Adaptive pricing for resource reservations in Shared environments

Abstract

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 arc 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.