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 bin packing: a survey
Approximation algorithms for NP-hard problems
Efficient on-line call control algorithms
Journal of Algorithms
Cost-based query scrambling for initial delays
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
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
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
Online Weighted Flow Time and Deadline Scheduling
APPROX '01/RANDOM '01 Proceedings of the 4th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems and 5th International Workshop on Randomization and Approximation Techniques in Computer Science: Approximation, Randomization and Combinatorial Optimization
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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 will present optimally competitive deterministic and randomized algorithms for shut-down scheduling. Our deterministic algorithm is parameterized by the number of machines m. Its competitive ratio increases as the number of machines decreases, but it is optimal for any given choice of m. Such family of deterministic algorithm can be translated into a family of randomized algorithms that use progressively less randomization and that are optimal for the given amount of randomization. Hence, we establish a precise trade-off between amount of randomization and competitive ratios. We also give a probabilistic analysis for the cases of uniform and exponential distributions. Finally, we report experimental results from trace-driven simulations.