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Every computer system -- from schedulers in clouds (e.g. Amazon) to computernetworks to operating systems -- performs resource allocation across systemusers. The defacto allocation policies are max-min fairness (MMF) for singleresources and dominant resource fairness (DRF) for multiple resources whichguarantee properties like incentive compatibility, envy-freeness, and Paretoefficiency, assuming user demands are static (time-independent). However, inreal-world systems, user demands are dynamic, i.e. time-dependant. As a result,there is now a fundamental mismatch between the goals of computer systems andthe properties enabled by classic resource allocation policies. We aim tobridge this mismatch. When demands are dynamic, instant-by-instant MMF can beextremely unfair over longer periods of time, i.e. lead to unbalanced userallocations as previous allocations have no effect in the present. We considera natural generalization of MMF and DRF for multiple resources and users withdynamic demands: this algorithm ensures that user allocations are as max-minfair as possible up to any time instant, given past allocations. This dynamicmechanism remains Pareto optimal and envy-free, but not incentive compatible.However, our results show that the possible increase in utility by misreportingis bounded and, since this can lead to significant decrease in overall usefulallocation, this suggests that it is not a useful strategy. Our main result isto show that our dynamic DRF algorithm is $(1+\rho)$-incentive compatible,where $\rho$ quantifies the relative importance of a resource for differentusers; we show that this factor is tight even with only two resources. We alsopresent a $3/2$ upper bound and a $\sqrt 2$ lower bound for incentivecompatibility when there is only one resource. We also offer extensions for thecase when users are weighted to prioritize them differently.

Incentives in Dominant Resource Fair Allocation under Dynamic Demands