scholarly journals MORA: An Energy-Aware Slack Reclamation Scheme for Scheduling Sporadic Real-Time Tasks upon Multiprocessor Platforms

2009 ◽  
Author(s):  
Vincent Nelis ◽  
Joël Goossens
Keyword(s):  
Real Time ◽  
Energy Aware ◽  
Multiprocessor Platforms ◽  
Slack Reclamation

scholarly journals Hybrid Scheduling Scheme for Real Time Systems

2016 ◽  
Vol 15 (6) ◽  
pp. 6838-6849
Author(s):  
Medhat H A Awadalla
Keyword(s):  
Real Time ◽  
Energy Savings ◽  
Hybrid Approach ◽  
Energy Aware ◽  
Task Partitioning ◽  
Priority Assignment ◽  
Power Efficient ◽  
Multiprocessor Platforms ◽  
Static Partitioning ◽  
Time Systems

Systems as asymmetric multiprocessor platforms are considered power-efficient multiprocessor architectures, efficient task partitioning (assignment) and play a crucial role in achieving more energy efficiency at these multiprocessor platforms. This paper addresses the problem of energy-aware static partitioning of periodic real time tasks on heterogeneous multiprocessor platforms. A hybrid approach of Particle Swarm Optimization variant and priority assignment based Min-Min algorithm for task partitioning is proposed. The proposed approach aims to minimize the overall energy consumption, meanwhile avoid deadline violations. An energy-aware cost function is proposed to be considered in the proposed approach. Extensive simulated experiments and comparisons with related approaches are conducted in order to validate the effectiveness of the proposed technique. The achieved results demonstrate that the proposed partitioning scheme significantly outperforms in terms of the number of executed iterations to accomplish a specific task in addition to the energy savings.

scholarly journals Energy-aware Demand Selection and Allocation for Real-time IoT Data Trading

2020 ◽  
Author(s):  
Pooja Gupta ◽  
Volkan Dedeoglu ◽  
Kamran Najeebullah ◽  
Salil S. Kanhere ◽  
Raja Jurdak
Keyword(s):  
Real Time ◽  
Energy Aware ◽  
Demand Selection

scholarly journals DeepFogSim: A Toolbox for Execution and Performance Evaluation of the Inference Phase of Conditional Deep Neural Networks with Early Exits Atop Distributed Fog Platforms

2021 ◽  
Vol 11 (1) ◽  
pp. 377
Author(s):  
Michele Scarpiniti ◽  
Enzo Baccarelli ◽  
Alireza Momenzadeh ◽  
Sima Sarv Ahrabi
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Future Internet ◽  
Operating Conditions ◽  
Graphic User Interface ◽  
Energy Aware ◽  
Internet Applications ◽  
The Real ◽  
Delay Performance ◽  
Hard Constraints

The recent introduction of the so-called Conditional Neural Networks (CDNNs) with multiple early exits, executed atop virtualized multi-tier Fog platforms, makes feasible the real-time and energy-efficient execution of analytics required by future Internet applications. However, until now, toolkits for the evaluation of energy-vs.-delay performance of the inference phase of CDNNs executed on such platforms, have not been available. Motivated by these considerations, in this contribution, we present DeepFogSim. It is a MATLAB-supported software toolbox aiming at testing the performance of virtualized technological platforms for the real-time distributed execution of the inference phase of CDNNs with early exits under IoT realms. The main peculiar features of the proposed DeepFogSim toolbox are that: (i) it allows the joint dynamic energy-aware optimization of the Fog-hosted computing-networking resources under hard constraints on the tolerated inference delays; (ii) it allows the repeatable and customizable simulation of the resulting energy-delay performance of the overall Fog execution platform; (iii) it allows the dynamic tracking of the performed resource allocation under time-varying operating conditions and/or failure events; and (iv) it is equipped with a user-friendly Graphic User Interface (GUI) that supports a number of graphic formats for data rendering. Some numerical results give evidence for about the actual capabilities of the proposed DeepFogSim toolbox.

Energy-Aware Scheduling for Real-Time Systems

2016 ◽  
Vol 15 (1) ◽  
pp. 1-34 ◽  
Author(s):  
Mario Bambagini ◽  
Mauro Marinoni ◽  
Hakan Aydin ◽  
Giorgio Buttazzo
Keyword(s):  
Real Time ◽  
Real Time Systems ◽  
Energy Aware ◽  
Energy Aware Scheduling ◽  
Time Systems

scholarly journals Contention & Energy-Aware Real-Time Task Mapping on NoC Based Heterogeneous MPSoCs

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 75110-75123 ◽  
Author(s):  
Haider Ali ◽  
Umair Ullah Tariq ◽  
Yongjun Zheng ◽  
Xiaojun Zhai ◽  
Lu Liu
Keyword(s):  
Real Time ◽  
Task Mapping ◽  
Energy Aware ◽  
Time Task
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