scholarly journals Intelligent fitting global real‐time task scheduling strategy for high‐performance multi‐core systems

2021 ◽  
Author(s):  
Junpeng Wu ◽  
Enyuan Zhao ◽  
Sizhao Li ◽  
Yanqiang Wang
Keyword(s):  
Real Time ◽  
Task Scheduling ◽  
High Performance ◽  
Scheduling Strategy ◽  
Time Task

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.

scholarly journals Task Scheduling Based on a Hybrid Heuristic Algorithm for Smart Production Line with Fog Computing

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1023 ◽  
Author(s):  
Juan Wang ◽  
Di Li
Keyword(s):  
Energy Consumption ◽  
Fault Detection ◽  
Real Time ◽  
Task Scheduling ◽  
Fog Computing ◽  
Superior Performance ◽  
Smart Manufacturing ◽  
Hybrid Heuristic ◽  
Scheduling Strategy ◽  
Terminal Equipment

Fog computing provides computation, storage and network services for smart manufacturing. However, in a smart factory, the task requests, terminal devices and fog nodes have very strong heterogeneity, such as the different task characteristics of terminal equipment: fault detection tasks have high real-time demands; production scheduling tasks require a large amount of calculation; inventory management tasks require a vast amount of storage space, and so on. In addition, the fog nodes have different processing abilities, such that strong fog nodes with considerable computing resources can help terminal equipment to complete the complex task processing, such as manufacturing inspection, fault detection, state analysis of devices, and so on. In this setting, a new problem has appeared, that is, determining how to perform task scheduling among the different fog nodes to minimize the delay and energy consumption as well as improve the smart manufacturing performance metrics, such as production efficiency, product quality and equipment utilization rate. Therefore, this paper studies the task scheduling strategy in the fog computing scenario. A task scheduling strategy based on a hybrid heuristic (HH) algorithm is proposed that mainly solves the problem of terminal devices with limited computing resources and high energy consumption and makes the scheme feasible for real-time and efficient processing tasks of terminal devices. Finally, the experimental results show that the proposed strategy achieves superior performance compared to other strategies.

On the competitiveness of on-line real-time task scheduling

2002 ◽  
Author(s):  
S. Baruah ◽  
G. Koren ◽  
D. Mao ◽  
B. Mishra ◽  
A. Raghunathan ◽  
...  
Keyword(s):  
Real Time ◽  
Task Scheduling ◽  
Time Task ◽  
On Line
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