scholarly journals A utility optimization approach to network cache design

Author(s):  
Mostafa Dehghan ◽  
Laurent Massoulie ◽  
Don Towsley ◽  
Daniel Menasche ◽  
Y. C. Tay
2019 ◽  
Vol 27 (3) ◽  
pp. 1013-1027 ◽  
Author(s):  
Mostafa Dehghan ◽  
Laurent Massoulie ◽  
Don Towsley ◽  
Daniel Sadoc Menasche ◽  
Y. C. Tay

2011 ◽  
Vol 467-469 ◽  
pp. 69-74
Author(s):  
Hai Feng Li

Existing Web service optimal combination approach is mainly focused on single tasks using “selfish” behavior to pursue optimal solutions. This causes conflicts because many concurrent tasks compete for limited optimal resources, reducing service quality in services. With the best reply function of quantified task conflicts and game theory as bases, this paper establishes a mathematical model depicting the competitive relationship between multitasks and Web service under QoS constraints, and guarantees that every task can obtain optimal utility services considering other task combination strategies. Moreover, an iterative algorithm which reaches the Nash equilibrium is also proposed, and all tasks attain utility optimization under conflicting environments. Experimental analyses show that the approach can considerably enhance the actual utility of all tasks compared with existing Web services combinatorial methods.


2020 ◽  
Vol 180 ◽  
pp. 107379
Author(s):  
Nitish K. Panigrahy ◽  
Jian Li ◽  
Don Towsley ◽  
C.V. Hollot

2020 ◽  
Vol 54 (6) ◽  
pp. 1703-1722 ◽  
Author(s):  
Narges Soltani ◽  
Sebastián Lozano

In this paper, a new interactive multiobjective target setting approach based on lexicographic directional distance function (DDF) method is proposed. Lexicographic DDF computes efficient targets along a specified directional vector. The interactive multiobjective optimization approach consists in several iteration cycles in each of which the Decision Making Unit (DMU) is presented a fixed number of efficient targets computed corresponding to different directional vectors. If the DMU finds one of them promising, the directional vectors tried in the next iteration are generated close to the promising one, thus focusing the exploration of the efficient frontier on the promising area. In any iteration the DMU may choose to finish the exploration of the current region and restart the process to probe a new region. The interactive process ends when the DMU finds its most preferred solution (MPS).


2016 ◽  
Vol 18 (1) ◽  
pp. 114
Author(s):  
She Wei ◽  
Huang Huang ◽  
Guan Chunyun ◽  
Chen Fu ◽  
Chen Guanghui

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