Name
Abstract | ||
|---|---|---|
Consolidation of workloads has emerged as a key mechanism to dampen the rapidly growing energy expenditure within enterprise-scale data centers. To gainfully utilize consolidation-based techniques, we must be able to characterize the power consumption of groups of colocated applications. Such characterization is crucial for effective prediction and enforcement of appropriate limits on power consumption-power budgets-within the data center. We identify two kinds of power budgets: 1) an average budget to capture an upper bound on long-term energy consumption within that level and 2) a sustained budget to capture any restrictions on sustained draw of current above a certain threshold. Using a simple measurement infrastructure, we derive power profiles-statistical descriptions of the power consumption of applications. Based on insights gained from detailed profiling of several applications-both individual and consolidated-we develop models for predicting average and sustained power consumption of consolidated applications. We conduct an experimental evaluation of our techniques on a Xen-based server that consolidates applications drawn from a diverse pool. For a variety of consolidation scenarios, we are able to predict average power consumption within five percent error margin and sustained power within 10 percent error margin. Using prediction techniques allows us to ensure safe yet efficient system operation-in a representative case, we are able to improve the number of applications consolidated on a server from two to three (compared to existing baseline techniques) by choosing the appropriate power state that satisfies the power budgets associated with the server. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/TC.2010.91 | IEEE Trans. Computers |
Keywords | Field | DocType |
energy expenditure,power-aware systems,power consumption,consolidated application,computer centres,enterprise-scale data center,server consolidation.,power-aware packing,consolidated environments,percent error margin,power consumption prediction,consolidated environment,workload consolidation,long-term energy consumption,consolidation-based technique,derive power profile,sustained power consumption,average power consumption,xen-based server,energy consumption,reliability,sustained power,consumption-power budget,power budget,appropriate power state,sustained budget,data center,servers,upper bound,predictive models,satisfiability | Profiling (computer programming),Upper and lower bounds,Computer science,Server,Parallel computing,Real-time computing,Enforcement,Consolidation (soil),Energy consumption,Data center,Approximation error | Journal |
Volume | Issue | ISSN |
59 | 12 | 0018-9340 |
Citations | PageRank | References |
13 | 0.95 | 22 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeonghwan Choi | 1 | 289 | 15.16 |
| Sriram Govindan | 2 | 807 | 38.92 |
| Jinkyu Jeong | 3 | 300 | 21.96 |
| Bhuvan Urgaonkar | 4 | 2309 | 158.10 |
| Anand Sivasubramaniam | 5 | 4485 | 291.86 |