Abstract:This paper presents MULTIFIT-COM, a static task allocator which could be incorporated into an automated compiler/linker/loader for distributed processing systems. The allocator uses performance information for the processes making up the system in order to determine an appropriate mapping of tasks onto processors. It uses several heuristic extensions of the MULTIFIT bin-packing algorithm to nd an allocation that will o er a high system throughput, taking into account the expected execution and interprocessor c… Show more
“…The execution time of tasks varies from 0.28 to 1728 ms and the data size of messages changes from 1 to 32 Kbytes. The detailed information pertinent to the DSP application can be found in [28], [29]. We conducted two groups of experiments.…”
Section: Evaluation In a Real Applicationmentioning
confidence: 99%
“…The DSP system processes sonar data with five independent threads, each driven by its own sensors [29]. The entire system can be represented as a DAG composed of five sub-DAGs, with a total of 119 tasks.…”
Section: Evaluation In a Real Applicationmentioning
confidence: 99%
“…To validate the results from the synthetic collaborative applications, we evaluate in this experiment the BEATA algorithm by using a real system, that is, a digital signal processing system [28], [29].…”
Section: Evaluation In a Real Applicationmentioning
confidence: 99%
“…All synthetic parallel jobs used from Sections 5.2 to 5.5 were created by TGFF [7], which is a randomized task graph generator. The task graph used in Section 5.6 is based on a digital signal processing system detailed in [29].…”
Abstract-Collaborative applications with energy and low-delay constraints are emerging in various networked embedded systems like wireless sensor networks and multimedia terminals. Conventional energy-aware task allocation schemes developed for collaborative applications only concentrated on energy savings when making allocation decisions. Consequently, the length of the schedules generated by such allocation schemes could be very long, which is unfavorable or, in some situations, even not tolerated. To remedy this problem, we developed a novel task allocation strategy called Balanced Energy-Aware Task Allocation (BEATA) for collaborative applications running on heterogeneous networked embedded systems. The BEATA algorithm aims at blending an energy-delay efficiency scheme with task allocations, thereby making the best trade-offs between energy savings and schedule lengths. Aside from that, we introduced the concept of an energy-adaptive window, which is a critical parameter in the BEATA strategy. By fine-tuning the size of the energy-adaptive window, users can readily customize BEATA to meet their specific energy-delay trade-off needs imposed by applications. Further, we built a mathematical model to approximate the energy consumption caused by both computation and communication activities. Experimental results show that BEATA significantly improves the performance of embedded systems in terms of energy savings and schedule length over existing allocation schemes.
“…The execution time of tasks varies from 0.28 to 1728 ms and the data size of messages changes from 1 to 32 Kbytes. The detailed information pertinent to the DSP application can be found in [28], [29]. We conducted two groups of experiments.…”
Section: Evaluation In a Real Applicationmentioning
confidence: 99%
“…The DSP system processes sonar data with five independent threads, each driven by its own sensors [29]. The entire system can be represented as a DAG composed of five sub-DAGs, with a total of 119 tasks.…”
Section: Evaluation In a Real Applicationmentioning
confidence: 99%
“…To validate the results from the synthetic collaborative applications, we evaluate in this experiment the BEATA algorithm by using a real system, that is, a digital signal processing system [28], [29].…”
Section: Evaluation In a Real Applicationmentioning
confidence: 99%
“…All synthetic parallel jobs used from Sections 5.2 to 5.5 were created by TGFF [7], which is a randomized task graph generator. The task graph used in Section 5.6 is based on a digital signal processing system detailed in [29].…”
Abstract-Collaborative applications with energy and low-delay constraints are emerging in various networked embedded systems like wireless sensor networks and multimedia terminals. Conventional energy-aware task allocation schemes developed for collaborative applications only concentrated on energy savings when making allocation decisions. Consequently, the length of the schedules generated by such allocation schemes could be very long, which is unfavorable or, in some situations, even not tolerated. To remedy this problem, we developed a novel task allocation strategy called Balanced Energy-Aware Task Allocation (BEATA) for collaborative applications running on heterogeneous networked embedded systems. The BEATA algorithm aims at blending an energy-delay efficiency scheme with task allocations, thereby making the best trade-offs between energy savings and schedule lengths. Aside from that, we introduced the concept of an energy-adaptive window, which is a critical parameter in the BEATA strategy. By fine-tuning the size of the energy-adaptive window, users can readily customize BEATA to meet their specific energy-delay trade-off needs imposed by applications. Further, we built a mathematical model to approximate the energy consumption caused by both computation and communication activities. Experimental results show that BEATA significantly improves the performance of embedded systems in terms of energy savings and schedule length over existing allocation schemes.
“…[2][3] [4]). Bin-packing has been applied to pack execution requirements [10], execution and communications requirements [23] and memory; these have been combined in multidimensional bin-packing [8].…”
Section: Application Processes In a Cloudmentioning
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