FARCREST: Euclidean Steiner Tree-based cloud service latency prediction system

Abstract

Cloud resource provisioning is crucial to assure timely delivery of delay-sensitive cloud services. Today, virtual machine (VM) reservations are done mainly based on cloud resource availability. Maximum VM resources are often provisioned to ensure service response time, resulting in a waste of resources. While various techniques have been proposed to perform cloud response time measurement, most of these methodologies involve deploying standard target applications on selected cloud infrastructures, gathering and analyzing each individual dataset collected. Such methods are useful for offline analysis, but incur high overhead and not useful for real-time performance measurement for delay-sensitive application. In this paper, we first present a light-weight real time service latency prediction mechanism based on a Euclidean Steiner Tree (EST) model for optimum VM resource allocation in delay-sensitive cloud services. Our aim is to derive a highly accurate service latency prediction mechanism in a short time reflecting timely information of the actual cloud resources' conditions, while imposing minimum overheads on the cloud service itself. The experimental results demonstrate that the EST model achieves 60–80% VM service latency prediction accuracy with measurements towards only 20% of existing VMs.