scholarly journals Schedulability Analysis of Non-preemptive Real-Time Scheduling for Multicore Processors with Shared Caches

2017 ◽  
Author(s):  
Jun Xiao ◽  
Sebastian Altmeyer ◽  
Andy Pimentel
Keyword(s):  
Real Time ◽  
Multicore Processors ◽  
Schedulability Analysis ◽  
Real Time Scheduling ◽  
Shared Caches ◽  
Time Scheduling

Schedulability Analysis for Timed Automata With Tasks

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-26
Author(s):  
Jinghao Sun ◽  
Nan Guan ◽  
Rongxiao Shi ◽  
Guozhen Tan ◽  
Wang Yi
Keyword(s):  
Model Checking ◽  
State Space ◽  
Real Time ◽  
Timed Automata ◽  
Schedulability Analysis ◽  
Scheduling Theory ◽  
Real Time Scheduling ◽  
Analysis Techniques ◽  
Model Complex ◽  
Time Scheduling

Research on modeling and analysis of real-time computing systems has been done in two areas, model checking and real-time scheduling theory. In model checking, an expressive modeling formalism such as timed automata (TA) is used to model complex systems, but the analysis is typically very expensive due to state-space explosion. In real-time scheduling theory, the analysis techniques are highly efficient, but the models are often restrictive. In this paper, we aim to exploit the possibility of applying efficient analysis techniques rooted in real-time scheduling theory to analysis of real-time task systems modeled by timed automata with tasks (TAT). More specifically, we develop efficient techniques to analyze the feasibility of TAT-based task models (i.e., whether all tasks can meet their deadlines on single-processor) using demand bound functions (DBF), a widely used workload abstraction in real-time scheduling theory. Our proposed analysis method has a pseudo-polynomial time complexity if the number of clocks used to model each task is bounded by a constant, which is much lower than the exponential complexity of the traditional model-checking based analysis approach (also assuming the number of clocks is bounded by a constant). We apply dynamic programming techniques to implement the DBF-based analysis framework, and propose state space pruning techniques to accelerate the analysis process. Experimental results show that our DBF-based method can analyze a TAT system with 50 tasks within a few minutes, which significantly outperforms the state-of-the-art TAT-based schedulability analysis tool TIMES.

STEM: A Thermal-Constrained Real-Time Scheduling for 3D Heterogeneous-ISA Multicore Processors

2018 ◽  
Vol 67 (6) ◽  
pp. 874-889 ◽  
Author(s):  
Ting-Hao Tsai ◽  
Ya-Shu Chen ◽  
Xue-Xin He ◽  
Cheng-Yu Li
Keyword(s):  
Real Time ◽  
Multicore Processors ◽  
Real Time Scheduling ◽  
Time Scheduling

scholarly journals Dynamic Priority Real-Time Scheduling on Power Asymmetric Multicore Processors

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1488
Author(s):  
Basharat Mahmood ◽  
Naveed Ahmad ◽  
Majid Iqbal Khan ◽  
Adnan Akhunzada
Keyword(s):  
Real Time ◽  
Power Efficiency ◽  
Multicore Processors ◽  
Real Time Systems ◽  
Real Time Scheduling ◽  
Dynamic Priority ◽  
Time Scheduling ◽  
Asymmetric Multicore Processors ◽  
Asymmetric Multicore ◽  
Time Systems

The use of real-time systems is growing at an increasing rate. This raises the power efficiency as the main challenge for system designers. Power asymmetric multicore processors provide a power-efficient platform for building complex real-time systems. The utilization of this efficient platform can be further enhanced by adopting proficient scheduling policies. Unfortunately, the research on real-time scheduling of power asymmetric multicore processors is in its infancy. In this research, we have addressed this problem and added new results. We have proposed a dynamic-priority semi-partitioned algorithm named: Earliest-Deadline First with C=D Task Splitting (EDFwC=D-TS) for scheduling real-time applications on power asymmetric multicore processors. EDFwC=D-TS outclasses its counterparts in terms of system utilization. The simulation results show that EDFwC=D-TS schedules up to 67% more tasks with heavy workloads. Furthermore, it improves the processor utilization up to 11% and on average uses 14% less cores to schedule the given workload.

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