Dynamic Multiresource Fair Allocation With Time Discount Utility

Multiresource allocation mechanisms have been studied in many scenarios. A new dynamic multiresource fair allocation model with time discount utility is proposed in this article, where users can arrive and depart at different time slots. We propose a new any price share time discount (APS-TD) mechanism for this model, which accounts for the users’ time discount utility while maintaining desirable properties. We prove that the APS-TD mechanism satisfies cumulative incentive sharing (CSI), i.e., that the cumulative utility of each user is not lower than the cumulative utility generated by evenly allocating the available resources in each time slot; cumulative strategyproofness (CSP), where users cannot increase their cumulative utility by falsely reporting their demands in any time slot; cumulative Pareto optimality (CPO), i.e., where no allocation can increase the cumulative utility of one user without reducing the cumulative utility of another user in any time slot; cumulative envy-freeness (CEF), where users who arrive later should not prefer allocations from other users who arrive first in any time slot; time discount share fairness (TDSF), where users with higher time discount values occupy larger resource shares in each time slot unless the utility levels of both users are generated by evenly allocating resources; and bottleneck fairness (BF), where the allocation should satisfy max-min fairness with respect to the bottleneck resources contained in each time slot. We run the APS-TD mechanism on Alibaba trace-driven data to demonstrate the performance enhancement achieved by our proposed mechanism over the existing mechanism extensions. The results show that the APS-TD mechanism is superior to hybrid multiresource fairness (H-MRF) and stateful dominant resource fairness (SDRF) in many ways.