Rubik: Fast analytical power management for latency-critical systems
Latency-critical workloads (e.g., web search), common in datacenters, require stable tail (e.g., 95 th percentile) latencies of a few milliseconds. Servers running these workloads are kept lightly loaded to meet these stringent latency targets. This low utilization wastes billions of dollars in ener...
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| Vydáno v: | 2015 48th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO) s. 598 - 610 |
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| Hlavní autoři: | , , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
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ACM
01.12.2015
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| ISSN: | 2379-3155 |
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| Abstract | Latency-critical workloads (e.g., web search), common in datacenters, require stable tail (e.g., 95 th percentile) latencies of a few milliseconds. Servers running these workloads are kept lightly loaded to meet these stringent latency targets. This low utilization wastes billions of dollars in energy and equipment annually. Applying dynamic power management to latency-critical workloads is challenging. The fundamental issue is coping with their inherent short-term variability: requests arrive at unpredictable times and have variable lengths. Without knowledge of the future, prior techniques either adapt slowly and conservatively or rely on application-specific heuristics to maintain tail latency. We propose Rubik, a fine-grain DVFS scheme for latency-critical workloads. Rubik copes with variability through a novel, general, and efficient statistical performance model. This model allows Rubik to adjust frequencies at sub-millisecond granularity to save power while meeting the target tail latency. Rubik saves up to 66% of core power, widely outperforms prior techniques, and requires no application-specific tuning. Beyond saving core power, Rubik robustly adapts to sudden changes in load and system performance. We use this capability to design RubikColoc, a co-location scheme that uses Rubik to allow batch and latency-critical work to share hardware resources more aggressively than prior techniques. RubikColoc reduces data-center power by up to 31% while using 41% fewer servers than a datacenter that segregates latency-critical and batch work, and achieves 100% core utilization. |
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| AbstractList | Latency-critical workloads (e.g., web search), common in datacenters, require stable tail (e.g., 95 th percentile) latencies of a few milliseconds. Servers running these workloads are kept lightly loaded to meet these stringent latency targets. This low utilization wastes billions of dollars in energy and equipment annually. Applying dynamic power management to latency-critical workloads is challenging. The fundamental issue is coping with their inherent short-term variability: requests arrive at unpredictable times and have variable lengths. Without knowledge of the future, prior techniques either adapt slowly and conservatively or rely on application-specific heuristics to maintain tail latency. We propose Rubik, a fine-grain DVFS scheme for latency-critical workloads. Rubik copes with variability through a novel, general, and efficient statistical performance model. This model allows Rubik to adjust frequencies at sub-millisecond granularity to save power while meeting the target tail latency. Rubik saves up to 66% of core power, widely outperforms prior techniques, and requires no application-specific tuning. Beyond saving core power, Rubik robustly adapts to sudden changes in load and system performance. We use this capability to design RubikColoc, a co-location scheme that uses Rubik to allow batch and latency-critical work to share hardware resources more aggressively than prior techniques. RubikColoc reduces data-center power by up to 31% while using 41% fewer servers than a datacenter that segregates latency-critical and batch work, and achieves 100% core utilization. |
| Author | Bartolini, Davide B. Kasture, Harshad Sanchez, Daniel Beckmann, Nathan |
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| Snippet | Latency-critical workloads (e.g., web search), common in datacenters, require stable tail (e.g., 95 th percentile) latencies of a few milliseconds. Servers... |
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| SubjectTerms | Adaptation models colocation Delays DVFS interference isolation latency-critical Load modeling power management quality of service Servers tail latency Time factors Uncertainty Voltage control |
| Title | Rubik: Fast analytical power management for latency-critical systems |
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