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

Abstract

Cloud resource provisioning is crucial to assure timely deliverable of delay-sensitive cloud services. Today, virtual machine (VM) reservations are done mainly based on cloud resource availability. Often, maximum VM resources are preserved to assure 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 infrastructure, gathering, and analyzing each individual dataset collected. Such methods are useful for offline analysis, but incur high overhead and are not useful for real-time performance measurement for delay-sensitive application. In this demo, we present a light-weight real time service latency prediction mechanism based on 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 to the cloud service itself. We shall present a fast response cloud resource estimation system - FARCREST which integrates the prediction model with cloud front-end server for VM services latency prediction and deployment with production cloud experiment results.