Sequential Learning-based IaaS Composition
This work addresses IaaS composition for cloud service providers, but appears incremental as it builds on existing preference networks and learning methods.
The authors tackled the problem of selecting optimal consumer requests for IaaS providers by developing a framework that maximizes global preference rankings using qualitative preferences and temporal similarity measures. Experimental results demonstrated the framework's feasibility.
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.