Energy-Aware Real-Time Task Scheduling on Local/Shared Memory Systems

2016 ◽  
Author(s):  
Chenchen Fu ◽  
Gruia Calinescu ◽  
Kai Wang ◽  
Minming Li ◽  
Chun Jason Xue
Keyword(s):  
Real Time ◽  
Shared Memory ◽  
Task Scheduling ◽  
Memory Systems ◽  
Energy Aware ◽  
Time Task

scholarly journals Energy-Aware Real-Time Task Scheduling for Heterogeneous Multiprocessors with Particle Swarm Optimization Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Weizhe Zhang ◽  
Hucheng Xie ◽  
Boran Cao ◽  
Albert M. K. Cheng
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Real Time ◽  
Task Scheduling ◽  
Particle Swarm ◽  
Energy Aware ◽  
Swarm Optimization ◽  
Heterogeneous Multiprocessors ◽  
Time Task ◽  
Feasible Solutions

Energy consumption in computer systems has become a more and more important issue. High energy consumption has already damaged the environment to some extent, especially in heterogeneous multiprocessors. In this paper, we first formulate and describe the energy-aware real-time task scheduling problem in heterogeneous multiprocessors. Then we propose a particle swarm optimization (PSO) based algorithm, which can successfully reduce the energy cost and the time for searching feasible solutions. Experimental results show that the PSO-based energy-aware metaheuristic uses 40%–50% less energy than the GA-based and SFLA-based algorithms and spends 10% less time than the SFLA-based algorithm in finding the solutions. Besides, it can also find 19% more feasible solutions than the SFLA-based algorithm.

scholarly journals Real-Time Task Scheduling for Energy-Aware Embedded Systems

2005 ◽  
Author(s):  
Vishnu Swaminathan ◽  
Krishnendu Chakrabarty
Keyword(s):  
Embedded Systems ◽  
Real Time ◽  
Task Scheduling ◽  
Energy Aware ◽  
Time Task

Energy-aware whale optimization algorithm for real-time task scheduling in multiprocessor systems

2020 ◽  
Vol 93 ◽  
pp. 106349
Author(s):  
Mohamed Abdel-Basset ◽  
Doaa El-Shahat ◽  
Kalyanmoy Deb ◽  
Mohamed Abouhawwash
Keyword(s):  
Real Time ◽  
Task Scheduling ◽  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Multiprocessor Systems ◽  
Energy Aware ◽  
Time Task ◽  
Whale Optimization

Dynamic harvesting- and energy-aware real-time task scheduling

2020 ◽  
Vol 28 ◽  
pp. 100413
Author(s):  
Mahmoud Hasanloo ◽  
Mehdi Kargahi ◽  
Shahrokh Jalilian
Keyword(s):  
Real Time ◽  
Task Scheduling ◽  
Energy Aware ◽  
Time Task

scholarly journals Real-time task scheduling for energy-aware embedded systems

2001 ◽  
Vol 338 (6) ◽  
pp. 729-750 ◽  
Author(s):  
Vishnu Swaminathan ◽  
Krishnendu Chakrabarty
Keyword(s):  
Embedded Systems ◽  
Real Time ◽  
Task Scheduling ◽  
Energy Aware ◽  
Time Task

scholarly journals Energy-Aware Real-Time Task Scheduling in Multiprocessor Systems Using a Hybrid Genetic Algorithm

Electronics ◽  
2017 ◽  
Vol 6 (2) ◽  
pp. 40 ◽  
Author(s):  
Amjad Mahmood ◽  
Salman Khan ◽  
Fawzi Albalooshi ◽  
Noor Awwad
Keyword(s):  
Genetic Algorithm ◽  
Real Time ◽  
Task Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Multiprocessor Systems ◽  
Energy Aware ◽  
Time Task

scholarly journals Energy-Aware Real-Time Task Scheduling Exploiting Temporal Locality

2010 ◽  
Vol E93-D (5) ◽  
pp. 1147-1153 ◽  
Author(s):  
Yong-Hee KIM ◽  
Myoung-Jo JUNG ◽  
Cheol-Hoon LEE
Keyword(s):  
Real Time ◽  
Task Scheduling ◽  
Energy Aware ◽  
Time Task ◽  
Temporal Locality

EA-MSCA: An effective energy-aware multi-objective modified sine-cosine algorithm for real-time task scheduling in multiprocessor systems: Methods and analysis

2021 ◽  
Vol 173 ◽  
pp. 114699
Author(s):  
Mohamed Abdel-Basset ◽  
Reda Mohamed ◽  
Mohamed Abouhawwash ◽  
Ripon K. Chakrabortty ◽  
Michael J. Ryan
Keyword(s):  
Real Time ◽  
Task Scheduling ◽  
Multiprocessor Systems ◽  
Effective Energy ◽  
Energy Aware ◽  
Multi Objective ◽  
Time Task ◽  
Sine Cosine Algorithm

Prepare: P owe r -Awar e A p proximate Re a l-time Task Scheduling for Ene r gy-Adaptiv e QoS Maximization

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-25
Author(s):  
Shounak Chakraborty ◽  
Sangeet Saha ◽  
Magnus Själander ◽  
Klaus Mcdonald-Maier
Keyword(s):  
Real Time ◽  
Task Scheduling ◽  
Processing Speed ◽  
Empirical Evaluation ◽  
Adaptive Mechanism ◽  
Approximate Computing ◽  
Thermal Safety ◽  
Time Task ◽  
Individual Task ◽  
The Individual

Achieving high result-accuracy in approximate computing (AC) based real-time applications without violating power constraints of the underlying hardware is a challenging problem. Execution of such AC real-time tasks can be divided into the execution of the mandatory part to obtain a result of acceptable quality, followed by a partial/complete execution of the optional part to improve accuracy of the initially obtained result within the given time-limit. However, enhancing result-accuracy at the cost of increased execution length might lead to deadline violations with higher energy usage. We propose Prepare , a novel hybrid offline-online approximate real-time task-scheduling approach, that first schedules AC-based tasks and determines operational processing speeds for each individual task constrained by system-wide power limit, deadline, and task-dependency. At runtime, by employing fine-grained DVFS, the energy-adaptive processing speed governing mechanism of Prepare reduces processing speed during each last level cache miss induced stall and scales up the processing speed once the stall finishes to a higher value than the predetermined one. To ensure on-chip thermal safety, this higher processing speed is maintained only for a short time-span after each stall, however, this reduces execution times of the individual task and generates slacks. Prepare exploits the slacks either to enhance result-accuracy of the tasks, or to improve thermal and energy efficiency of the underlying hardware, or both. With a 70 - 80% workload, Prepare offers 75% result-accuracy with its constrained scheduling, which is enhanced by 5.3% for our benchmark based evaluation of the online energy-adaptive mechanism on a 4-core based homogeneous chip multi-processor, while meeting the deadline constraint. Overall, while maintaining runtime thermal safety, Prepare reduces peak temperature by up to 8.6 °C for our baseline system. Our empirical evaluation shows that constrained scheduling of Prepare outperforms a state-of-the-art scheduling policy, whereas our runtime energy-adaptive mechanism surpasses two current DVFS based thermal management techniques.

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