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Power management of online data-intensive services

Published: 04 June 2011 Publication History

Abstract

Much of the success of the Internet services model can be attributed to the popularity of a class of workloads that we call Online Data-Intensive (OLDI) services. These workloads perform significant computing over massive data sets per user request but, unlike their offline counterparts (such as MapReduce computations), they require responsiveness in the sub-second time scale at high request rates. Large search products, online advertising, and machine translation are examples of workloads in this class. Although the load in OLDI services can vary widely during the day, their energy consumption sees little variance due to the lack of energy proportionality of the underlying machinery. The scale and latency sensitivity of OLDI workloads also make them a challenging target for power management techniques.
We investigate what, if anything, can be done to make OLDI systems more energy-proportional. Specifically, we evaluate the applicability of active and idle low-power modes to reduce the power consumed by the primary server components (processor, memory, and disk), while maintaining tight response time constraints, particularly on 95th-percentile latency. Using Web search as a representative example of this workload class, we first characterize a production Web search workload at cluster-wide scale. We provide a fine-grain characterization and expose the opportunity for power savings using low-power modes of each primary server component. Second, we develop and validate a performance model to evaluate the impact of processor- and memory-based low-power modes on the search latency distribution and consider the benefit of current and foreseeable low-power modes. Our results highlight the challenges of power management for this class of workloads. In contrast to other server workloads, for which idle low-power modes have shown great promise, for OLDI workloads we find that energy-proportionality with acceptable query latency can only be achieved using coordinated, full-system active low-power modes.

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Published In

cover image ACM Conferences
ISCA '11: Proceedings of the 38th annual international symposium on Computer architecture
June 2011
488 pages
ISBN:9781450304726
DOI:10.1145/2000064
  • cover image ACM SIGARCH Computer Architecture News
    ACM SIGARCH Computer Architecture News  Volume 39, Issue 3
    ISCA '11
    June 2011
    462 pages
    ISSN:0163-5964
    DOI:10.1145/2024723
    Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 04 June 2011

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  1. power management
  2. servers

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  • (2024)Energy consumption estimation and profiling for queries in distributed database systems based on a bottom-up comprehensive energy modelFuture Generation Computer Systems10.1016/j.future.2024.04.059159:C(379-394)Online publication date: 1-Oct-2024
  • (2023)Tail Prediction for Heterogeneous Data Center ClustersProcesses10.3390/pr1102040711:2(407)Online publication date: 30-Jan-2023
  • (2023)DPS: Adaptive Power Management for Overprovisioned SystemsProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3581784.3607091(1-14)Online publication date: 12-Nov-2023
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  • (2021)NMAP: Power Management Based on Network Packet Processing Mode Transition for Latency-Critical WorkloadsMICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture10.1145/3466752.3480098(143-154)Online publication date: 18-Oct-2021
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