: - In this paper we present several novel efficient techniques and multidimensional data structures which can improve the decision making process in many domains. We consider onli...
ThispaperpresentsatheoreticalframeworkbasedonBayesian decision theory for analyzing recently reported results on implicit coscheduling of parallel applications on clusters of work...
Partially Observable Markov Decision Processes (POMDPs) provide a rich framework for sequential decision-making under uncertainty in stochastic domains. However, solving a POMDP i...
Obtaining performance models, like Markov chains and queueing networks, for systems of significant complexity and magnitude is a difficult task that is usually tackled using human...
Progressive processing allows a system to satisfy a set of requests under time pressure by limiting the amount of processing allocated to each task based on a predefined hierarchic...