CloudMap: Workload-aware placement in private heterogeneous clouds

Abstract

Cloud computing has emerged as an exciting hosting paradigm to drive up server utilization and reduce data center operational costs. Even though clouds present a single unified homogeneous resource pool view to end users, the underlying server landscape may differ in terms of functionality and reconfiguration capabilities (e.g., support for shared processors, live migration). In a private cloud setting where information on the resources as well as workloads are available, the placement of applications on clouds can leverage it to achieve better consolidation with performance guarantees. In this work, we present the design and implementation of CloudMap, a provisioning system for private clouds. Given an application's resource usage patterns, we match it with a server cluster with the appropriate level of reconfiguration capability. In this cluster, we place the application on a server that has existing workloads with complementary resource usage profile. CloudMap is implemented using a hybrid architecture with a global server cluster selection module and local cluster-specific server selection modules. Using production traces from live data centers, we demonstrate the effectiveness of CloudMap over existing placement methodologies. © 2012 IEEE.