Software energy optimization in the cloud
Pages 243 - 249
Abstract
A promising avenue to control energy-related costs in enterprise data centers is to investigate power-aware resource management strategies. Mechanisms to accurately capture energy consumption in data centers include source and machine code instruction analysis, kernel sensors, system call monitors and per-VM metering techniques. Though very accurate, these approaches are highly invasive, requiring modifications to software or hardware, and introduce an observer effect that can adversely impact performance. Perhaps most important, results obtained from these approaches require refinement before they can actually be used for management decisions that must strike a balance between costs, SLOs and SLAs. Using existing instrumentation at a rack's PDU provides sufficient granularity to determine the true energy consumption of servers in a non-intrusive way. We show that by leveraging existing instrumentation at a rack's PDU, profiling the type of resource (e.g., CPU, memory, disk, network) a process is using on a given server is not only possible, but highly accurate despite the anticipated signal noise from other servers on a rack's power circuit. This provides a better foundation and allows us to forecast and manage energy demands in data centers.
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- Software energy optimization in the cloud
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Information
Published In
October 2016
355 pages
- Conference Chair:
- Marcellus Mindel,
- Editor:
- Blake Jones,
- Program Chairs:
- Hausi A. Müller,
- Vio Onut
Publisher
IBM Corp.
United States
Publication History
Published: 31 October 2016
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Overall Acceptance Rate 24 of 90 submissions, 27%
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