Managing energy and server resources in hosting centers

By Jeffrey S. Chase, Darrell C. Anderson, P. N. Thakar, Amin Vahdat, Ronald P. Doyle. Symposium on Operating Systems Principles, 2001

1,510 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/f72883df10708e4672ebf259910644c499ac797f

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Citations in the corpus

SHARP: an architecture for secure resource peering (2003, methodology): “While the literature on economic resource allocation is too large to survey here, research systems using economic models include Amoeba [40], Spawn [45], Muse [13], Millennium [15], and many systems based on lottery tickets and currencies [46].”
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.”
Automated control of multiple virtualized resources (2009, background): “Resource provisioning for large clusters hosting multiple services was modeled as a “bidding” process in order to save energy in [8].”
Energy-Aware Server Provisioning and Load Dispatching for Connection-Intensive Internet Services (2008, background): “Similar patterns were found in other studies [3, 20].”
Power provisioning for a warehouse-sized computer (2007, background): “[6] treat energy as a resource to be scheduled by a hosting center’s management infrastructure, and propose a scheme that can reduce energy us-”
DRPM: dynamic speed control for power management in server class disks (2003, background): “Traditional approaches to disk power management involve completely stopping the disk rotation, which can take a considerable amount of time, making them less useful in cases where idle times between disk requests may not be long enough to outweigh the overheads.”
Capping the brown energy consumption of Internet services at low cost (2010, background): “The base energy cost, BaseCosti(offeredi, t), is defined similarly in Equation 3.”
Pocket: Elastic Ephemeral Storage for Serverless Analytics (2018, background): “Muse takes an economic approach, allocating resources to their most efficient use based on a utility function that estimates the impact of resource allocations on job performance [23].”