Energy-Aware Server Provisioning and Load Dispatching for Connection-Intensive Internet Services

By Gong Chen, Wenbo He, Jie Liu, Suman Nath, Leonidas Rigas, Lin Xiao, Feng Zhao. Symposium on Networked Systems Design and Implementation, 2008

777 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/09ed9cb47e09f56608bdbe6dffaa527c8ca0cd73

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

GreenCloud: a new architecture for green data center (2009, methodology): “The output of the algorithm is an action list in terms of migrate actions (e.g. Migrate VM1 from PM2 to PM4) and local adjustment actions (e.g. Set VM2 CPU to 1500MHz) [40].”
A Survey on Green-Energy-Aware Power Management for Datacenters (2014, background): “(2) Green-energy-aware Virtual efforts consider datacenters in a virtualized environment.”
Ensuring Safety, Security, and Sustainability of Mission-Critical Cyber–Physical Systems (2012, background): “, frequency scaling of CPU according to the offered workload [55]–[57], (ii) server provisioning, adjusting the number of active servers in a servers depending upon load requirements [25], [58]–[62], (iii) thermal aware spatiotemporal job scheduling at both the chip-level and the data center-level [”
Energy-Efficient Cloud Computing (2010, background): “Similar dynamic provisioning algorithms [38] are studied for long-lived TCP connections as in instant messaging (IM) and gaming.”
Workload characterization and prediction in the cloud: A multiple time series approach (2012, background): “The work on capacity management and VM placement typically employs some workload modeling and prediction techniques [14], [15], [16], [17], [18].”
DENS: data center energy-efficient network-aware scheduling (2010, background): “An idle server consumes around two-thirds of its peak-load consumption to keep memory, disks, and I/O resources running [8].”
Cutting the electric bill for internet-scale systems (2009, methodology): “We use price data for 30 locations, covering January 2006 through March 2009.”
A survey on techniques for improving the energy efficiency of large-scale distributed systems (2014, background): “Hence, it is preferable to place the workload in Phoenix and use less cooling capacity than in New Delhi.”
A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments (2014, methodology): “The auto-regression method has also been used for resource or workload forecasting ([49, 50, 60, 71, 73, 94]).”
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…”
Swayam: distributed autoscaling to meet SLAs of machine learning inference services with resource efficiency (2017, background): “Prior autoscaling and prediction solutions for stateless web services [10, 12, 16, 19, 31] typically perform centralized monitoring of”
Minimizing Electricity Cost: Optimization of Distributed Internet Data Centers in a Multi-Electricity-Market Environment (2010, background): “While most of the new technologies have been developed aim to reduce the total power consumptions of IDCs[5], [8], [10], [18], [25], [28], [35], we are lacking a holistic approach to help better reduce the total electricity cost of IDC service providers.”
PRESS: PRedictive Elastic ReSource Scaling for cloud systems (2010, methodology): “[3] used sparse periodic auto-regression to perform load prediction.”