Power management of online data-intensive services

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 Map...

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Bibliographic Details
Published in:2011 38th Annual International Symposium on Computer Architecture (ISCA) pp. 319 - 330
Main Authors: Meisner, David, Sadler, Christopher M., Barroso, Luiz André, Weber, Wolf-Dietrich, Wenisch, Thomas F.
Format: Conference Proceeding
Language:English
Published: New York, NY, USA ACM 04.06.2011
IEEE
Series:ACM Conferences
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Abstracts
Computer systems organization > Architectures > Distributed architectures > Client-server architectures
Information systems > Data management systems > Middleware for databases > Application servers
Information systems > Data management systems > Middleware for databases > Database web servers
Servers
power management
servers
ISBN:9781450304726, 1450304729
ISSN:1063-6897
Online Access:Get full text
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Summary: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.
ISBN:9781450304726
1450304729
ISSN:1063-6897
DOI:10.1145/2000064.2000103