Real-Time Task Assignment in Heterogeneous Distributed Systems with Rechargeable Batteries

2009 ◽  
Author(s):  
Jian Lin ◽  
A.M.K. Cheng ◽  
R. Kumar
Keyword(s):  
Distributed Systems ◽  
Real Time ◽  
Task Assignment ◽  
Rechargeable Batteries ◽  
Time Task ◽  
Heterogeneous Distributed Systems

scholarly journals A heuristic based real time task assignment algorithm for heterogeneous multiprocessors

2014 ◽  
Vol 11 (3) ◽  
pp. 20130975-20130975 ◽  
Author(s):  
M Poongothai ◽  
A Rajeswari ◽  
V Kanishkan
Keyword(s):  
Real Time ◽  
Task Assignment ◽  
Heterogeneous Multiprocessors ◽  
Time Task ◽  
Assignment Algorithm

scholarly journals Real-Time Task Assignment Approach Leveraging Reinforcement Learning with Evolution Strategies for Long-Term Latency Minimization in Fog Computing

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2830 ◽  
Author(s):  
Long Mai ◽  
Nhu-Ngoc Dao ◽  
Minho Park
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Heterogeneous Computing ◽  
Learning Algorithm ◽  
Fog Computing ◽  
Task Assignment ◽  
Evolution Strategies ◽  
Time Task ◽  
Computing Infrastructure

The emerging fog computing technology is characterized by an ultralow latency response, which benefits a massive number of time-sensitive services and applications in the Internet of things (IoT) era. To this end, the fog computing infrastructure must minimize latencies for both service delivery and execution phases. While the transmission latency significantly depends on external factors (e.g., channel bandwidth, communication resources, and interferences), the computation latency can be considered as an internal issue that the fog computing infrastructure could actively self-handle. From this view point, we propose a reinforcement learning approach that utilizes the evolution strategies for real-time task assignment among fog servers to minimize the total computation latency during a long-term period. Experimental results demonstrate that the proposed approach reduces the latency by approximately 16.1% compared to the existing methods. Additionally, the proposed learning algorithm has low computational complexity and an effectively parallel operation; therefore, it is especially appropriate to be implemented in modern heterogeneous computing platforms.

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