On-line scheduling in the presence of overload
SFCS '91 Proceedings of the 32nd annual symposium on Foundations of computer science
On the competitiveness of on-line real-time task scheduling
Real-Time Systems
MOCA: a multiprocessor on-line competitive algorithm for real-time system scheduling
Theoretical Computer Science - Special issue on dependable parallel computing
Dover: An Optimal On-Line Scheduling Algorithm for Overloaded Uniprocessor Real-Time Systems
SIAM Journal on Computing
SODA '94 Proceedings of the fifth 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
On-line Scheduling with Hard Deadlines (Extended Abstract)
WADS '97 Proceedings of the 5th International Workshop on Algorithms and Data Structures
On-Line Deadline Scheduling on Multiple Resources
COCOON '01 Proceedings of the 7th Annual International Conference on Computing and Combinatorics
Online deadline scheduling: multiple machines and randomization
Proceedings of the fifteenth annual ACM symposium on Parallel algorithms and architectures
Patience is a virtue: the effect of slack on competitiveness for admission control
Journal of Scheduling - Special issue: On-line algorithm part I
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We study the nonpreemptive online scheduling of jobs with deadlines and weights. The goal of the scheduling algorithm is to maximize the total weight of jobs completed by their deadlines. As a special case, the weights may be given as the processing times of jobs, where the job instance is said to have uniform value density. Most previous work of nonpreemptively scheduling jobs online concentrates on a single machine and uniform value density. For the single machine, Goldwasser [6] shows a matching upper bound and lower bound of $(2 + \frac{1}{\kappa})$ on the best competitive ratio, where every job can be delayed for at least κ times its processing time before meeting its deadline. This paper is concerned with multiple machines. We provide a $(7 + 3\sqrt{\frac{1}{\kappa}})$-competitive algorithm defined on multiple machines. Also we consider arbitrary value density, where jobs have arbitrary weights. We derive online scheduling algorithms on a single machine as well as on multiple machines.