Long-term IaaS Selection using Performance Discovery

TL;DR

This paper introduces a framework for long-term IaaS provider selection that uses short-term trials and performance prediction techniques to meet long-term QoS requirements.

Contribution

It presents a novel approach combining temporal skyline filtering, workload replay, and a new workload generation model for long-term IaaS performance prediction.

Findings

Effective provider filtering with skyline-based method

Accurate long-term performance prediction using trial data

Framework validated with real-world datasets

Abstract

We propose a novel framework to select IaaS providers according to a consumer's long-term performance requirements. The proposed framework leverages free short-term trials to discover the unknown QoS performance of IaaS providers. We design a temporal skyline-based filtering method to select candidate IaaS providers for the short-term trials. A novel cooperative long-term QoS prediction approach is developed that utilizes past trial experiences of similar consumers using a workload replay technique. We propose a new trial workload generation model that estimates a provider's long-term performance in the absence of past trial experiences. The confidence of the prediction is measured based on the trial experience of the consumer. A set of experiments are conducted based on real-world datasets to evaluate the proposed framework.