Fuzzy Energy Aware Real Time Scheduling Targeting Mono-processor Embedded Architectures

2016 ◽  
pp. 81-91
Author(s):  
Ridha Mehalaine ◽  
Fateh Boutekkouk
Keyword(s):  
Real Time ◽  
Energy Aware ◽  
Real Time Scheduling ◽  
Time Scheduling ◽  
Fuzzy Energy ◽  
Embedded Architectures

Global EDF-Based On-Line Energy-Aware Real-Time Scheduling Algorithm in Multi-Core Systems

2012 ◽  
Vol 23 (4) ◽  
pp. 996-1009
Author(s):  
Dong-Song ZHANG ◽  
Tong WU ◽  
Fang-Yuan CHEN ◽  
Shi-Yao JIN
Keyword(s):  
Real Time ◽  
Scheduling Algorithm ◽  
Energy Aware ◽  
Line Energy ◽  
Real Time Scheduling ◽  
Time Scheduling ◽  
On Line ◽  
Global Edf

scholarly journals Energy-Efficient Task Partitioning for Real-Time Scheduling on Multi-Core Platforms

Computers ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 10
Author(s):  
Manal A. El Sayed ◽  
El Sayed M. Saad ◽  
Rasha F. Aly ◽  
Shahira M. Habashy
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Bin Packing ◽  
Shared Resources ◽  
Energy Aware ◽  
Task Partitioning ◽  
Worst Case ◽  
Real Time Scheduling ◽  
Static Partitioning ◽  
Time Scheduling

Multi-core processors have become widespread computing engines for recent embedded real-time systems. Efficient task partitioning plays a significant role in real-time computing for achieving higher performance alongside sustaining system correctness and predictability and meeting all hard deadlines. This paper deals with the problem of energy-aware static partitioning of periodic, dependent real-time tasks on a homogenous multi-core platform. Concurrent access of the tasks to shared resources by multiple tasks running on different cores induced a higher blocking time, which increases the worst-case execution time (WCET) of tasks and can cause missing the hard deadlines, consequently resulting in system failure. The proposed blocking-aware-based partitioning (BABP) algorithm aims to reduce the overall energy consumption while avoiding deadline violations. Compared to existing partitioning strategies, the proposed technique achieves more energy-saving. A series of experiments test the capabilities of the suggested algorithm compared to popular heuristics partitioning algorithms. A comparison was made between the most used bin-packing algorithms and the proposed algorithm in terms of energy consumption and system schedulability. Experimental results demonstrate that the designed algorithm outperforms the Worst Fit Decreasing (WFD), Best Fit Decreasing (BFD), and Similarity-Based Partitioning (SBP) algorithms of bin-packing algorithms, reduces the energy consumption of the overall system, and improves schedulability.

scholarly journals Fault-Tolerant Energy-Aware Task Scheduling on Multiprocessor System for Fixed-Priority Real-Time Tasks

2019 ◽  
Vol 9 (1) ◽  
pp. 2269-2275 ◽  
Keyword(s):  
Energy Efficiency ◽  
Fault Tolerance ◽  
Real Time ◽  
Task Scheduling ◽  
Fault Tolerant ◽  
Multiprocessor System ◽  
Energy Aware ◽  
Fixed Priority ◽  
Real Time Scheduling ◽  
Time Scheduling

Energy-aware real-time scheduling is gaining attention in recent years owing to environmental concerns and applications in numerous fields. System reliability also gets affected adversely with increasing energy dissipations posing serious challenges before the researchers. Keeping these in view, in recent times researchers have diverted to combining issues of fault-tolerance and energy efficiency. In literature, DVFS and DPM, most commonly used techniques for power management in task scheduling, are often combined with Primary/Backup technique to achieve fault tolerance against transient and permanent faults. Optimal algorithms, Earliest deadline first (EDF) and Rate-Monotonic (RM), meant for scheduling dynamic and fixed priority tasks respectively, have mainly been analyzed using a dual-processor approach for fault-tolerance and energy efficiency. In this paper, to handle higher workload of fixed-priority real-time tasks, energy-aware fault-tolerant scheduling algorithms are proposed for multiprocessor systems with balanced and unbalanced number of main and auxiliary processors. Simulations over extensive task-sets indicate that balanced approach is more energy-efficient than the unbalanced one.

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