An optimal spin-lock priority assignment algorithm for real-time multi-core systems

2017 ◽  
Author(s):  
Sara Afshar ◽  
Moris Behnam ◽  
Reinder J. Bril ◽  
Thomas Nolte
Keyword(s):  
Real Time ◽  
Priority Assignment ◽  
Spin Lock ◽  
Assignment Algorithm

scholarly journals Assigning Priorities for Fixed Priority Preemption Threshold Scheduling

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Saehwa Kim
Keyword(s):  
Real Time ◽  
Empirical Evaluation ◽  
Priority Assignment ◽  
Fixed Priority ◽  
Assignment Algorithm ◽  
Optimal Priority Assignment ◽  
A Performance

Preemption threshold scheduling (PTS) enhances real-time schedulability by controlling preemptiveness of tasks. This benefit of PTS highly depends on a proper algorithm that assigns each task feasible scheduling attributes, which are priority and preemption threshold. Due to the existence of an efficient optimal preemption threshold assignment algorithm that works with fully assigned priority orderings, we need an optimal priority assignment algorithm for PTS. This paper analyzes the inefficiency or nonoptimality of the previously proposed optimal priority assignment algorithms for PTS. We develop theorems for exhaustively but safely pruning infeasible priority orderings while assigning priorities to tasks for PTS. Based on the developed theorems, we correct the previously proposed optimal priority assignment algorithm for PTS. We also propose a performance improved optimal priority assignment algorithm for PTS proving its optimality. The empirical evaluation results clearly show the effectiveness of the proposed algorithm.

scholarly journals A New Method of Priority Assignment for Real-Time Flows in the WirelessHART Network by the TDMA Protocol

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4242 ◽  
Author(s):  
Yulong Wu ◽  
Weizhe Zhang ◽  
Hui He ◽  
Yawei Liu
Keyword(s):  
Real Time ◽  
Network Flows ◽  
Heuristic Algorithms ◽  
Optimal Algorithm ◽  
Time Data ◽  
Priority Assignment ◽  
Calculation Time ◽  
Greedy Strategy ◽  
Assignment Algorithm ◽  
A Priority

WirelessHART is a wireless sensor network that is widely used in real-time demand analyses. A key challenge faced by WirelessHART is to ensure the character of real-time data transmission in the network. Identifying a priority assignment strategy that reduces the delay in flow transmission is crucial in ensuring real-time network performance and the schedulability of real-time network flows. We study the priority assignment of real-time flows in WirelessHART on the basis of the multi-channel time division multiple access (TDMA) protocol to reduce the delay and improve the ratio of scheduled. We provide three kinds of methods: (1) worst fit, (2) best fit, and (3) first fit and choose the most suitable one, namely the worst-fit method for allocating flows to each channel. More importantly, we propose two heuristic algorithms—a priority assignment algorithm based on the greedy strategy for C (WF-C) and a priority assignment algorithm based on the greedy strategy for U(WF-U)—for assigning priorities to the flows in each channel, whose time complexity is O ( m a x ( N ∗ m ∗ l o g ( m ) , ( N − m ) 2 ) ) . We then build a new simulation model to simulate the transmission of real-time flows in WirelessHART. Finally, we compare our two algorithms with WF-D and HLS algorithms in terms of the average value of the total end-to-end delay of flow sets, the ratio of schedulable flow sets, and the calculation time of the schedulability analysis. The optimal algorithm WF-C reduces the delay by up to 44.18 % and increases the schedulability ratio by up to 70.7 % , and it reduces the calculation time compared with the HLS algorithm.

scholarly journals Real-Time Instance Segmentation of Traffic Videos for Embedded Devices

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 275
Author(s):  
Ruben Panero Martinez ◽  
Ionut Schiopu ◽  
Bruno Cornelis ◽  
Adrian Munteanu
Keyword(s):  
Real Time ◽  
Network Architecture ◽  
Training Procedure ◽  
Segmentation Method ◽  
Embedded Devices ◽  
Network Training ◽  
Assignment Algorithm ◽  
Ablation Study ◽  
Reduced Rate ◽  
Instance Segmentation

The paper proposes a novel instance segmentation method for traffic videos devised for deployment on real-time embedded devices. A novel neural network architecture is proposed using a multi-resolution feature extraction backbone and improved network designs for the object detection and instance segmentation branches. A novel post-processing method is introduced to ensure a reduced rate of false detection by evaluating the quality of the output masks. An improved network training procedure is proposed based on a novel label assignment algorithm. An ablation study on speed-vs.-performance trade-off further modifies the two branches and replaces the conventional ResNet-based performance-oriented backbone with a lightweight speed-oriented design. The proposed architectural variations achieve real-time performance when deployed on embedded devices. The experimental results demonstrate that the proposed instance segmentation method for traffic videos outperforms the you only look at coefficients algorithm, the state-of-the-art real-time instance segmentation method. The proposed architecture achieves qualitative results with 31.57 average precision on the COCO dataset, while its speed-oriented variations achieve speeds of up to 66.25 frames per second on the Jetson AGX Xavier module.

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