Sebastian Stein

TL;DR: A constant-factor approximation algorithm for the homogeneous case and efficient heuristics for the general case are developed, which show that while the problem is polynomial-time solvable without storage constraints, it is NP-hard even if each edge cloud has unlimited communication or computation resources.

TL;DR: This work designs a novel online auction protocol for plug-in hybrid electric vehicles, wherein vehicle owners use agents to bid for power and also state time windows in which a vehicle is available for charging, and considers two variations: burning at each time step or on-departure.

TL;DR: A novel multi-armed bandit (MAB) model, the bounded MAB is introduced, and an algorithm to solve it efficiently, called bounded e-first, is developed, which outperforms existing crowdsourcing methods by up to 300%, while achieving up to 95% of a hypothetical optimum with full information.

TL;DR: A novel multi-armed bandit (MAB) model, the bounded MAB, is introduced that naturally captures the problem of expert crowdsourcing and an algorithm to solve it efficiently, called bounded e-first, which uses the first eB of its total budget B to derive estimates of the workers' quality characteristics (exploration), while the remaining eB is used to maximise the total utility based on those estimates (exploitation).

TL;DR: A novel online mechanism that schedules the allocation of an expiring and continuously-produced resource to self-interested agents with private preferences and shows empirically that the average utility achieved by the mechanism is 93% or more of the offline optimal.