The complexity of Markov decision processes
Mathematics of Operations Research
A Survey of solution techniques for the partially observed Markov decision process
Annals of Operations Research
A survey of algorithmic methods for partially observed Markov decision processes
Annals of Operations Research
Optimal adaptive policies for Markov decision processes
Mathematics of Operations Research
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
On the Effect of Large-Scale Deployment of Parallel Downloading
WIAPP '03 Proceedings of the The Third IEEE Workshop on Internet Applications
Exact and approximate algorithms for partially observable markov decision processes
Exact and approximate algorithms for partially observable markov decision processes
Planning and control in stochastic domains with imperfect information
Planning and control in stochastic domains with imperfect information
Deployable multipath communication scheme with sufficient performance data distribution method
Computer Communications
Guest Editorial: Concurrent multipath transport
Computer Communications
Approximating optimal policies for partially observable stochastic domains
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Effective load for flow-level performance modelling of file transfers in wireless LANs
Computer Communications
A heuristic variable grid solution method for POMDPs
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Region-based approximations for planning in stochastic domains
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Fundamental limitations on increasing data rate in wireless systems
IEEE Communications Magazine
IEEE Communications Magazine
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Contemporary wireless networks are based on a wide range of different technologies providing overlapping coverage. This offers users a seamless integration of connectivity by allowing to switch between networks, and opens up a promising area for boosting the performance of wireless networks. Motivated by this, we consider a networking environment in which users are able to select between the available wireless networks to minimize the mean processing times for file downloads in the presence of background traffic. The information available to the user is only the total number of jobs in each network, rather than the per-network numbers of foreground and background jobs. This leads to a complex partial information decision problem which is the focus of this paper. We develop and evaluate a Bayesian learning algorithm that optimally splits a stream of jobs that minimizes the expected sojourn time. The algorithm learns as the system operates and provides information at each decision and departure epoch. We evaluate the optimality of the partial information algorithm by comparing the performance of the algorithm with the ''ideal'' performance obtained by solving a Markov decision problem with full state information. To this end, we have conducted extensive experiments both numerically and in a simulation testbed with the full wireless protocol stack. The results show that the Bayesian algorithm has close to optimal performance over a wide range of parameter values.