SIGMETRICS '94 Proceedings of the 1994 ACM SIGMETRICS conference on Measurement and modeling of computer systems
The grid: blueprint for a new computing infrastructure
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Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment
Journal of the ACM (JACM)
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IPPS '99/SPDP '99 Proceedings of the 13th International Symposium on Parallel Processing and the 10th Symposium on Parallel and Distributed Processing
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IPPS '95 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Parallel Job Scheduling: Issues and Approaches
IPPS '95 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Theory and Practice in Parallel Job Scheduling
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
Multiple-Queue Backfilling Scheduling with Priorities and Reservations for Parallel Systems
JSSPP '02 Revised Papers from the 8th International Workshop on Job Scheduling Strategies for Parallel Processing
Utilization and Predictability in Scheduling the IBM SP2 with Backfilling
IPPS '98 Proceedings of the 12th. International Parallel Processing Symposium on International Parallel Processing Symposium
Mechanism design for online real-time scheduling
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Distributed computing in practice: the Condor experience: Research Articles
Concurrency and Computation: Practice & Experience - Grid Performance
Bid-Based approach for pricing web service
ODBASE'06/OTM'06 Proceedings of the 2006 Confederated international conference on On the Move to Meaningful Internet Systems: CoopIS, DOA, GADA, and ODBASE - Volume Part I
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We consider a virtual computing environment that provides computational resources on demand to users with multi attribute task descriptions that include a valuation, resource (CPU) needs and a completion deadline. Achieving a high quality of service in this environment depends on finding a balance between processing high priority tasks before their deadlines expire, while maximizing resource utilization. The problem becomes more challenging in an economic setting, where the task valuation is private. We propose a bid-based server that publishes a history of the success rate table (SRT) for processed tasks. Clients use the history to optimize their bid for resources on a (single) multiprocessor server. The server schedules tasks in descending order of their bid- Highest Bid First (HBF) and backfills the schedule with smaller tasks when resources are still available. The scheduler follows a hard deadline model where tasks cannot be processed after their deadline. We propose three variations of the SRT where biding history is publicized at different granularity. Using a simulation based study, we analyze the behavior of clients' bids in respond to the SRT. We compare the best HBF variant with an efficient Earliest Deadline First (EDF) mechanism that charges a fixed price. Our results show that the HBF mechanism is able to exploit price discrimination and therefore complete the execution of more high value jobs under a heavy workload, leading to better weighted throughput. HBF can also maximize server profit and client surplus (the difference between value and the client bid) in different settings. Thus, HBF may yield solutions that benefit both the client and the server.