Pricing computer services: queueing effects
Communications of the ACM
Optimal incentive-compatible priority pricing for the M/M/1 queue
Operations Research
Utopia: a load sharing facility for large, heterogeneous distributed computer systems
Software—Practice & Experience
Future Generation Computer Systems - Special issue on metacomputing
IEEE Transactions on Parallel and Distributed Systems
Predicting Queue Times on Space-Sharing Parallel Computers
IPPS '97 Proceedings of the 11th International Symposium on Parallel Processing
GriPhyN and LIGO, Building a Virtual Data Grid for Gravitational Wave Scientists
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
Scheduling with Advanced Reservations
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
The Philosophy of TeraGrid: Building an Open, Extensible, Distributed TeraScale Facility
CCGRID '02 Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid
HPDC '04 Proceedings of the 13th IEEE International Symposium on High Performance Distributed Computing
A Comparison of Two Methods for Building Astronomical Image Mosaics on a Grid
ICPPW '05 Proceedings of the 2005 International Conference on Parallel Processing Workshops
Predicting bounds on queuing delay for batch-scheduled parallel machines
Proceedings of the eleventh ACM SIGPLAN symposium on Principles and practice of parallel programming
Applying Advance Reservation to Increase Predictability of Workflow Execution on the Grid
E-SCIENCE '06 Proceedings of the Second IEEE International Conference on e-Science and Grid Computing
On the impact of reservations from the grid on planning-based resource management
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III
Co-scheduling with user-settable reservations
JSSPP'05 Proceedings of the 11th international conference on Job Scheduling Strategies for Parallel Processing
Feedback-controlled resource sharing for predictable eScience
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
The cost of doing science on the cloud: the Montage example
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Workflows and e-Science: An overview of workflow system features and capabilities
Future Generation Computer Systems
Evaluating the cost-benefit of using cloud computing to extend the capacity of clusters
Proceedings of the 18th ACM international symposium on High performance distributed computing
Self-Tuning Virtual Machines for Predictable eScience
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Distributed systems meet economics: pricing in the cloud
HotCloud'10 Proceedings of the 2nd USENIX conference on Hot topics in cloud computing
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Application scheduling studies on large-scale shared resources have advocated the use of resource provisioning in the form of advance reservations for providing predictable and deterministic quality of service to applications. Resource scheduling studies however have shown the adverse impact of advance reservations in the form of reduced utilization and increased response time of the resources. Thus, resource providers either disallow reservations or impose restrictions such as minimum notice periods and this reduces the effectiveness of reservations as the means of allocating desired resources at a desired time. In this paper, we suggest adaptive pricing as an alternative for allowing reservation of resources. The price charged for allowing a reservation is based directly on the impact that the reservation has on other users sharing the resource. Using trace-based simulations, we show that adaptive pricing allows users to make reservations at the desired time while making it more expensive than best effort service. Thus, users are induced to make the correct choice between reservations and best-effort service based on their real needs. Moreover, this pricing scheme is more cost effective and sensitive to the system load as compared to a flat pricing scheme and encourages load balancing across resources.