Worst case bound of an LRF schedule for the mean weighted flow-time problem
SIAM Journal on Computing
Predicate migration: optimizing queries with expensive predicates
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Query optimization over web services
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
Sequencing unreliable jobs on parallel machines
Journal of Scheduling
On the complexity of mapping pipelined filtering services on heterogeneous platforms
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Mapping Filtering Streaming Applications
Algorithmica
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In this paper, we study a scheduling problem with unreliable jobs. Each job is characterized by a success probability and by a reward earned in case of success. In case of failure, the job blocks the machine that is processing it, and the jobs subsequently sequenced on that machine cannot be performed. The objective function is to maximize the expected reward. We address the problem in the case of two parallel machines, and analyze the worst-case performance of a simple list scheduling algorithm. We show that the algorithm provides an approximation ratio of (2+2)/4~0,853, and that the bound is tight. We also provide a complexity result concerning the related Total Weighted Discounted Completion Time Problem on parallel machines.