Agent-based load balancing in Cloud data centers

Abstract

Cloud data centers are generally composed of heterogeneous commodity servers hosting multiple virtual machines (VMs) with potentially different specifications and fluctuating resource usages. This may cause a resource usage imbalance within servers that may result in performance degradation and violations to service level agreements. This work proposes a collaborative agent-based problem solving technique capable of balancing workloads across commodity, heterogeneous servers by making use of VM live migration. The agents are endowed with (i) migration heuristics to determine which VMs should be migrated and their destination hosts, (ii) migration policies to decide when VMs should be migrated, (iii) VM acceptance policies to determine which VMs should be hosted, and (iv) front-end load balancing heuristics. The results show that agents, through autonomous and dynamic collaboration, can efficiently balance loads in a distributed manner outperforming centralized approaches with a performance comparable to commercial solutions, namely Red Hat, while migrating fewer VMs.