{"id":150654,"date":"2001-12-01T00:00:00","date_gmt":"2001-12-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/operating-systems-techniques-for-reducing-processor-energy-consumption\/"},"modified":"2018-10-16T20:21:23","modified_gmt":"2018-10-17T03:21:23","slug":"operating-systems-techniques-for-reducing-processor-energy-consumption","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/operating-systems-techniques-for-reducing-processor-energy-consumption\/","title":{"rendered":"Operating systems techniques for reducing processor energy consumption"},"content":{"rendered":"
\n

In the last decade, limiting computer energy consumption has become a pervasive goal in computer design, largely due to growing use of portable and embedded computers with limited battery capacities. This work concerns ways to reduce processor energy consumption, since the processor consumes much of a computer\u2019s energy. Our specific contributions are as follows.<\/p>\n

First, we introduce our thesis that operating systems should have a significant role in processor energy management. The operating system knows what threads and applications are running, and can predict their future requirements based on their past usage and their user interaction. We motivate using software to control energy management decisions by describing how software has traditionally been applied to this regime.<\/p>\n

Next, we describe operating system techniques for increasing processor sleep time. We suggest never running blocked processes, and delaying processes that execute without producing output or otherwise signaling useful activity. These techniques reduce CPU energy by 47-66%.<\/p>\n

Next, we address ways to dynamically change a processor\u2019s speed and voltage. We suggest considering what tasks the system is working on and their performance needs, then using a speed schedule that just meets those needs. We show that the optimal schedule increases speed as a task progresses according to a formula dependent on the probability distribution of task CPU requirement. Such a schedule can reduce CPU energy consumption by 20.6% on average, with no effect on performance.<\/p>\n

Next, we analyze real user workloads to evaluate ways to infer task information from observations of user interface events. We find that observable differences in such events have significant effects on CPU usage. Using such information in estimating the probability distribution of task CPU requirements can reduce energy consumption by a further 0.5-1.5%.<\/p>\n

Finally, we implement our methods. We deal with I\/O wait time, overlap of multiple simultaneous tasks, limited speed\/voltage settings, limited timer granularity, and limited ability to modify an operating system. The resulting task-based scheduler implements our energy-saving methods with 1.2% background overhead. We find that our methods will be more effective on future processors capable of a wider range of speeds than modern processors.<\/p>\n<\/div>\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

In the last decade, limiting computer energy consumption has become a pervasive goal in computer design, largely due to growing use of portable and embedded computers with limited battery capacities. This work concerns ways to reduce processor energy consumption, since the processor consumes much of a computer\u2019s energy. Our specific contributions are as follows. First, 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