A truthful mechanism for optimally purchasing iaas instances and scheduling parallel jobs in service clouds

Abstract

Recently, more and more users outsource their job executions to service clouds. To reduce the costs and risks, many service providers purchase on-demand instances from IaaS clouds to provide services elastically. For maximizing social welfare, service providers need effective approaches to optimally purchase IaaS instances and schedule parallel jobs which have soft deadline, according to the valuations reported by users. In order to address the challenges such as NP-hardness and possible misreports of users, we design an auction-style randomized mechanism for the instance purchasing as well as job scheduling and pricing problem in service clouds. This mechanism can achieve an approximately optimal social welfare while scheduling jobs in a way without preemption. Many critical properties can be guaranteed simultaneously by our mechanism, including truthfulness in expectation, computational efficiency and individual rationality. Both the theoretical analysis and the extensive simulations based on synthetic data and real-world job traces validate the effectiveness of our mechanism on social welfare maximization.