Long-term IaaS Provider Selection using Short-term Trial Experience
This addresses a domain-specific problem for cloud computing users needing reliable long-term provider selection, though it appears incremental in its approach.
The paper tackles the problem of selecting privacy-sensitive IaaS providers for long-term use by leveraging short-term trial experiences, proposing methods like equivalence partitioning and performance fingerprint matching to estimate long-term performance, with experimental results showing efficiency on real-world datasets.
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.