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MultiScaler: A Multi-Loop Auto-Scaling Approach for Cloud-Based Applications
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0002-3548-2973
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0001-9194-010X
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0002-9446-8143
University of Sydney, AUS.ORCID iD: 0000-0002-7851-9377
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2022 (English)In: IEEE Transactions on Cloud Computing, ISSN 2168-7161, Vol. 10, no 4, p. 2769-2786Article in journal (Refereed) Published
Abstract [en]

Cloud computing offers a wide range of services through a pool of heterogeneous Physical Machines (PMs) hosted on cloud data centers, where each PM can host several Virtual Machines (VMs). Resource sharing among VMs comes with major benefits, but it can create technical challenges that have a detrimental effect on the performance. To ensure a specific service level requested by the cloud-based applications, there is a need for an approach to assign adequate resources to each VM. To this end, we present our novel Multi-Loop Control approach, called MultiScaler , to allocate resources to VMs based on the Service Level Agreement (SLA) requirements and the run-time conditions. MultiScaler is mainly composed of three different levels working closely with each other to achieve an optimal resource allocation. We propose a set of tailor-made controllers to monitor VMs and take actions accordingly to regulate contention among collocated VMs, to reallocate resources if required, and to migrate VMs from one PM to another. The evaluation in a VMware cluster have shown that the MultiScaler approach can meet applications performance goals and guarantee the SLA by assigning the exact resources that the applications require. Compared with sophisticated baselines, MultiScaler produces significantly better reaction to changes in workloads even under the presence of noisy neighbors.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022. Vol. 10, no 4, p. 2769-2786
Keywords [en]
Auto-Scaling, Cloud computing, Control systems, Control theory, Data centers, Horizontal Scaling, Monitoring, Resource Allocation, Resource management, Service Level Agreement, Vertical Scaling, Computer programming, Computer science, Cloud data centers, Cloud-based applications, Multi-loop control, Optimal resource allocation, Reallocate resources, Resource sharing, Service Level Agreements, Technical challenges
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-83069DOI: 10.1109/TCC.2020.3031676ISI: 000894810300039Scopus ID: 2-s2.0-85092909121OAI: oai:DiVA.org:kau-83069DiVA, id: diva2:1530066
Funder
Knowledge FoundationAvailable from: 2021-02-21 Created: 2021-02-21 Last updated: 2024-07-23Bibliographically approved

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Al-Dulaimy, AudayTaheri, JavidKassler, Andreas

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Al-Dulaimy, AudayTaheri, JavidKassler, AndreasHoseinyFarahabady, M. RezaDeng, ShuiguangZomaya, Albert
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IEEE Transactions on Cloud Computing
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