Investigating Decision Support Techniques for Automating Cloud Service Selection
Miranda Zhang, Rajiv Ranjan, Armin Haller, Dimitrios Georgakopoulos,, Peter Strazdins

TL;DR
This paper proposes developing an intelligent decision support system to assist users in selecting optimal cloud infrastructure services amidst complex dependencies and diverse criteria.
Contribution
It introduces a novel approach to automate cloud service selection through decision support techniques, addressing the complexity of current cloud service choices.
Findings
Identifies key challenges in cloud service selection.
Proposes a framework for decision support in cloud environments.
Lays groundwork for future implementation and evaluation.
Abstract
The compass of Cloud infrastructure services advances steadily leaving users in the agony of choice. To be able to select the best mix of service offering from an abundance of possibilities, users must consider complex dependencies and heterogeneous sets of criteria. Therefore, we present a PhD thesis proposal on investigating an intelligent decision support system for selecting Cloud based infrastructure services (e.g. storage, network, CPU).
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
