Energy-aware mixed partitioning scheduling in standby-sparing systems |
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| Affiliation: | 1. College of Computer Science and Technology, Huaqiao University, XiaMen 361021, China,;2. Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang, 110168, China;1. School of Management Science and Engineering, Dalian University of Technology, Dalian 116023, PR China;2. School of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian 116025, PR China;3. Department of Computer Science, University of Surrey, Guildford, Surrey GU2 7XH, United Kingdom;1. School of Mechanical and Automotive Engineering, Anhui Polytechnic University, Wuhu, China;2. Department of Industrial Engineering and Management, Shanghai Jiao Tong University, Shanghai, China;1. School of Software, Yunnan University, China;2. School of Computer Science and Engineering, Nanyang Technological University, Singapore;3. School of Computer Science and Engineering, University of Electronical Science and Technology of China, China;4. Department of Electrical and Computer Engineering, University of Central Florida, United States |
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| Abstract: | Previous standby-sparing techniques assume that all tasks don't access to shared resources. In addition, primary tasks and backup tasks are allocated to the primary processor and spare processor respectively. Spare processor schedules tasks with maximum processor speed. Unlike previous techniques, we have studied the problem of minimizing energy consumption and preserving the original reliability for dynamic-priority real-time task set with shared resources in a standby-sparing system. We propose a novel energy-aware mixed partitioning scheduling algorithm (EAMPSA). Earliest deadline first/dynamic deadline modification (EDF/DDM) scheduling scheme is used to ensure that the shared resources can be accessed in a mutual exclusive manner. Uniformly speed is used to the primary processor and the spare processor. In addition, we use the mixed mapping partitioning of primary and backup tasks method to map tasks. A novel method of mapping task is proposed i.e. the tasks which need to access to shared resources are mapped into the primary processor and the tasks which have no resource requirements are mapped into the spare processor. Furthermore, DVS and DPM techniques are used for both primary and backup tasks to save energy. The experimental results show that the EAMPSA algorithm consumes average 55.43% less energy than that of the SSPT algorithm. |
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