A probabilistic analysis of the multiknapsack value function
Mathematical Programming: Series A and B
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
Stochastic on-line knapsack problems
Mathematical Programming: Series A and B
Approximation algorithms for scheduling
Approximation algorithms for NP-hard problems
Approximation algorithms for bin packing: a survey
Approximation algorithms for NP-hard problems
Efficient on-line call control algorithms
Journal of Algorithms
Online computation and competitive analysis
Online computation and competitive analysis
Web prefetching between low-bandwidth clients and proxies: potential and performance
SIGMETRICS '99 Proceedings of the 1999 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Average-case analysis of off-line and on-line knapsack problems
Proceedings of the sixth annual ACM-SIAM symposium on Discrete algorithms
SODA '94 Proceedings of the fifth annual ACM-SIAM symposium on Discrete algorithms
Competitive non-preemptive call control
SODA '94 Proceedings of the fifth annual ACM-SIAM symposium on Discrete algorithms
Pushing politely: improving Web responsiveness one packet at a time
ACM SIGMETRICS Performance Evaluation Review
A Polynomial Time Approximation Scheme for the Multiple Knapsack Problem
RANDOM-APPROX '99 Proceedings of the Third International Workshop on Approximation Algorithms for Combinatorial Optimization Problems: Randomization, Approximation, and Combinatorial Algorithms and Techniques
Fault-Tolerant Real-Time Scheduling
ESA '97 Proceedings of the 5th Annual European Symposium on Algorithms
On finding the exact solution of a zero-one knapsack problem
STOC '84 Proceedings of the sixteenth annual ACM symposium on Theory of computing
Journal of Scheduling
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Distributed systems execute background or alternative jobs while waiting for data or requests to arrive from another processor. In those cases, the following shut-down scheduling problem arises: given a set of jobs of known processing time. schedule them on m machines so as to maximize the total weight of jobs completed before an initially unknown deadline. We present optimally competitive deterministic and randomized algorithms for shut-down scheduling. Our deterministic algorithm is parameterized by the number m of machines. Its competitive ratio increases as the number of machines decreases, but it is optimal for any given choice of m. Such a family of deterministic algorithms can be translated into a family of randomized algorithms that use progressively less randomization and that are optimal for any given number of fair coin tosses. Hence, we establish a precise trade-off between the amount of randomization and the best possible competitive ratio.