A large-scale parallel task energy-saving scheduling method based on dvfs technology

Abstract

The invention provides a DVFS (Dynamic Voltage and Frequency Scaling)-based energy-saving dispatching method for large-scale parallel tasks, and belongs to the field of distributed computation. The method comprises the following steps: firstly, task mapping stage: setting the original states of all the processors as the states running at the maximal voltage and the maximal frequency, then obtaining the overall execution time of the dispatching result of the directed acyclic graph for the task mapping stage through computation MHEFT; and secondly, task expansion stage: performing expansion optimization of the execution voltage and frequency for the tasks, and lowering energy consumption and cost without affecting the overall performance. The method provided by the invention can remarkably reduce the energy consumption and cost caused by parallel tasks, without affecting the overall execution time of the large-scale parallel tasks.

Description

technical field [0001] The invention belongs to the field of distributed computing, and specifically relates to a large-scale parallel task energy-saving scheduling method based on DVFS technology, which is used to solve the energy consumption problem of large-scale parallel tasks in environments such as data centers. Background technique [0002] With the rise of cloud computing technology, large-scale distributed processing systems (such as data centers and clusters, etc.) mainstream platform. However, the cloud computing platform also brings the problem of huge energy consumption while providing powerful computing capabilities. According to the EPA (Environment Protection Agency) report, the total energy consumption of US data centers reached 61.4 billion KWh in 2006, which doubled compared to 2000 (please refer to U.S.E.P. Agency, "Report to congress on server and data center energy efficiency public law," 2007.). The rapid growth of energy consumption has become a bot...

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Problems solved by technology

Different from the above two methods, literature 3 (Y. Lee and A. Zomaya, "Energy conscious scheduling for distributed computing systems under different operating conditions," Parallel and Distributed Systems, IEEE Transactions on, no. 99, pp. 1-1, 2011) does not make a strict stage distinction between task mapping and voltage regulation. A comprehensive objective function of task performance and energy consumption is proposed, and the execution position and execution voltage of the task are determined at the same time through the Pareto optimal method. This method has achieved good results in energy consumption, but there are still execution problems The performance cannot be dynamically adjusted and cannot meet the dynamic needs of users

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