2020 · 19 citations · 31 references
Cluster ComputingEngineeringEnergy EfficiencyWorkload CharacteristicsComputer ArchitectureData Center NetworkCloud Resource ManagementDatacenter-scale ComputingGreen Data CenterData ManagementData Center SystemPublic CloudComputer EngineeringData CentersComputer ScienceData Center NetworksMobile ComputingPower ConsumptionData SecurityData Center ManagementEnergy ManagementEdge ComputingCloud ComputingApplication Classes
Data center energy consumption has become an increasingly significant contributor both to greenhouse emissions and costs. To increase utilization of individual hosts and improve efficiency, most modern data centers co-locate workloads belonging to different application classes, some being latency-sensitive (LS) and others best-effort (BE) which are more tolerant to performance variation. It is therefore necessary to design mechanisms that reduce power consumption even in the resulting high-utilization environment, while preserving LS task performance. Moreover, the abundance of different workloads and the security implications of public cloud make mechanisms that rely on extensive knowledge of workload characteristics or on application-exported metrics challenging to deploy.
31
2017 USENIX Annual Technical Conference (USENIX ATC'17)
Do Le Quoc, Martin S. Beck, Pramod Bhatotia et al. · 2017 · 3.1K citations
Large-scale cluster management at Google with Borg
Abhishek Verma, Luis Pedrosa, Madhukar Korupolu et al. · 2015 · 1.3K citations · Full text
Christina Delimitrou, Christos Kozyrakis · 2014 · 692 citations
Fog Networks, Cluster Computing, Provisioning (Technology) +14
Jason Mars, Lingjia Tang, Robert Hundt et al. · 2011 · 598 citations
David Lo, Liqun Cheng, Rama Govindaraju et al. · 2015 · 443 citations · Full text