Well-behaved Online Load Balancing Against Strategic Jobs
Bo Li, Minming Li, Xiaowei Wu

TL;DR
This paper studies online load balancing with strategic jobs, proposing a truthful mechanism that achieves near well-behaved schedules with competitive ratios of O(log m) and O(1) under certain conditions.
Contribution
It introduces a new truthful online load balancing mechanism that is nearly well-behaved, improving previous bounds and addressing fairness and truthfulness in strategic settings.
Findings
Achieves an O(log m) competitive ratio for general cases.
Improves to O(1) when job sizes are bounded.
Shows certain cases where the mechanism is truthful against selfish machines.
Abstract
In the online load balancing problem on related machines, we have a set of jobs (with different sizes) arriving online, and we need to assign each job to a machine immediately upon its arrival, so as to minimize the makespan, i.e., the maximum completion time. In classic mechanism design problems, we assume that the jobs are controlled by selfish agents, with the sizes being their private information. Each job (agent) aims at minimizing its own cost, which is its completion time plus the payment charged by the mechanism. Truthful mechanisms guaranteeing that every job minimizes its cost by reporting its true size have been well-studied [Aspnes et al. JACM 1997, Feldman et al. EC 2017]. In this paper, we study truthful online load balancing mechanisms that are well-behaved [Epstein et al., MOR 2016]. Well-behavior is important as it guarantees fairness between machines, and implies…
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Taxonomy
TopicsAuction Theory and Applications · Optimization and Search Problems · Blockchain Technology Applications and Security
