Energy-aware real-time scheduling on Heterogeneous Multi-Processor

Wang, Gang; Li, Wenming; Hei, Xiali

doi:10.1109/ciss.2015.7086884

Cited by 7 publications

(6 citation statements)

References 14 publications

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“…For comparison, we consider the normalised total energy with respect to the number of jobs (task instance) which we get by varying the number of jobs in our proposed scheduling scheme and calculating normalised total energy using geometric mean formula (1). The normalised total energy consumptions versus a number of jobs (task instance) for EDF, DM, and OJFPF scheduling are taken from [19]. We plot the comparison result as shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, we compare the energy consumption of our proposed scheduling scheme with other existing scheduling algorithms namely EDF, DM and relatively new scheduling OJFPF as defined in [19]. For comparison, we consider the normalised total energy with respect to the number of jobs (task instance) which we get by varying the number of jobs in our proposed scheduling scheme and calculating normalised total energy using geometric mean formula (1).…”

Section: Resultsmentioning

confidence: 99%

“…It gives better performance than the previous model. Furthermore, the proposed scheduling scheme significantly reduced the total energy consumption of the system with respect to some popular scheduling algorithms namely earliest deadline first (EDF) by 18%, deadline‐monotonic (DM) by 17.5%, and a relatively new scheduling scheme optimal job to a fast processor first (OJFPF) as explained in [19] by 6%. (2) The proposed model finds an optimum state of the system where throughput [i.e. central processing unit (CPU) utilisation] and energy of the system are optimum. (3) A ranking methodology of the system has been established considering energy budgets and corresponding throughputs of the system. (4) The user of the model has the flexibility to choose any of the ranking states depending upon the requirements of the throughput and energy without missing the deadline of the task. (5) To the best of our knowledge, there is no previous research work existing to find an optimum state of the real‐time system using ranking methodology considering energy budgets and corresponding throughputs of the system.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic scheduling of tasks for multi‐core real‐time systems based on optimum energy and throughput

Baital¹,

Chakrabarti

2019

IET Computers & Digital Techniques

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One of the critical design issues in real-time systems is energy consumption, especially in battery-operated systems. Generally higher processor voltage generates higher throughput of the system while decreasing voltage can perform energy minimisation. Instead of lowering processor voltage, this paper presents an optimum energy efficient real-time scheduling to adjust voltage dynamically to achieve optimum throughput. Earlier research works have considered random new tasks, which have been divided into jobs using pfair scheduling to fit into idle times of different cores of the system. In this paper we consider each job has different power levels and execution time at each power level can be found using normalised execution time. Based on the power levels and their corresponding execution time, we find different combinations of energy signature of the system and derive the optimum state of the system using a weighted average of the energy of the system and corresponding throughput. We verify the proposed model using generated task sets and the results show that the model performs excellently in all the cases and significantly reduced the total energy consumption of the system with respect to some popular and relatively new scheduling schemes.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic scheduling of tasks for multi‐core real‐time systems based on optimum energy and throughput

Baital¹,

Chakrabarti

2019

IET Computers & Digital Techniques

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“…There exist many commercially available designs in the embedded and mobile world [1][2][3]. Nevertheless, in this type of architecture it is increasingly more complicated to find the number of cores, operating frequency, and voltage that optimize performance and energy consumption to meet the requirements of a given application [4][5][6]. This complexity increases when application characteristics need to be extracted at runtime to increase performance or save energy at different phases during the execution of an application [7].…”

Section: Introductionmentioning

confidence: 99%

Performance and Energy Trade-Offs for Parallel Applications on Heterogeneous Multi-Processing Systems

et al. 2020

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This work proposes a methodology to find performance and energy trade-offs for parallel applications running on Heterogeneous Multi-Processing systems with a single instruction-set architecture. These offer flexibility in the form of different core types and voltage and frequency pairings, defining a vast design space to explore. Therefore, for a given application, choosing a configuration that optimizes the performance and energy consumption is not straightforward. Our method proposes novel analytical models for performance and power consumption whose parameters can be fitted using only a few strategically sampled offline measurements. These models are then used to estimate an application’s performance and energy consumption for the whole configuration space. In turn, these offline predictions define the choice of estimated Pareto-optimal configurations of the model, which are used to inform the selection of the configuration that the application should be executed on. The methodology was validated on an ODROID-XU3 board for eight programs from the PARSEC Benchmark, Phoronix Test Suite and Rodinia applications. The generated Pareto-optimal configuration space represented a 99% reduction of the universe of all available configurations. Energy savings of up to 59.77%, 61.38% and 17.7% were observed when compared to the performance, ondemand and powersave Linux governors, respectively, with higher or similar performance.

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