Amortized efficiency of list update and paging rules
Communications of the ACM
Algorithms for Scheduling Imprecise Computations
Computer - Special issue on real-time systems
On the competitiveness of on-line real-time task scheduling
Real-Time Systems
Dover: An Optimal On-Line Scheduling Algorithm for Overloaded Uniprocessor Real-Time Systems
SIAM Journal on Computing
Optimal time-critical scheduling via resource augmentation (extended abstract)
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Online computation and competitive analysis
Online computation and competitive analysis
Speed is as powerful as clairvoyance
Journal of the ACM (JACM)
Performance guarantee for online deadline scheduling in the presence of overload
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Extra processors versus future information in optimal deadline scheduling
Proceedings of the fourteenth annual ACM symposium on Parallel algorithms and architectures
Preemptive Scheduling in Overloaded Systems
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Probabilistic computations: Toward a unified measure of complexity
SFCS '77 Proceedings of the 18th Annual Symposium on Foundations of Computer Science
Online competitive algorithms for maximizing weighted throughput of unit jobs
Journal of Discrete Algorithms
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This paper considers an online scheduling problem arising from Quality-of-Service (QoS) applications. We are required to schedule a set of jobs, each with release time, deadline, processing time and weight. The objective is to maximize the total value obtained for scheduling the jobs. Unlike the traditional model of this scheduling problem, in our model unfinished jobs also get partial values proportional to their amounts processed. We give a new non-timesharing algorithm GAP for this problem for bounded m, where m is the number of concurrent jobs or the number of weight classes. The competitive ratio is improved from 2 to 1.618 (golden ratio) which is optimal for m = 2, and when applied to cases with m 2 it still gives a competitive ratio better than 2, e.g. 1.755 when m = 3. We also give a new study of the problem in the multiprocessor setting, giving an upper bound of 2 and a lower bound of 1.25 for the competitiveness. Finally, we consider resource augmentation and show that O(log α) speedup or extra processors is sufficient to achieve optimality, where α is the importance ratio. We also give a tradeoff result between competitiveness and the amount of extra resources.