Details

Autor(en) / Beteiligte

• Li, Jian
• Shuang, Kai
• Su, Sen
• Huang, Qingjia
• Xu, Peng
• Cheng, Xiang
• Wang, Jie

Titel

Reducing Operational Costs through Consolidation with Resource Prediction in the Cloud

Ist Teil von

• 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012), 2012, p.793-798

Ort / Verlag

Washington, DC, USA: IEEE Computer Society

Erscheinungsjahr

2012

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE Xplore / Electronic Library Online (IEL)

Beschreibungen/Notizen

• How to achieve energy efficiency to run a cloud data center is a major challenge in the era of rising electricity cost and environmental protection. Various techniques have been devised to help reduce energy consumption for cloud data centers that consist of a large number of identical servers, including dynamic allocation of active servers, consolidating diverse applications to run on them, and adjusting the CPU speed of an active server. Leveraging these techniques, we use an Online Coloring Bin Packing problem to model the consolidation problem and devise an effective application-aware approximation algorithm to find a near-optimal solution. We show a 1.7 asymptotic approximation ratio. We then apply a Predictive Bayesian Network model to identify daily workload patterns and adjust resource provisioning accordingly. We evaluate our approaches using traces collected from a real data center and demonstrate that (1) our prediction algorithm is effective in estimating future demands, (2) our coordinated approaches can provide significant savings of energy and operational costs close to the near-optimal offline solution, and (3) our approaches incur little reliability costs in term of wear-and-tear of server components.