Energy conservation in heterogeneous server clusters

By Taliver Heath, Bruno Diniz, Enrique V. Carrera, Wagner Meira Jr, Ricardo Bianchini. ACM SIGPLAN Symposium on Principles & Practice of Parallel Programming, 2005

350 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/7f15250a90a02785bcadc9f4850e719c1c58299f

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In Computer Networks and Communications

In Information Systems

Citations in the corpus

EnaCloud: An Energy-Saving Application Live Placement Approach for Cloud Computing Environments (2009, background): “Some research work [3][4][5][16][17] implement energyefficiency policy in a front-end load balancer to distribute the requests to backend servers, so as to minimize the energy consumption without violating application's QoS requirements.”
pMapper: Power and Migration Cost Aware Application Placement in Virtualized Systems (2008, background): “Most of the cluster energy management literature addresses the problem of distributing requests in a web server cluster in such a way that the performance goals are met and the energy consumption is minimized [5, 4, 19, 21, 10].”
Energy-Efficient Thermal-Aware Task Scheduling for Homogeneous High-Performance Computing Data Centers: A Cyber-Physical Approach (2008, methodology): “At the data center level, some research has also been conducted to reduce computing power cost [27], [ 28 ].”
Capping the brown energy consumption of Internet services at low cost (2010, background): “The per-request cost, ReqCostyi (t), is defined in Equation 2, where gyi (t) is the average cost of serving a request of type y at center i using only green energy in epoch t, byi (t) is the same cost when only brown energy is used, BEC is the service’s brown energy cap, and marketyi (t) is the…”
Energy-Aware Server Provisioning and Load Dispatching for Connection-Intensive Internet Services (2008, background): “[14] studied Web service on-off strategies in the context of heterogeneous server types, although focusing on only short transactions.”
Power management of online data-intensive services (2011, background): “Many authors have examined cluster-level approaches, such as dynamic resizing, load dispatching, and server/virtual machine consolidation, to achieve energy-proportionality by powering down unused systems (e.g., [8, 16, 27]).”