Optimization Heuristics for Cost-Efficient Long-Term Cloud Portfolio Allocations

Optimization Heuristics for Cost-Efficient Long-Term Cloud Portfolio Allocations

Abstract

Today's cloud infrastructure landscape offers a broad range of services to operate software applications. The myriad of options, however, has also brought along a new layer of complexity. When it comes to procuring cloud computing resources, consumers can purchase their virtual machines from different providers on different marketspaces to form so called cloud portfolios: a bundle of virtual machines whereby the virtual machines have different technical characteristics and pricing mechanisms. Thus, selecting virtual machines for a given set of applications such that the allocations are cost-efficient is a non-trivial task. In this paper we propose a formal specification of the cloud portfolio management problem that takes an application-driven approach and incorporates the nuances of the commonly encountered reserved, on-demand and spot market types. We present two distinct cost optimization heuristics for this stochastic temporal bin packing problem, one taking a naive first fit strategy, while the other is built on the concepts of genetic algorithms. The results of the evaluation show that the former optimization approach significantly outperforms the latter.

Grafik Top
Authors
  • Kiessler, Maximilian
  • Haag, Valentin
  • Pittl, Benedikt
  • Schikuta, Erich
Grafik Top
Shortfacts
Category
Paper in Conference Proceedings or in Workshop Proceedings (Paper)
Event Title
24th International Conference on Information Integration and Web Intelligence (iiWAS2022)
Divisions
Workflow Systems and Technology
Subjects
Angewandte Informatik
Event Location
Online virtual conference
Event Type
Conference
Event Dates
28 - 30 November 2022
Date
2022
Export
Grafik Top