Cost modelling and optimisation for cloud: a graph-based approachShow others and affiliations
2024 (English)In: Journal of Cloud Computing: Advances, Systems and Applications, E-ISSN 2192-113X, Vol. 13, no 1, article id 147
Article in journal (Refereed) Published
Abstract [en]
Cloud computing has become popular among individuals and enterprises due to its convenience, scalability, and flexibility. However, a major concern for many cloud service users is the rising cost of cloud resources. Since cloud computing uses a pay-per-use model, costs can add up quickly, and unexpected expenses can arise from a lack of visibility and control. The cost structure gets even more complicated when working with multi-cloud or hybrid environments. Businesses may spend much of their IT budget on cloud computing, and any savings can improve their competitiveness and financial stability. Hence, an efficient cloud cost management is crucial. To overcome this difficulty, new approaches and tools are being developed to provide greater oversight and command over cloud a graph-based approach for modelling cost elements and cloud resources and a potential way to solve the resulting constraint problem of cost optimisation. In this context, we primarily consider utilisation, cost, performance, and availability. The proposed approach is evaluated on three different user scenarios, and results indicate that it could be effective in cost modelling, cost optimisation, and scalability. This approach will eventually help organisations make informed decisions about cloud resource placement and manage the costs of software applications and data workflows deployed in single, hybrid, or multi-cloud environments.
Place, publisher, year, edition, pages
Springer Nature , 2024. Vol. 13, no 1, article id 147
Keywords [en]
Cloud computing, Cost optimisation, Cost modelling, Graph theory, Resource placement
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-354788DOI: 10.1186/s13677-024-00709-6ISI: 001322682600002Scopus ID: 2-s2.0-85205680039OAI: oai:DiVA.org:kth-354788DiVA, id: diva2:1905662
Note
QC 20241015
2024-10-152024-10-152024-10-15Bibliographically approved