Journal article Open Access

MAGNETIC: Multi-Agent Machine Learning-Based Approach for Energy Efficient Dynamic Consolidation in Data Centers

Haghshenas, Kosar; Pahlevan, Ali; Zapater Sancho, Marina; Mohammadi, Siamak; Atienza, David

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<identifier identifierType="URL">https://zenodo.org/record/3903316</identifier>
<creators>
<creator>
<creatorName>Haghshenas, Kosar</creatorName>
<givenName>Kosar</givenName>
<familyName>Haghshenas</familyName>
<affiliation>University of Tehran</affiliation>
</creator>
<creator>
<creatorName>Pahlevan, Ali</creatorName>
<givenName>Ali</givenName>
<familyName>Pahlevan</familyName>
<affiliation>EPFL</affiliation>
</creator>
<creator>
<creatorName>Zapater Sancho, Marina</creatorName>
<givenName>Marina</givenName>
<familyName>Zapater Sancho</familyName>
<affiliation>EPFL</affiliation>
</creator>
<creator>
<givenName>Siamak</givenName>
<affiliation>University of Tehran</affiliation>
</creator>
<creator>
<creatorName>Atienza, David</creatorName>
<givenName>David</givenName>
<familyName>Atienza</familyName>
<affiliation>EPFL</affiliation>
</creator>
</creators>
<titles>
<title>MAGNETIC: Multi-Agent Machine Learning-Based Approach for Energy Efficient Dynamic Consolidation in Data Centers</title>
</titles>
<publisher>Zenodo</publisher>
<publicationYear>2019</publicationYear>
<dates>
<date dateType="Issued">2019-05-31</date>
</dates>
<resourceType resourceTypeGeneral="Text">Journal article</resourceType>
<alternateIdentifiers>
<alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3903316</alternateIdentifier>
</alternateIdentifiers>
<relatedIdentifiers>
<relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1109/TSC.2019.2919555</relatedIdentifier>
<relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/deephealth</relatedIdentifier>
</relatedIdentifiers>
<rightsList>
<rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
</rightsList>
<descriptions>
<description descriptionType="Abstract">&lt;p&gt;Improving the energy efficiency of data centers while guaranteeing Quality of Service (QoS), together with detecting performance variability of servers caused by either hardware or software failures, are two of the major challenges for efficient resource management of large-scale cloud infrastructures. Previous works in the area of dynamic Virtual Machine (VM) consolidation are mostly focused on addressing the energy challenge, but fall short in proposing comprehensive, scalable, and low-overhead approaches that jointly tackle energy efficiency and performance variability. Moreover, they usually assume over-simplistic power models, and fail to accurately consider all the delay and power costs associated with VM migration and host power mode transition. These assumptions are no longer valid in modern servers executing heterogeneous workloads and lead to unrealistic or inefficient results. In this paper, we propose a centralized-distributed low-overhead failure-aware dynamic VM consolidation strategy to minimize energy consumption in large-scale data centers. Our approach selects the most adequate power mode and frequency of each host during runtime using a distributed multi-agent Machine Learning (ML) based strategy, and migrates the VMs accordingly using a centralized heuristic. Our Multi-AGent machine learNing-based approach for Energy efficienT dynamIc Consolidation (MAGNETIC) is implemented in a modified version of the CloudSim simulator, and considers the energy and delay overheads associated with host power mode transition and VM migration, and is evaluated using power traces collected from various workloads running in real servers and resource utilization logs from cloud data center infrastructures. Results show how our strategy reduces data center energy consumption by up to 15% compared to other works in the state-of-the-art (SoA), guaranteeing the same QoS and reducing the number of VM migrations and host power mode transitions by up to 86% and 90%, respectively. Moreover, it shows better scalability than all other approaches, taking less than 0.7% time overhead to execute for a data center with 1500 VMs. Finally, our solution is capable of detecting host performance variability due to failures, automatically migrating VMs from failing hosts and draining them from workload.&lt;/p&gt;</description>
</descriptions>
<fundingReferences>
<fundingReference>
<funderName>European Commission</funderName>
<funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
<awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/671668/">671668</awardNumber>
<awardTitle>MANGO: exploring Manycore Architectures for Next-GeneratiOn HPC systems</awardTitle>
</fundingReference>
<fundingReference>
<funderName>European Commission</funderName>
<funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
<awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/725657/">725657</awardNumber>
<awardTitle>Computing Server Architecture with Joint Power and Cooling Integration at the Nanoscale</awardTitle>
</fundingReference>
<fundingReference>
<funderName>European Commission</funderName>
<funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
<awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/825111/">825111</awardNumber>
<awardTitle>Deep-Learning and HPC to Boost Biomedical Applications for Health</awardTitle>
</fundingReference>
</fundingReferences>
</resource>

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