Mathematical analysis and handling of a general stochastic scheduling problem arising in heterogeneous clouds

2020 ◽  
Vol 147 ◽  
pp. 106631
Author(s):  
Wei Wei ◽  
Xiaohui Gong ◽  
Weidong Yang ◽  
Lijun Sun
2016 ◽  
Vol 25 (7) ◽  
pp. 1194-1202 ◽  
Author(s):  
Harish Guda ◽  
Milind Dawande ◽  
Ganesh Janakiraman ◽  
Kyung Sung Jung

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Julien Petit ◽  
Renaud Lambiotte ◽  
Timoteo Carletti

Abstract Random walks find applications in many areas of science and are the heart of essential network analytic tools. When defined on temporal networks, even basic random walk models may exhibit a rich spectrum of behaviours, due to the co-existence of different timescales in the system. Here, we introduce random walks on general stochastic temporal networks allowing for lasting interactions, with up to three competing timescales. We then compare the mean resting time and stationary state of different models. We also discuss the accuracy of the mathematical analysis depending on the random walk model and the structure of the underlying network, and pay particular attention to the emergence of non-Markovian behaviour, even when all dynamical entities are governed by memoryless distributions.


2010 ◽  
Vol 108-111 ◽  
pp. 519-524
Author(s):  
Lie Ping Zhang ◽  
Yun Sheng Zhang

In order to improve the production of process industry, the ant colony system(ACS) was applied to the production scheduling problem. Based on the analysis of the production scheduling problem for process industry, a production scheduling model was established, whose goal was to obtain the shortest total process time. The search strategy, heuristic information rules, pheromone updating mechanism, process step starting time and detailed algorithm implementation of ACS were discussed. Using a practical production scheduling problem as an example, the established model and designed algorithm were applied to implement the scheduling simulation. The simulation results show that the scheduling model and algorithm are feasible, and have a better scheduling performance than the stochastic scheduling method, and can be applied to solve practical production scheduling problem for process industry.


1995 ◽  
Vol 9 (2) ◽  
pp. 269-284 ◽  
Author(s):  
Ulrich Rieder ◽  
Jürgen Weishaupt

A stochastic scheduling model with linear waiting costs and unknown routing probabilities is considered. Using a Bayesian approach and methods of Bayesian dynamic programming, we investigate the finite-horizon stochastic scheduling problem with incomplete information. In particular, we study an equivalent nonstationary bandit model and show the monotonicity of the total expected reward and of the Gittins index. We derive the monotonicity and well-known structural properties of the (greatest) maximizers, the so-called stay-on-a-winnerproperty and the stopping-property. The monotonicity results are based on a special partial ordering on .


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