Decomposition approximation of queueing-network control models with tree structures

1987 ◽  
Vol 8 (1) ◽  
pp. 117-132 ◽  
Author(s):  
Kyung Y. Jo
2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Zhicong Zhang ◽  
Shuai Li ◽  
Xiaohui Yan

We study an online multisource multisink queueing network control problem characterized with self-organizing network structure and self-organizing job routing. We decompose the self-organizing queueing network control problem into a series of interrelated Markov Decision Processes and construct a control decision model for them based on the coupled reinforcement learning (RL) architecture. To maximize the mean time averaged weighted throughput of the jobs through the network, we propose a reinforcement learning algorithm with time averaged reward to deal with the control decision model and obtain a control policy integrating the jobs routing selection strategy and the jobs sequencing strategy. Computational experiments verify the learning ability and the effectiveness of the proposed reinforcement learning algorithm applied in the investigated self-organizing network control problem.


2002 ◽  
Vol 716 ◽  
Author(s):  
C. L. Gan ◽  
C. V. Thompson ◽  
K. L. Pey ◽  
W. K. Choi ◽  
F. Wei ◽  
...  

AbstractElectromigration experiments have been carried out on simple Cu dual-damascene interconnect tree structures consisting of straight via-to-via (or contact-to-contact) lines with an extra via in the middle of the line. As with Al-based interconnects, the reliability of a segment in this tree strongly depends on the stress conditions of the connected segment. Beyond this, there are important differences in the results obtained under similar test conditions for Al-based and Cu-based interconnect trees. These differences are thought to be associated with variations in the architectural schemes of the two metallizations. The absence of a conducting electromigrationresistant overlayer in Cu technology, and the possibility of liner rupture at stressed vias lead to significant differences in tree reliabilities in Cu compared to Al.


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