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METSM: Multiobjective energy-efficient task scheduling model for an edge heterogeneous multiprocessor system

FUTURE GENERATION COMPUTER systemS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 152卷 207-223页

作者： Jiang, Qiangqiang Xin, Xu Yao, Libo Chen, Bo Harbin Inst Technol Inst Space Sci & Appl Technol Univ Town ShenzhenTaoyuan St Shenzhen 518055 Guangdong Peoples R China Hong Kong Polytech Univ Fac Business Dept Logist & Maritime Studies Hong Kong 999077 Peoples R China Naval Aviat Univ Inst Informat Fus 188 Erma Rd Yantai 264000 Shandong Peoples R China

ABS T R A C T Along with the growth of computing requirements, edge devices, such as satellites and unmanned aerial vehicles, are equipped with heterogeneous multiprocessor systems to cope with complicated missions, including attitude control, signal processing, and objective detection. Concerning the extremely limited power on these devices, reducing the energy and time overhead of task execution evolves into a crucial challenge. Therefore, a multiobjective energy-efficient task scheduling technique (METSM) is proposed. First, a mathematical model is established for the energy-efficient task scheduling problem on edge heterogeneous multiprocessor systems. In this model, both makespan and total energy consumption are the optimization objectives. The decision variables include the task execution sequence, processor assignment, and dynamic voltage and frequency scaling level for each processor. Second, a problem-specific algorithm, namely, iterated greedy-based multiobjective optimizer (IMO), is proposed. Specifically, destruction-reconstruction and local search are redesigned for optimizing task scheduling and resource allocation. Considering local optima avoidance, a probabilistic mutation operation is developed. In addition, multiobjective-oriented strategies of optimal solution selection and acceptance criteria are adopted to accelerate the convergence. Finally, our proposed IMO is compared with several of the latest algorithms through multiple performance metrics. The experimental results show that IMO can obtain optimal Pareto fronts among several multiobjective methods. Savings of approximately 10% and 12% in time and power consumption, respectively, can be achieved by IMO. Moreover, in comparison with classic list-based heuristics for solving test cases, while IMO maintains a similar makespan, energy is reduced by nearly 90%.

关键词： heterogeneous multiprocessor system Task scheduling Energy efficiency Multiobjective optimization Iterated greedy algorithm Edge device

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TSP: A heterogeneous multiprocessor Supercomputing system Based on i860XP

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Journal of Computer Science & Technology 1994年第3期9卷 285-288页

作者：黄国勇李三立 DepartmentofComputerScienceandTechnology TsinghuaUniversityBeijing100084 DepartmentofComputerScience

Numerous new RISC processors provide support for supercomputing. By using the 'mini-Cray' i860 superscalar processor, an add-on board has been developed to boost the performance of a real time system. A parallel heterogeneous multiprocessor supercomputing system, TSP, is constructed. In this paper, we present the system design consideration and described the architecture of the TSP and its features.

关键词： Parallel processing heterogeneous multiprocessor system supercom-puting

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A novel algorithm for priority-based task scheduling on a multiprocessor heterogeneous system

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MICROPROCESSORS AND MICROsystemS 2022年 95卷

作者： Sahoo, Ronali Madhusmita Padhy, Sasmita Kumari Veer Surendra Sai Univ Technol Dept Comp Applicat Sambalpur 768018 Orissa India

Task scheduling is a major challenging issue in parallel and distributed heterogeneous computing systems. The task scheduling problem in a heterogeneous multiprocessor system typically comprises two phases: assigning priority to tasks and mapping tasks to processors. This work considers the task scheduling problem a multi -objective optimization problem to minimize the makespan and maximize the processor utilization. In this paper, the authors have proposed a novel dynamic task scheduling algorithm that is based on an existing met-aheuristic algorithm called Black Widow Optimization (BWO) and an existing deep learning method called Deep Recurrent Neural Network (DRNN) to achieve the objective. The scheduling algorithm is called DRNN-BWO (DRNN trained by using BWO). Authors have used the Upward Rank technique to prioritize the tasks and the Priority-based Modules Encoding (PME) technique for mapping tasks to processors. The simulation results of the proposed algorithm are compared with some standard algorithms to find out how they fare against each other in reducing the makespan. The proposed algorithm is also compared with some recently developed metaheuristic algorithms based on some real-life applications. All the simulation results show that the proposed algorithm significantly reduces the makespan and increases processor utilization.

关键词： heterogeneous multiprocessor system Task scheduling Deep Recurrent Neural Network Black Widow Optimization Parallel computation

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A hybrid optimization algorithm for energy-aware multi-objective task scheduling in heterogeneous multiprocessor systems

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EVOLUTIONARY INTELLIGENCE 2024年第5-6期17卷 3441-3467页

作者： Sahoo, Ronali Madhusmita Padhy, Sasmita Kumari Veer Surendra Sai Univ Technol Dept Comp Applicat Burla Odisha India

In order to successfully address the multiprocessor work scheduling issues, a novel hybrid approach is presented in this paper. Executing a group of tasks on a small number of processors is the main goal of a task scheduling method. The job scheduling issue is viewed as a multi-objective optimisation issue in this study. The goals of this optimisation challenge are to determine the system's least energy consumption and minimum makespan (the sum of the schedule lengths for each task's execution). In order to lower the processors' energy usage, the Dynamic Voltage Frequency Scaling (DVFS) level has the task scheduling issue enabled. To determine the goals, a hybrid algorithm called Hybrid Grey Wolf Crow Search Optimisation (HGWCSO) is suggested. The Grey Wolf Optimisation (GWO) and Crow Search Optimisation (CSO) algorithms are combined to create the HGWCSO algorithm. The social structure and hunting strategy of wolves serve as the inspiration for the GWO metaheuristic algorithm. The CSO, on the other hand, is a metaheuristic algorithm that was motivated by crows' clever conduct. The proposed hybrid approach is put into practise using tasks that are produced at random, and the results of this comparison are classic methods. The proposed method's simulation results are contrasted with those from five existing widely used methods. The proposed technique has also been contrasted with some newly created metaheuristic algorithms using various real-world data sets. According to the simulation results, HGWCSO outperforms other algorithms in terms of lowering makespan and energy usage. The proposed algorithm has successfully improved the makespan and energy consumption with a maximum of 31.07% and 58.36%, respectively, compared to other algorithms.

关键词： Task scheduling heterogeneous multiprocessor system Energy consumption Makespan Hybrid algorithm

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Energy-aware scheduling for dependent tasks in heterogeneous multiprocessor systems

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JOURNAL OF systemS ARCHITECTURE 2022年 129卷

作者： Chen, Jinchao He, Yu Zhang, Ying Han, Pengcheng Du, Chenglie Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China

heterogeneous multiprocessor platform has been widely adopted as an effective approach to providing strong calculation capability while keeping complexity and energy consumption under control in large-scale systems. Although this platform is able to achieve efficient cost reduction and flexibility enhancement in the design and development process of real-time applications, it brings a serious and complex multi-task scheduling problem, especially for dependent tasks with energy consumption constraints. All tasks should be scheduled according to appropriate strategies such that their dependence requirements and energy consumption limitations would be satisfied even in the worst-case situations. In this work, we focus on the energy-aware scheduling problem of dependent tasks in heterogeneous multiprocessor systems. First, we model the dependent tasks and heterogeneous processors, and formulate the energy-aware scheduling problem as a constrained optimization one with an objective of minimizing the schedule length of tasks. Then, by adopting an efficient task prioritization strategy and a weight-based energy distribution strategy, we propose a list-based energyaware scheduling algorithm to seek an approximate optimal start time and processor allocation for each task, guaranteeing that all tasks would be executed efficiently while meeting the dependence and energy requirements. Experiments with randomly generated tasks are conducted to evaluate the performances of the proposed approach in terms of schedule length, optimal solution ratio, and execution time.

关键词： heterogeneous multiprocessor system Dependent tasks Energy-aware scheduling Schedule length Energy consumption limitation

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An enhanced meta-heuristic algorithm used for energy conscious priority-based task scheduling problems in heterogeneous multiprocessor systems

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SUSTAINABLE COMPUTING-INFORMATICS & systemS 2024年 43卷

作者： Sahoo, Ronali Madhusmita Padhy, Sasmita Kumari Veer Surendra Sai Univ Technol Dept Comp Applicat Sambalpur 768018 Odisha India

The Task Scheduling Problem (TSP) in multiprocessor systems (MPS) is an emerging research area in heterogeneous distributed computing environments. Managing complex tasks and achieving optimal efficiency while scheduling tasks in MPS presents challenges. Although adding more processors to a single system greatly increases its processing power, the energy these processors produce is the main drawback. Here, we employed a multi-objective optimization strategy to reduce the makespan and Energy Consumption (EC). To reduce processor EC, we considered an effective Dynamic Voltage and Frequency Scaling (DVFS) technique. Three distinct energy sources are considered, originating from the processors' communication, idle, and active states. The scheduling sequence of tasks and the assignment of tasks to processors are two vital aspects of TSP. Here, the tasks are arranged based on priority using the Upward Rank technique. To minimize the makespan and EC while allocating tasks to processors, we use the population-based metaheuristic method called Enhanced Honey Badger Optimisation (EHBO). We proposed three improvements to the HBO algorithm in EHBO. Initially, we addressed opposite learning-based population initialization to exclude the least suitable candidates and generate a candidate scheduling population that adheres to task precedence. Subsequently, the levy-flight technique is employed to improve the local and global search and preserve their ideal healthy balance. Finally, using dynamic values rather than constant value improves the ability to obtain maximum food. Several experiments are conducted on random task graphs and real-life data sets. Additionally, the results are compared with other upgraded meta- heuristic algorithms, demonstrating the superiority of EHBO.

关键词： Task Scheduling Problem heterogeneous multiprocessor system Makespan Energy Consumption Honey Badger Optimization

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OPTIMAL-ALGORITHMS FOR SYNTHESIS OF RELIABLE APPLICATION-SPECIFIC heterogeneous multiprocessorS

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IEEE TRANSACTIONS ON RELIABILITY 1995年第4期44卷 603-613页

作者： DASGUPTA, A KARRI, R Dept. Electrical & Computer Engineering University of Massachusetts Amherst MA USA

Fast and optimally-reliable application-specific multiprocessor-synthesis is critical in system-level design, especially in medical, automotive, space, and military applications. Previous work in multiprocessor-synthesis & task-allocation for performance & reliability requires exponential time, and therefore, is useful only for small examples, We present the first deterministic and provably-optimal algorithm (RELSYN-OPT) to synthesize real-time, reliable multiprocessors using a heterogeneous library of N processors and L link types. We prove that for a series-parallel graph with M subtasks and nested-depth d, the worst-case computational complexity of RELSYN-OPT is O(M .(L + N).(d)). For tree-structured task graphs, RELSYN-OPT runs in O(M .(L + N)), and is asymptotically optimum, RELSYN-OPT, because of its speed, applies to static & dynamic task allocation for an ultra-reliable distributed processing environment for which, until now, research has produced only suboptimal heuristic solutions.

关键词： heterogeneous multiprocessor system TASK ALLOCATION

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Energy-Aware Task Scheduling on heterogeneous Computing systems With Time Constraint

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IEEE ACCESS 2020年 8卷 23936-23950页

作者： Deng, Zexi Yan, Zihan Huang, Huimin Shen, Hong Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Peoples R China Univ Adelaide Sch Comp Sci Adelaide SA 5005 Australia

As a technique to help achieve high performance in parallel and distributed heterogeneous computing systems, task scheduling has attracted considerable interest. In this paper, we propose an effective Cuckoo Search algorithm based on Gaussian random walk and Adaptive discovery probability which combined with a cost-to-time ratio Modification strategy (GACSM), to address task scheduling on heterogeneous multiprocessor systems using Dynamic Voltage and Frequency Scaling (DVFS). First, to overcome the shortcomings of poor performance in exploitation of the cuckoo search algorithm, we use chaos variables to initialize populations to maintain the population diversity, a Gaussian random walk strategy to balance the exploration and exploitation capabilities of the algorithm, and an adaptive discovery probability strategy to improve population diversity. Then, we apply the improved Cuckoo Search (CS) algorithm to assign tasks to resources, and a widely used downward rank heuristic strategy to find the corresponding scheduling sequence. Finally, we apply a cost-to-time ratio improvement strategy to further improve the performance of the improved CS algorithm. Extensive experiments are conducted to evaluate the effectiveness and efficiency of our method. The results validate our approach and show its superiority in comparison with the state-of-the-art methods.

关键词： Task scheduling DVFS cuckoo search algorithm heterogeneous multiprocessor system

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Low-energy Scheduling Algorithms for Wearable Fall Pre-impact Detection system

Low-energy Scheduling Algorithms for Wearable Fall Pre-impac...

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12th World Multi-Conference on systemics, Cybernetics and Informatics/14th International Conference on Information systems Analysis and Synthesis

作者： Nyan, M. N. Tay, Francis E. H. Guo, D. Xu, L. Yap, K. L. Goh, L. K. Veeravalli, B. Natl Univ Singapore Dept Mech Engn Singapore 117548 Singapore Inst Bioengn & Nanotechnol Med Div Grp Singapore Singapore Inst Infocomm Res Dept Commun & Syst Singapore Singapore Natl Univ Singapore Dept Elect Engn Singapore Singapore

ISBN: (纸本)9781934272329

In this paper, novel low-energy static and dynamic scheduling algorithms with low computational complexities for heterogeneous multiprocessor systems are proposed. Since battery life of the system plays a critical role in wearable embedded systems, the algorithms are useful for energy consumption reduction in Body Area Network (BAN)-based wearable multiprocessor systems in healthcare applications. Our developed BAN-based fall pre-impact detection system is used in this investigation. Based on simulation results using the algorithms, it is found that the battery life can be extended up to 41.6 percent more of its normal life without the algorithms.

关键词： Energy-aware scheduling embedded systems heterogeneous multiprocessor system

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Optimization of Multi-core Task Scheduling based on Improved Particle Swarm Optimization Algorithm 4

Optimization of Multi-core Task Scheduling based on Improved...

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4th International Conference on Intelligent Information Processing (ICIIP)

作者： Cheng, Xiaohui Chi, Jinqiu Guilin Univ Technol Coll Informat Sci & Engn Guilin 541000 Peoples R China Guangxi Key Lab Embedded Technol & Intelligent Sy Jiangan Rd 12 Guilin 541000 Peoples R China

ISBN: (纸本)9781450361910

Embedded heterogeneous multiprocessor systems have a set of processors with different processing capabilities, and task scheduling becomes a key factor in improving system performance. Aiming at the problem of heterogeneous multiprocessor system task scheduling, this paper proposes an improved particle swarm optimization algorithm. By analyzing the computing power of each processor, a scheduling model for independent tasks of heterogeneous multiprocessor systems is established. In the calculation of the fitness value function, this paper rounds up the position value of the particle, so that the particle swarm optimization algorithm can be better applied to the discrete space. At the same time, the strategy of adjusting the inertia weight is used to improve the global convergence speed, overcome the shortcomings of the search performance degradation of the particle in the late iteration, introduce the shrinkage factor and the function factor, improve the learning rule of the particle, and expand the search space of the particle, increasing the diversity of the particle. Compared with traditional algorithms such as genetic algorithm, annealing algorithm, genetic algorithm, ant colony algorithm, local particle swarm optimization algorithm and global particle swarm optimization, it has better convergence and higher computational efficiency. The simulation results show that the improved algorithm proposed in this paper can obtain better scheduling results in a very short time.

关键词： heterogeneous multiprocessor system Particle Swarm Optimization Inertia Weight

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