We introduce resource augmentation as a method for analyzing online scheduling problems. In resource augmentation analysis the on-line scheduler is given more resources, say faster processors or more processors, than the adversary. We apply this analysis to two well-known on-line scheduling problems, the classic uniprocessor CPU scheduling problem 1͉r i , pmtn͉͚ F i , and the best-effort firm real-time scheduling problem 1͉r i , pmtn͉͚ w i (1 Ϫ U i ). It is known that there are no constant competitive nonclairvoyant on-line algorithms for these problems. We show that there are simple on-line scheduling algorithms for these problems that are constant competitive if the online scheduler is equipped with a slightly faster processor than the adversary. Thus, a moderate increase in processor speed effectively gives the on-line scheduler the power of clairvoyance. Furthermore, the on-line scheduler can be constant competitive on all inputs that are not closely correlated with processor speed. We also show that the performance of an on-line scheduler in best-effort real time scheduling can be significantly improved if the system is designed in such a way that the laxity of every job is proportional to its length.

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We consider problems involving how to schedule broadcasts in a pulled-based data-dissemination service, such as the DirecPC system, where data requested by the clients is delivered via broadcast. In particular, we consider the case where all the data items are of equal size and preemption is not allowed. We give an o ine O(1)-speed O(1)-approximation algorithm for the problem of minimizing the average response time. We provide worst-case analysis, under various objective functions, of the online algorithms that have appeared in the literature, namely, Most Requests First, First Come First Served, and Longest Wait First.

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