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

arXiv: https://arxiv.org/abs/2102.12222 · HTML: https://mlsanity.com/paper/2102.12222/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2021.

## Built on by (most cited first, 2 of 2)

- [Test-Time Adaptive Composition for Machine Learning as a Service (MLaaS) in IoT Environments](https://mlsanity.com/paper/2606.07685/) (2026)
- [OrchNAS: Orchestrated Neural Architecture Search Service for Personalised Federated Edge Intelligence](https://mlsanity.com/paper/2607.22805/) (2026)

## Abstract (arXiv)

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.