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This paper considers an opportunistic scheduling problem over a renewalsystem. A controller observes a random event at the beginning of each renewalframe and then chooses an action in response to the event, which affects theduration of the frame, the amount of resources used, and a penalty metric. Thegoal is to make frame-wise decisions so as to minimize the time average penaltysubject to time average resource constraints. This problem has applications totask processing and communication in data networks, as well as to certainclasses of Markov decision problems. We formulate the problem as a dynamicfractional program and propose an adaptive algorithm which uses an empiricalaccumulation as a feedback parameter. A key feature of the proposed algorithmis that it does not require knowledge of the random event statistics andpotentially allows (uncountably) infinite event sets. We prove the algorithmsatisfies all desired constraints and achieves $O(\epsilon)$ near optimalitywith probability 1.

Opportunistic Scheduling over Renewal Systems: An Empirical Method