predict the VMs future resource demands and the latter calculates a fixed threshold value for all physical machines (PMs) while each PM hosts VMs with different workload patterns. Moreover, both methods cannot consolidate VMs to maintain PMs in a long-term safe state. To overcome these problems, we propose a fully distributed and threshold-free DVMC algorithm called, GLAP. We combine Q-Learning with a gossip-based protocol to characterize workload patterns of VMs and take consolidation decisions. We also propose a novel two-phase distributed algorithm by which PMs unify the learned pattern which is vital for efficient execution of the algorithm. Finally, we compare GLAP experimentally against three existing techniques and show that GLAP reduces by from 43% to 78% the number of overloaded PMs under the Google Cluster VMs workload traces.