# Sequential Learning-based IaaS Composition

arXiv: https://arxiv.org/abs/2102.12598 · HTML: https://mlsanity.com/paper/2102.12598/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 83 references that are in the map.
- First posted on arXiv 24 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Safe and Efficient Off-Policy Reinforcement Learning](https://mlsanity.com/paper/1606.02647/) (2016)
- [Next Generation Cloud Computing: New Trends and Research Directions](https://mlsanity.com/paper/1707.07452/) (2017)
- [Event-based Detection of Changes in IaaS Performance Signatures](https://mlsanity.com/paper/2007.11705/) (2020)
- [Long-term IaaS Selection using Performance Discovery](https://mlsanity.com/paper/2011.00644/) (2020)
- [Signature-based Selection of IaaS Cloud Services](https://mlsanity.com/paper/2004.12378/) (2020)

## Abstract (arXiv)

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 textitk-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.