# Long-term IaaS Selection using Performance Discovery

arXiv: https://arxiv.org/abs/2011.00644 · HTML: https://mlsanity.com/paper/2011.00644/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2020.

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

- [Adaptive Composition of Machine Learning as a Service (MLaaS) for IoT Environments](https://mlsanity.com/paper/2506.11054/) (2025)
- [Sequential Learning-based IaaS Composition](https://mlsanity.com/paper/2102.12598/) (2021)

## Abstract (arXiv)

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.