Online Mechanism Design for Scheduling Non-Preemptive Jobs under Uncertain Supply and Demand

ABSTRACT We design new algorithms for the problem of allocating uncertain, flexible, and multi-unit demand online given uncertain supply, in order to maximise social welfare. The algorithms can be seen as extensions of the expectation and consensus algorithms from the domain of online scheduling. The problem is especially relevant to the future smart grid, where uncertain output from renewable generators and conventional supply need to be integrated and matched to flexible, non-preemptive demand. To deal with uncertain supply and demand, the algorithms generate multiple scenarios which can then be solved offline. Furthermore, we use a novel method of reweighting the scenarios based on their likelihood whenever new information about supply becomes available. An additional improvement allows the selection of multiple non-preemptive jobs at the same time. Finally, our main contribution is a novel online mechanism based on these extensions, where it is in the agents' best interest to truthfully reveal their preferences. The experimental evaluation of the extended algorithms and different variants of the mechanism show that both achieve more than 85% of the offline optimal economic efficiency. Importantly, the mechanism yields comparable efficiency, while, in contrast to the algorithms, it allows for strategic agents

Online Revenue Maximization for Server Pricing

Efficient and truthful mechanisms to price resources on remote servers/machines has been the subject of much work in recent years due to the importance of the cloud market. This paper considers revenue maximization in the online stochastic setting with non-preemptive jobs and a unit capacity server. One agent/job arrives at every time step, with parameters drawn from an underlying unknown distribution. We design a posted-price mechanism which can be efficiently computed, and is revenue-optimal in expectation and in retrospect, up to additive error. The prices are posted prior to learning the agent's type, and the computed pricing scheme is deterministic, depending only on the length of the allotted time interval and on the earliest time the server is available. If the distribution of agent's type is only learned from observing the jobs that are executed, we prove that a polynomial number of samples is sufficient to obtain a near-optimal truthful pricing strategy

Online mechanism design for scheduling non-preemptive jobs under uncertain supply and demand

We design new algorithms for the problem of allocating uncertain flexible, and multi-unit demand online given uncertain supply, in order to maximise social welfare. The algorithms can be seen as extensions of the expectation and consensus algorithms from the domain of online scheduling. The problem is especially relevant to the future smart grid, where uncertain output from renewable generators and conventional supply need to be integrated and matched to flexible, non-preemptive demand. To deal with uncertain supply and demand, the algorithms generate multiple scenarios which can then be solved offline. Furthermore, we use a novel method of reweighting the scenarios based on their likelihood whenever new information about supply becomes available. An additional improvement allows the selection of multiple non-preemptive jobs at the same time. Finally, our main contribution is a novel online mechanism based on these extensions, where it is in the agents' best interest to truthfully reveal their preferences. The experimental evaluation of the extended algorithms and different variants of the mechanism show that both achieve more than 85% of the offline optimal economic efficiency. Importantly, the mechanism yields comparable efficiency, while, in contrast to the algorithms, it allows for strategic agents

Integrating Consumer Flexibility in Smart Grid and Mobility Systems - An Online Optimization and Online Mechanism Design Approach

Consumer flexibility may provide an important lever to align supply and demand in service systems. However, harnessing dispersed flexibility endowments in the presence of self-interested agents requires appropriate incentive structures. This thesis quantifies the potential value of consumers\u27 flexibility in smart grid and mobility systems. In order to include incentives, online optimization approaches are augmented with methods from online mechanism design