Mechanism design for decentralized online machine scheduling

Traditional optimization models assume a central decision maker who optimizes a global system performance measure. However, problem data is often distributed among several agents, and agents take autonomous decisions. This gives incentives for strategic behavior of agents, possibly leading to sub-optimal system performance. Furthermore, in dynamic environments, machines are locally dispersed and administratively independent. Examples are found both in business and engineering applications. We investigate such issues for a parallel machine scheduling model where jobs arrive online over time. Instead of centrally assigning jobs to machines, each machine implements a local sequencing rule and jobs decide for machines themselves. In this context, we introduce the concept of a myopic best response equilibrium, a concept weaker than the classical dominant strategy equilibrium, but appropriate for online problems. Our main result is a polynomial time, online mechanism that |assuming rational behavior of jobs| results in an equilibrium schedule that is 3.281-competitive with respect to the maximal social welfare. This is only lightly worse than state-of-the-art algorithms with central coordination

Games and Mechanism Design in Machine Scheduling – An Introduction

In this paper, we survey different models, techniques, and some recent results to tackle machine scheduling problems within a distributed setting. In traditional optimization, a central authority is asked to solve a (computationally hard) optimization problem. In contrast, in distributed settings there are several agents, possibly equipped with private information that is not publicly known, and these agents need to interact in order to derive a solution to the problem. Usually the agents have their individual preferences, which induces them to behave strategically in order to manipulate the resulting solution. Nevertheless, one is often interested in the global performance of such systems. The analysis of such distributed settings requires techniques from classical Optimization, Game Theory, and Economic Theory. The paper therefore briefly introduces the most important of the underlying concepts, and gives a selection of typical research questions and recent results, focussing on applications to machine scheduling problems. This includes the study of the so-called price of anarchy for settings where the agents do not possess private information, as well as the design and analysis of (truthful) mechanisms in settings where the agents do possess private information.computer science applications;

Decentralization and Mechanism Design for Online Machine Scheduling

We study the online version of the classical parallel machine scheduling problem to minimize the total weighted completion time from a new perspective: We assume a strategic setting, where the data of each job j, namely its release date r(j) , its processing time p(j) and its weight w(j) is only known to the job itself, but not to the system. Furthermore, we assume a decentralized setting, where jobs choose the machine on which they want to be processed themselves. We study this setting from the perspective of algorithmic mechanism design and present a polynomial time decentralized online scheduling mechanism that induces rational jobs to select their machine in such a way that the resulting schedule is 3.281-competitive. The mechanism deploys an online payment scheme that induces rational jobs to truthfully report about their private data: with respect to release dates and processing times, truthfully reporting is a dominant strategy equilibrium, whereas truthfully reporting the weights is a myopic best response equilibrium. We also show that the local scheduling policy used in the mechanism cannot be extended to a mechanism where truthful reports with respect to weights constitute a dominant strategy equilibrium.operations research and management science;

Characterization of revenue equivalence

The property of an allocation rule to be implementable in dominant strategies by a unique payment scheme is called \emph{revenue equivalence}. In this paper we give a characterization of revenue equivalence based on a graph theoretic interpretation of the incentive compatibility constraints. The characterization holds for any (possibly infinite) outcome space and many of the known results are immediate consequences. Moreover, revenue equivalence can be identified in cases where existing theorems are silent

Characterization of Revenue Equivalence

The property of an allocation rule to be implementable in dominant strategies by a unique payment scheme is called revenue equivalence. In this paper we give a characterization of revenue equivalence based on a graph theoretic interpretation of the incentive compatibility constraints. The characterization holds for any (possibly infinite) outcome space and many of the known results are immediate consequences. Moreover, revenue equivalence can be identified in cases where existing theorems are silent.computer science applications;

Optimal Mechanisms for Single Machine Scheduling

We study the design of optimal mechanisms in a setting where job-agents compete for being processed by a service provider that can handle one job at a time. Each job has a processing time and incurs a waiting cost. Jobs need to be compensated for waiting. We consider two models, one where only the waiting costs of jobs are private information (1-d), and another where both waiting costs and processing times are private (2-d). Probability distributions represent the public common belief about private information. We consider discrete and continuous distributions. In this setting, an optimal mechanism minimizes the total expected expenses to compensate all jobs, while it has to be Bayes-Nash incentive compatible. We derive closed formulae for the optimal mechanism in the 1-d case and show that it is efficient for symmetric jobs. For non-symmetric jobs, we show that efficient mechanisms perform arbitrarily bad. For the 2-d discrete case, we prove that the optimal mechanism in general does not even satisfy IIA, the `independent of irrelevant alternatives'' condition. Hence any attempt along the lines of the classical auction setting is doomed to fail. In the 2-d discrete case, we also show that the optimal mechanism is not even efficient for symmetric agents.operations research and management science;

Mechanisms for Decentralized Online Scheduling

The paper introduces a model for online parallel machine scheduling, where any single machine is run on the basis of a locally optimal sequencing policy. Jobs choose the machine on which they want to be processed themselves, and in addition, any job owns a piece of private information, namely its indifference cost for waiting one additional unit of timebefore being processed. We study this setting from the perspective of algorithmic mechanism design, and assuming that each job prefers to be completed as early as possible, the utilitarian social choice function minimizes the total weighted completiontimes.We prove that in this setting there exists an online mechanism, running in polynomial time, where rational jobs select their machine in such a way that the resulting schedule is 3.281-competitive with respect to the off-line optimal solution that maximizes social welfare. The mechanism deploys an online payment scheme that induces rational jobs to truthfullyreport their indifference costs, in the sense that it is a myopic best response. Moreover, the payment scheme results in a balanced budget, that is, payments are only made between jobs. We also discuss extensions to mechanisms where truth-telling is even an ex-post weakly dominant strategy, while preserving the competitive ratio.operations research and management science;