Energy-aware adaptation for mobile applications
Proceedings of the seventeenth ACM symposium on Operating systems principles
Low-power task scheduling for multiple devices
CODES '00 Proceedings of the eighth international workshop on Hardware/software codesign
System-level power optimization: techniques and tools
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Hardware and Software Techniques for Controlling DRAM Power Modes
IEEE Transactions on Computers
ECOSystem: managing energy as a first class operating system resource
Proceedings of the 10th international conference on Architectural support for programming languages and operating systems
PowerScope: A Tool for Profiling the Energy Usage of Mobile Applications
WMCSA '99 Proceedings of the Second IEEE Workshop on Mobile Computer Systems and Applications
Energy Is Just Another Resource: Energy Accounting and Energy Pricing in the Nemesis OS
HOTOS '01 Proceedings of the Eighth Workshop on Hot Topics in Operating Systems
Dynamic Power Management Using Data Buffers
Proceedings of the conference on Design, automation and test in Europe - Volume 1
Cooperative I/O: a novel I/O semantics for energy-aware applications
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
Hierarchical power management with application to scheduling
ISLPED '05 Proceedings of the 2005 international symposium on Low power electronics and design
A programming environment with runtime energy characterization for energy-aware applications
ISLPED '07 Proceedings of the 2007 international symposium on Low power electronics and design
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Reducing energy consumption is an important issue in modern computers. Dynamic power management (DPM) has been extensively studied in recent years. One approach for DPM is to adjust workloads, such as clustering or eliminating requests, as a way to trade-off energy consumption and quality of services. Previous studies focus on single processes. However, when multiple concurrently running processes are considered, workload adjustment must be determined based on the interleaving of the processes' requests. When multiple processes share the same hardware component, adjusting one process may not save energy. This paper presents an approach to assign energy responsibility to individual processes based on how they affect power management. The assignment is used to estimate potential energy reduction by adjusting the processes. We use the estimation to guide runtime adaptation of workload behavior. Experiments demonstrate that our approach can save more energy and improve energy efficiency.