Dynamic real-time scheduling : problem representation and performance implication

1996 ◽  
Author(s):  
Yacine Atif
2016 ◽  
Vol 4 (1) ◽  
pp. 48-60 ◽  
Author(s):  
Yaroslav Shepilov ◽  
Daria Pavlova ◽  
Daria Kazanskaia

The scheduling is the process of the optimal resource allocation that is widely used both in everyday life and specific domains. In the paper the description of scheduling problem is given. The authors consider traditional methods and tools for solving this problem, then describe the proposed approach based on multi-agent technologies and multithreading application. Nowadays there exist numerous approaches to solving of the scheduling problem. In the most of cases this process has to be supported and managed by the complex tools, sometimes based on mathematical principles. The suggested method of multithreading multi-agent scheduling allows efficient and fast solution of complex problems in real-time featuring rapid dynamic changes and uncertainty that cannot be handled by the other methods and tools.


Author(s):  
Friedrich Eisenbrand ◽  
Karthikeyan Kesavan ◽  
Raju S. Mattikalli ◽  
Martin Niemeier ◽  
Arnold W. Nordsieck ◽  
...  

1978 ◽  
Vol 26 (1) ◽  
pp. 127-140 ◽  
Author(s):  
Sudarshan K. Dhall ◽  
C. L. Liu

2021 ◽  
Vol 12 (1) ◽  
pp. 43-73
Author(s):  
Fateh Boutekkouk

The embedded real-time scheduling problem is qualified as a hard multi-objective optimization problem under constraints since it should compromise between three key conflictual objectives that are tasks deadlines guarantee, energy consumption reduction, and reliability enhancement. On this fact, conventional approaches can easily fail to find a good tradeoff in particular when the design space is too vast. On the other side, bio-inspired meta-heuristics have proved their efficiency even if the design space is very large. In this framework, the authors review the most pertinent works of literature targeting the application of bio-inspired methods to resolve the real-time scheduling problem for embedded systems, notably artificial immune systems, machine learning, cellular automata, evolutionary algorithms, and swarm intelligence. A deep discussion is conducted putting the light on the main challenges of using bio-inspired methods in the context of embedded systems. At the end of this review, the authors highlight some of the future directions.


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