QoS-aware service VM provisioning in clouds: Experiences, models, and cost analysis

Abstract

Recent studies show that service systems hosted in clouds can elastically scale the provisioning of pre-configured virtual machines (VMs) with workload demands, but suffer from performance variability, particularly from varying response times. Service management in clouds is further complicated especially when aiming at striking an optimal trade-off between cost (i.e., proportional to the number and types of VM instances) and the fulfillment of quality-of-service (QoS) properties (e.g., a system should serve at least 30 requests per second for more than 90% of the time). In this paper, we develop a QoS-aware VM provisioning policy for service systems in clouds with high capacity variability, using experimental as well as modeling approaches. Using a wiki service hosted in a private cloud, we empirically quantify the QoS variability of a single VM with different configurations in terms of capacity. We develop a Markovian framework which explicitly models the capacity variability of a service cluster and derives a probability distribution of QoS fulfillment. To achieve the guaranteed QoS at minimal cost, we construct theoretical and numerical cost analyses, which facilitate the search for an optimal size of a given VM configuration, and additionally support the comparison between VM configurations. © 2013 Springer-Verlag.