AutoScaleSim: A simulation toolkit for auto-scaling Web applications in clouds
2021 (English)In: Simulation (San Diego, Calif.), ISSN 1569-190X, E-ISSN 1878-1462, Vol. 108, article id 102245Article in journal (Refereed) Published
Abstract [en]
Auto-scaling of Web applications is an extensively investigated issue in cloud computing. To evaluate auto-scaling mechanisms, the cloud community is facing considerable challenges on either real cloud platforms or custom test-beds. Challenges include – but not limited to – deployment impediments, the complexity of setting parameters, and most importantly, the cost of hosting and testing Web applications on a massive scale. Hence, simulation is presently one of the most popular evaluation solutions to overcome these obstacles. Existing simulators, however, fail to provide support for hosting, deploying and subsequently auto-scaling of Web applications. In this paper, we introduce AutoScaleSim, which extends the existing CloudSim simulator, to support auto-scaling of Web applications in cloud environments in a customizable, extendable and scalable manner. Using AutoScaleSim, the cloud community can freely implement/evaluate policies for all four phases of auto-scaling mechanisms, that is, Monitoring, Analysis, Planning and Execution. AutoScaleSim can also be used for evaluating load balancing algorithms similarly. We conducted a set of experiments to validate and carefully evaluate the performance of AutoScaleSim in a real cloud platform, with a wide range of performance metrics.
Place, publisher, year, edition, pages
Elsevier, 2021. Vol. 108, article id 102245
Keywords [en]
Auto-scaling, Cloud computing, Elasticity, Resource provisioning, Simulation, Web application, Software engineering, Cloud environments, Cloud platforms, Load balancing algorithms, Performance metrics, Planning and execution, Scaling mechanism, Setting parameters, Simulation toolkits
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-83145DOI: 10.1016/j.simpat.2020.102245ISI: 000619720800002Scopus ID: 2-s2.0-85098704409OAI: oai:DiVA.org:kau-83145DiVA, id: diva2:1530094
2021-02-212021-02-212022-05-05Bibliographically approved