A Unified Approach to Truthful Scheduling on Related Machines

Abstract

We present a unified framework for designing deterministic monotone polynomial time approximation schemes (PTAS's) for a wide class of scheduling problems on uniformly related machines. This class includes (among others) minimizing the makespan, maximizing the minimum load, and minimizing the p norm of the machine loads vector. Previously, this kind of result was only known for the makespan objective. Monotone algorithms have the property that an increase in the speed of a machine cannot decrease the amount of work assigned to it. The key idea of our novel method is to show that for goal functions that are sufficiently well-behaved functions of the machine loads, it is possible to compute in polynomial time a highly structured nearly optimal schedule. An interesting aspect of our approach is that, in contrast to all known approximation schemes, we avoid rounding any job sizes or speeds throughout. We can therefore find the exact best structured schedule using dynamic programming. The state space encodes a sufficient amount of information such that no postprocessing is needed, allowing an elegant and relatively simple analysis without any special cases. The monotonicity is a consequence of the fact that we find the best schedule in a specific collection of schedules. Monotone approximation schemes have an important role in the emerging area of algorithmic mechanism design. In the game-theoretical setting of these scheduling problems there is a social goal, which is one of the objective functions that we study. Each machine is controlled by a selfish singleparameter agent, where its private information is its cost of processing a unit sized job, which is also the inverse of the speed of its machine. Each agent wishes to maximize its own profit, defined as the payment it receives from the mechanism minus its cost for processing all jobs assigned to it, and places a bid which corresponds to its private information. For each one of the problems, we show that we can calculate payments that guarantee truthfulness in an efficient manner. Thus, there exists a dominant strategy where agents report their true speeds, and we show the existence of a truthful * Department of Mathematics, University of Haifa, 31905 Haifa, Israel. lea@math.haifa.ac.il.† Faculty of Industrial Engineering and Management, The Technion, 32000 Haifa, Israel. levinas@ie.technion.ac.il.‡ Max Planck Institute for Informatics, Saarbrücken, Germany. vanstee@mpi-inf.mpg.de mechanism which can be implemented in polynomial time, where the social goal is approximated within a factor of 1+ ε for every ε > 0.

Truthful mechanism design via correlated tree rounding

“…The best mechanisms are PTAS'es that are truthful-in-expectation [Dhangwatnotai et al 2008] and deterministically truthful [Christodoulou and Kovács 2013]. In addition, there exists a deterministic PTAS that is applicable to a variety of other objective functions [Epstein et al 2013].…”

The cost of selfishness for maximizing the minimum load on uniformly related machines

“…Various game-theoretic aspects of max-min fairness in resource allocation games were considered, but unlike the makespan minimization problem for which the poa and the pos were extensively studied (see [17,3,18]), these measures were not previously considered for the uncoordinated machine covering problem in the setting of selfish jobs and uniformly related machines. A different model, where machines are selfish rather than jobs (with the same social goal function) was studied recently in [10,5,9].…”