An Energy-Efficient Task Scheduler for Multi-core Platforms with Per-core DVFS Based on Task Characteristics

Lin, Ching-Chi; Chang, Chao-Jui; Syu, You-Cheng; Wu, Jan‐Jan; Liu, Pangfeng; Cheng, Po-Wen; Hsu, Wei-Feng

doi:10.1109/icpp.2014.47

Cited by 9 publications

(5 citation statements)

References 19 publications

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“…The major scheduling problem which has been addressed is that of assigning a set of tasks to different cores in the system, in order to minimize the total response time of the total set of tasks [16]. To conserve energy in multi-core platforms, Ching-Chi et al in [17] and [18] propose task scheduling algorithms that leverage percore DVFS and achieve a balance between performance and energy consumption. They consider two task execution modes: The batch mode, which runs jobs in batches; and the online mode in which jobs with different time constraints, arrival times, and computation workloads co-exist in the system.…”

Section: Real-time Scheduling Of Multi-core Architecturesmentioning

confidence: 99%

“…These studies are interesting, but the authors did not consider the reconfiguration problem, neither the memory overflow problem. In addition, the authors of the paper [17] and [18] have not studied the rechargeable systems with a well-defined period of recharge. The goal of the current approach that deals with the reconfiguration and scheduling of real-time systems is to construct software tasks that meet their hard deadlines and that guarantee a possible execution until the next recharge.…”

Section: Real-time Scheduling Of Multi-core Architecturesmentioning

confidence: 99%

See 1 more Smart Citation

Real-Time Scheduling of Reconfigurable Battery-Powered Multi-Core Platforms

Gammoudi

Benzina

Khalgui

et al. 2016

2016 IEEE 28th International Conference on Tools With Artificial Intelligence (ICTAI)

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This paper deals with the real-time scheduling in a reconfigurable multi-core platform powered by a rechargeable battery. A reconfiguration scenario is defined as an operation that allows the addition-removal-modification of tasks which may result in timing unfeasibility. Such a system may face several scenarios: i) increased power consumption that, in the worst case, may surpass the available energy budget, ii) increased computing demand, which may lead to the violation of real-time constraints, and iii) increased memory demand, potentially exceeding the provided memory capacity. To prevent these problems during the execution, a new scheduling strategy is necessary. The proposal is based on the assignment of tasks to different processor cores to satisfy these constraints simultaneously after any reconfiguration scenario. The effectiveness and performance of the designed approach are evaluated through simulation studies. An intelligent tool named Reconf-Pack is developed in our research laboratory to support this new proposed approach and to simulate it over randomly generated tasks.

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Section: Real-time Scheduling Of Multi-core Architecturesmentioning

confidence: 99%

Section: Real-time Scheduling Of Multi-core Architecturesmentioning

confidence: 99%

Real-Time Scheduling of Reconfigurable Battery-Powered Multi-Core Platforms

Gammoudi

Benzina

Khalgui

et al. 2016

2016 IEEE 28th International Conference on Tools With Artificial Intelligence (ICTAI)

View full text Add to dashboard Cite

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“…A task scheduling strategy is presented in to tackle the task allocation problems on DVFS‐enabled CPU cores, the execution order of tasks, and the CPU processing frequency of each task. It presents the task scheduling model, the energy consumption model, the CPU frequency model, and a cost function for capture the scheduling scenario.…”

Section: Related Workmentioning

confidence: 99%

Developing power‐aware scheduling mechanisms for computing systems virtualized by Xen

Ren

Zhu

et al. 2016

Concurrency and Computation

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Summary Cloud computing emerges as one of the most important technologies for interconnecting people and building the so‐called Internet of People (IoP). In such a cloud‐based IoP, the virtualization technique provides the key supporting environments for running the IoP jobs such as performing data analysis and mining personal information. Nowadays, energy consumption in such a system is a critical metric to measure the sustainability and eco‐friendliness of the system. This paper develops three power‐aware scheduling strategies in virtualized systems managed by Xen, which is a popular virtualization technique. These three strategies are the Least performance Loss Scheduling strategy, the No performance Loss Scheduling strategy, and the Best Frequency Match scheduling strategy. These power‐aware strategies are developed by identifying the limitation of Xen in scaling the CPU frequency and aim to reduce the energy waste without sacrificing the jobs running performance in the computing systems virtualized by Xen. Least performance Loss Scheduling works by re‐arranging the execution order of the virtual machines (VMs). No performance Loss Scheduling works by setting a proper initial CPU frequency for running the VMs. Best Frequency Match reduces energy waste and performance loss by allowing the VMs to jump the queue so that the VM that is put into execution best matches the current CPU frequency. Scheduling for both single core and multicore processors is considered in this paper. The evaluation experiments have been conducted, and the results show that compared with the original scheduling strategy in Xen, the developed power‐aware scheduling algorithm is able to reduce energy consumption without reducing the performance for the jobs running in Xen. Copyright © 2016 John Wiley & Sons, Ltd.

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“…This method makes the best use of core resources and deploys more subsystems on systems with a fixed number of cores. With consideration for task deadline, Lin [9] builds dynamic energy model and realize the minimum energy consumption of DVFS multicore processors. In addition, Rayo [10] also realizes DVFS with heuristic method.…”

Section: B Dynamic Modelingmentioning

confidence: 99%

Task-Aware Priority Scheduling for Multicore Processors

Shi¹

2016

Proceedings of the 2016 International Conference on Computer Engineering and Information Systems

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Abstract-Task scheduling is a key issue in multicore processors, especially for the multicore processor in parallel framework. However, few state-of-art task scheduling strategies solve these important issues simultaneously: assignment of task to CPU core, task types, task order and resource conflict of task. To the best of our knowledge, the schedule problem is more complicated in the node of parallel framework. To settle this problem, we propose a method of task-aware priority scheduling for multicore processors. First, we introduce our scheduling framework, including four modules: Kernel Scheduler, Resource Management, Synchronization, Node Coordinator. Then follow two key algorithms: Task-to-core assignment algorithm and Resource distribution algorithm. Finally, we show that our method obtains good performance in contrast with Work-stealing algorithm.

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An Energy-Efficient Task Scheduler for Multi-core Platforms with Per-core DVFS Based on Task Characteristics

Cited by 9 publications

References 19 publications

Real-Time Scheduling of Reconfigurable Battery-Powered Multi-Core Platforms

Real-Time Scheduling of Reconfigurable Battery-Powered Multi-Core Platforms

Developing power‐aware scheduling mechanisms for computing systems virtualized by Xen

Task-Aware Priority Scheduling for Multicore Processors

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