Sequential Learning-based IaaS Composition

TL;DR

This paper introduces a sequential learning-based framework for IaaS composition that optimizes request selection based on provider preferences using TempCP-net, similarity measures, and Q-learning, demonstrating its effectiveness through experiments.

Contribution

It presents a novel IaaS composition framework integrating TempCP-net preferences, similarity measures, and an extended Q-learning approach for optimal request selection.

Findings

Framework effectively models qualitative preferences.

Q-learning approach improves request selection.

Experimental results validate the framework's feasibility.

Abstract

We propose a novel IaaS composition framework that selects an optimal set of consumer requests according to the provider's qualitative preferences on long-term service provisions. Decision variables are included in the temporal conditional preference networks (TempCP-net) to represent qualitative preferences for both short-term and long-term consumers. The global preference ranking of a set of requests is computed using a \textit{k}-d tree indexing based temporal similarity measure approach. We propose an extended three-dimensional Q-learning approach to maximize the global preference ranking. We design the on-policy based sequential selection learning approach that applies the length of request to accept or reject requests in a composition. The proposed on-policy based learning method reuses historical experiences or policies of sequential optimization using an agglomerative clustering approach. Experimental results prove the feasibility of the proposed framework.