Long-term IaaS Provider Selection using Short-term Trial Experience

TL;DR

This paper introduces a method for selecting long-term IaaS providers by analyzing short-term trial experiences, using equivalence partitioning and performance fingerprinting to predict long-term QoS performance.

Contribution

It presents a novel trial strategy and performance estimation method that effectively predicts long-term provider performance from short-term trials.

Findings

Efficient prediction of long-term QoS performance

Effective discovery of provider variability

Validated with real-world datasets

Abstract

We propose a novel approach to select privacy-sensitive IaaS providers for a long-term period. The proposed approach leverages a consumer's short-term trial experiences for long-term selection. We design a novel equivalence partitioning based trial strategy to discover the temporal and unknown QoS performance variability of an IaaS provider. The consumer's long-term workloads are partitioned into multiple Virtual Machines in the short-term trial. We propose a performance fingerprint matching approach to ascertain the confidence of the consumer's trial experience. A trial experience transformation method is proposed to estimate the actual long-term performance of the provider. Experimental results with real-world datasets demonstrate the efficiency of the proposed approach.