scholarly journals Applying MGAP Modeling to the Hard Real-time Task Allocation on Multiple Heterogeneous Processors Problem

2016 ◽  
Vol 80 ◽  
pp. 1135-1146 ◽  
Author(s):  
Eduardo Valentin ◽  
Rosiane de Freitas ◽  
Raimundo Barreto
Keyword(s):  
Real Time ◽  
Task Allocation ◽  
Heterogeneous Processors ◽  
Time Task ◽  
Hard Real Time

Decomposition and Learning for a Hard Real Time Task Allocation Problem

2004 ◽  
pp. 153-167 ◽  
Author(s):  
Hadrien Cambazard ◽  
Pierre-Emmanuel Hladik ◽  
Anne-Marie Déplanche ◽  
Narendra Jussien ◽  
Yvon Trinquet
Keyword(s):  
Real Time ◽  
Task Allocation ◽  
Allocation Problem ◽  
Time Task ◽  
Hard Real Time

scholarly journals A mutual-selecting market-based mechanism for dynamic coalition formation

2018 ◽  
Vol 15 (1) ◽  
pp. 172988141875584 ◽  
Author(s):  
Bing Xie ◽  
Shaofei Chen ◽  
Jing Chen ◽  
LinCheng Shen
Keyword(s):  
Real Time ◽  
Coalition Formation ◽  
Task Allocation ◽  
Completion Rate ◽  
Utilization Rate ◽  
Task Completion ◽  
Distributed Agents ◽  
Time Task ◽  
Comparable Performance ◽  
Negotiation Mechanism

This article presents a novel market-based mechanism for a dynamic coalition formation problem backgrounded under real-time task allocation. Specifically, we first analyze the main factors of the real-time task allocation problem, and formulate the problem based on the coalition game theory. Then, we employ a social network for communication among distributed agents in this problem, and propose a negotiation mechanism for agents forming coalitions on timely emerging tasks. In this mechanism, we utilize an auction algorithm for real-time agent assignment on coalitions, and then design a mutual-selecting method to acquire better performance on agent utilization rate and task completion rate. And finally, our experimental results demonstrate that our market-based mechanism has a comparable performance in task completion rate to a decentralized approach (within 25% better on average) and a centralized dynamic coalition formation method (within 10% less on average performance).

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