We study the fair and truthful allocation of m divisible public items among n agents, each with distinct preferences for the items. To aggregate agents’ preferences fairly, we focus on finding a core solution. For divisible items, a core solution always exists and can be calculated by maximizing the Nash welfare objective. However, such a solution is easily manipulated; agents might have incentives to misreport their preferences. To mitigate this, the current state-of-the-art finds an approximate core solution with high probability while ensuring approximate truthfulness. However, this approach has two main limitations. First, due to several approximations, the approximation error in the core could grow with n, resulting in a non-asymptotic core solution. This limitation is particularly significant as public-good allocation mechanisms are frequently applied in scenarios involving a large number of agents, such as the allocation of public tax funds for municipal projects. Second, implementing the current approach for practical applications proves to be a highly nontrivial task. To address these limitations, we introduce PPGA, a (differentially) Private Public-Good Allocation algorithm, and show that it attains asymptotic truthfulness and finds an asymptotic core solution with high probability. Additionally, to demonstrate the practical applicability of our algorithm, we implement PPGA and empirically study its properties using municipal participatory budgeting data.

We present a processor allocation framework that uses Amdahl's Law to model parallel performance and a market mechanism to allocate cores. First, we propose the Amdahl utility function and demonstrate its accuracy when modeling performance from processor core allocations. Second, we design a market based on Amdahl utility that optimizes users' bids for processors based on workload parallelizability. The framework uses entitlements to guarantee fairness yet outperforms existing proportional share algorithms.

Ensuring fairness in a system with scarce, preferred resources requires time sharing. We consider a heterogeneous system with a few “big” and many “small” processors. We allocate heterogeneous processors using a novel token mechanism, which frames the allocation problem as a repeated game. At each round, users request big processors and spend a token if their request is granted. We analyze the game and optimize users’ strategies to produce an equilibrium. In equilibrium, allocations balance performance and fairness. Our mechanism outperforms classical, fair mechanisms by 1.7×, on average, in performance gains, and is competitive with a performance maximizing mechanism.

Task colocation improves datacenter utilization but introduces resource contention for shared hardware. In this setting, a particular challenge is balancing performance and fairness. We present Cooper, a game-theoretic framework for task colocation that provides fairness while preserving performance. Cooper predicts users' colocation preferences and finds stable matches between them. Its colocations satisfy preferences and encourage strategic users to participate inshared systems. Given Cooper's colocations, users' performance penalties are strongly correlated to their contributions to contention, which is fair according to cooperative game theory. Moreover, its colocations perform within 5% of prior heuristics.

We study a dynamic social choice problem in which an alternative is chosen at each round according to the reported valuations of a set of agents. In the interests of obtaining a solution that is both efficient and fair, we aim to maximize the long-term Nash social welfare, which is the product of all agents' utilities. We present and analyze two greedy algorithms for this problem, including the classic Proportional Fair (PF) algorithm. We analyze several versions of the algorithms and how they relate, and provide an axiomatization of PF. Finally, we evaluate the algorithms on data gathered from a computer systems application.

Computational sprinting is a class of mechanisms that boost performance but dissipate additional power. We describe a sprinting architecture in which many, independent chip multiprocessors share a power supply and sprints are constrained by the chips’ thermal limits and the rack’s power limits. Moreover, we present the computational sprinting game, a multi-agent perspective on managing sprints. Strategic agents decide whether to sprint based on application phases and system conditions. The game produces an equilibrium that improves task throughput for data analytics workloads by 4--6× over prior greedy heuristics and performs within 90% of an upper bound on throughput from a globally optimized policy.

Computational sprinting is a class of mechanisms that boost performance but dissipate additional power. We describe a sprinting architecture in which many, independent chip multiprocessors share a power supply and sprints are constrained by the chips' thermal limits and the rack's power limits. Moreover, we present the computational sprinting game, a multi-agent perspective on managing sprints. Strategic agents decide whether to sprint based on application phases and system conditions. The game produces an equilibrium that improves task throughput for data analytics workloads by 4-6× over prior greedy heuristics and performs within 90% of an upper bound on throughput from a globally optimized policy.

With the democratization of cloud and datacenter computing, users increasingly share large hardware platforms. In this setting, architects encounter two challenges: sharing fairly and sharing multiple resources. Drawing on economic game-theory, we rethink fairness in computer architecture. A fair allocation must provide sharing incentives (SI), envy-freeness (EF), and Pareto efficiency (PE).