Energy-Efficient Fault-Tolerant Mapping and Scheduling on Heterogeneous Multiprocessor Real-Time Systems

Huang, Kai; Jiang, Xiaowen; Zhang, Xiaomeng; Yan, Rong; Wang, Ke; Xiong, Dongliang; Yan, Xiao

doi:10.1109/access.2018.2873641

Cited by 31 publications

(15 citation statements)

References 34 publications

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“…(3) task duplication is prohibited; (4) communication links with different startup time and bandwidth; (5) each processor has an independent I/O unit that allows for communication and computation to be performed simultaneously [9], [12].…”

Section: The Modelsmentioning

confidence: 99%

“…The communication between tasks assigned to different processors is performed through message passing over the bus [7]. If the data that a task needs to read is available in the local memory, inter-processor communication will not occur [9]. When T i and T j are scheduled to the same processor, the communication time is zero as the intra-processor communication can be ignored [3], [7], [9].…”

Section: B Application Modelmentioning

confidence: 99%

“…technique, and has been widely used in energy-aware scheduling to make processors energy-efficient [3], [5]- [8]. DVFS reduces energy consumption by scaling down supply voltage/frequency of processors [9]. When a real-time application executes on a heterogeneous multiprocessor system with DVFS technique, it contains three phases, namely, task prioritizing, processor selection and power supplying phases [3], [10].…”

Section: Introductionmentioning

confidence: 99%

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Energy-Aware Task Scheduling on Heterogeneous Computing Systems With Time Constraint

et al. 2020

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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. INDEX TERMS Task scheduling, DVFS, cuckoo search algorithm, heterogeneous multiprocessor system.

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Section: The Modelsmentioning

confidence: 99%

Section: B Application Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Energy-Aware Task Scheduling on Heterogeneous Computing Systems With Time Constraint

et al. 2020

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“…Zong et al proposed two energy aware job scheduling algorithms (EAD and PEBD) based on job duplication strategy for homogeneous clusters [18]. Chen et al established a hyper-heuristic framework and proposed a quantum-inspired high-level learning strategy to solve performance-constrained energy optimization problem [19] On the other hand, there are many studies that focus on optimizing the energy consumption and the above other objectives [12], [15], [20]. Huang et al focus on co-management of system reliability and energy consumption, and propose an energy-efficient fault-tolerant scheduling algorithm [20].…”

Section: Related Workmentioning

confidence: 99%

CPU–GPU Utilization Aware Energy-Efficient Scheduling Algorithm on Heterogeneous Computing Systems

Tang

2020

IEEE Access

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Nowadays, heterogeneous computing systems have proven to be a good solution for processing computation intensive high-performance applications. The main challenges for such large-scale systems are energy consumption, computing node CPU-GPU utilization dynamic variability, and so on. In response to these challenges, this study first provides heterogeneous computing systems architecture and parallel application job model. Then, we build system computing node CPU-GPU utilization model and analyze job execution energy consumption. We also deduce the optimal CPU-GPU utilization and job deadline scheduling constraint. Third, we propose a systems CPU-GPU utilization aware and energy-efficient heuristic greedy strategy (UEJS) to solve this job scheduling problem. To improve the algorithm global optimization ability, we design a hybrid particle swarm optimization algorithm (H-PSO), which incorporates the heuristic greedy strategy into the bio-inspired search optimization technique. The rigorous experimental results clearly demonstrate that our proposed H-PSO outperforms heuristic greedy strategy, Max-EAMin, and Genetic Algorithm in terms of the average energy consumption of jobs and system job rejection rate. In particular, H-PSO is better than UEJS by 36.5%, Max-EAMin by 36.3%, and GA by 46.7% in term of the job average energy consumption for heterogeneous system with high workload.

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“…Year: 2018 Advantage: The use of mechanism of positive feedback helps in performance evaluation. Disadvantage: Using hill climbing method all the tasks may get the common solution and meet at the local minima Simulator used: Cloud Sim Tool kit Kai Huang, Xiaowen Jiang, Xiameng Zhang, Rongjie Yan [22] During the scheduling of tasks the saving of the energy and the reliability of the system are two important features to be maintained. This study is concerned on energy saving and maintaining the reliability of the system.…”

Section: Disadvantagementioning

confidence: 99%

Fault Tolerant Scheduling Methods in Cloud Based 5G Networks

Yashaswi¹,

Sadashiv²

2019

IJITEE

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Cloud computing is one of the emerging technology in the field of business and also in the research field. High performance and high efficiency for 5G systems can be provided by the cloud radio access networks(C-RAN). There is also requirement of higher efficiency and also for the low latency for the 5G mobile networks. Sometimes there is probability of entire system failure due to the fault in some part of the hardware or the software. Hence by developing some of the fault tolerant methods these kind of issues can be avoided. Fault tolerance methods can also increase the use of cloud based services. The main idea of fault tolerant scheduling methods is to involve redundancy , so that all the tasks can be performed even when the system fails. But this may cause overhead for the cloud service providers. Hence various scheduling algorithms can be used to overcome this problem. In this paper, the different kinds of fault tolerant scheduling algorithms are discussed. Hence the suitable methods can be used according to the requirement of the user. This should also lead to increase in the efficiency, performance of the tasks and avoid the system failure in case of minute faults in the system.

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Energy-Efficient Fault-Tolerant Mapping and Scheduling on Heterogeneous Multiprocessor Real-Time Systems

Cited by 31 publications

References 34 publications

Energy-Aware Task Scheduling on Heterogeneous Computing Systems With Time Constraint

Energy-Aware Task Scheduling on Heterogeneous Computing Systems With Time Constraint

CPU–GPU Utilization Aware Energy-Efficient Scheduling Algorithm on Heterogeneous Computing Systems

Fault Tolerant Scheduling Methods in Cloud Based 5G Networks

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