Online computation and competitive analysis
Online computation and competitive analysis
Managing energy and server resources in hosting centers
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Model-based resource provisioning in a web service utility
USITS'03 Proceedings of the 4th conference on USENIX Symposium on Internet Technologies and Systems - Volume 4
Workload Analysis and Demand Prediction of Enterprise Data Center Applications
IISWC '07 Proceedings of the 2007 IEEE 10th International Symposium on Workload Characterization
Cutting the electric bill for internet-scale systems
Proceedings of the ACM SIGCOMM 2009 conference on Data communication
Distributed dynamic speed scaling
INFOCOM'10 Proceedings of the 29th conference on Information communications
Renewable and cooling aware workload management for sustainable data centers
Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems
Using batteries to reduce the power costs of internet-scale distributed networks
Proceedings of the Third ACM Symposium on Cloud Computing
Online algorithms for geographical load balancing
IGCC '12 Proceedings of the 2012 International Green Computing Conference (IGCC)
Temperature aware workload management in geo-distributed datacenters
Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems
Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems
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The critical need for clean and economical sources of energy is transforming data centers that are primarily energy consumers to also energy producers. We focus on minimizing the operating costs of next-generation data centers that can jointly optimize the energy supply from on-site generators and the power grid, and the energy demand from servers as well as power conditioning and cooling systems. We formulate the cost minimization problem and present an offline optimal algorithm. For "on-grid" data centers that use only the grid, we devise a deterministic online algorithm that achieves the best possible competitive ratio of 2αs, where αs is a normalized look-ahead window size. The competitive ratio of an online algorithm is defined as the maximum ratio (over all possible inputs) between the algorithm's cost (with no or limited look-ahead) and the offline optimal assuming complete future information. We remark that the results hold as long as the overall energy demand (including server, cooling, and power conditioning) is a convex and increasing function in the total number of active servers and also in the total server load. For "hybrid" data centers that have on-site power generation in addition to the grid, we develop an online algorithm that achieves a competitive ratio of at most Pmax(2--αs)/co+cm/L [1+2 Pmax--co/Pmax(1+αg], where αs and αg are normalized look-ahead window sizes, Pmax is the maximum grid power price, and L, co, and cm are parameters of an on-site generator. Using extensive workload traces from Akamai with the corresponding grid power prices, we simulate our offline and online algorithms in a realistic setting. Our offline (resp., online) algorithm achieves a cost reduction of 25.8% (resp., 20.7%) for a hybrid data center and 12.3% (resp., 7.3%) for an on-grid data center. The cost reductions are quite significant and make a strong case for a joint optimization of energy supply and energy demand in a data center. A hybrid data center provides about 13% additional cost reduction over an on-grid data center representing the additional cost benefits that on-site power generation provides over using the grid alone.