Combinatorial Auctions without Money

Algorithmic Mechanism Design attempts to marry computation and incentives, mainly by leveraging monetary transfers between designer and selfish agents involved. This is principally because in absence of money, very little can be done to enforce truthfulness. However, in certain applications, money is unavailable, morally unacceptable or might simply be at odds with the objective of the mechanism. For example, in Combinatorial Auctions (CAs), the paradigmatic problem of the area, we aim at solutions of maximum social welfare, but still charge the society to ensure truthfulness. We focus on the design of incentive-compatible CAs without money in the general setting of k-minded bidders. We trade monetary transfers with the observation that the mechanism can detect certain lies of the bidders: i.e., we study truthful CAs with verification and without money. In this setting, we characterize the class of truthful mechanisms and give a host of upper and lower bounds on the approximation ratio obtained by either deterministic or randomized truthful mechanisms. Our results provide an almost complete picture of truthfully approximating CAs in this general setting with multi-dimensional bidders

Randomized truthful mechanisms for scheduling unrelated machines

Abstract. We study the scheduling problem on unrelated machines in the mechanism design setting. This problem was proposed and studied in the seminal paper of Nisan and Ronen [NR99], where they gave a 1.75-approximation randomized truthful mechanism for the case of two machines. We improve this result by a 1.6737-approximation randomized truthful mechanism. We also generalize our result to a 0.8368m-approximation mechanism for task scheduling with m machines, which improve the previous best upper bound of 0.875m[MS07]. 1

A Self-Tuning Job Scheduler Family with Dynamic Policy Switching

The performance of job scheduling policies strongly depends on the properties of the incoming jobs. If the job characteristics often change, the scheduling policy should follow these changes. For this purpose the dynP job scheduler family has been developed

Comparison of multi-criteria scheduling techniques

This paper proposes a novel schedule-based approach for scheduling a continuous stream of batch jobs on the machines of a computational Grid. Our new solutions represented by dispatching rule Earliest Gap— Earliest Deadline First (EG-EDF) and Tabu search are based on the idea of filling gaps in the existing schedule. EG-EDF rule is able to build the schedule for all jobs incrementally by applying technique which fills earliest existing gaps in the schedule with newly arriving jobs. If no gap for a coming job is available EG-EDF rule uses Earliest Deadline First (EDF) strategy for including new job into the existing schedule. Such schedule is then optimized using the Tabu search algorithm moving jobs into earliest gaps again. Scheduling choices are taken to meet the Quality of Service (QoS) requested by the submitted jobs, and to optimize the usage of hardware resources. We compared the proposed solution with some of the most common queue-based scheduling algorithms like FCFS, EASY backfilling, and Flexible backfilling. Experiments shows that EG-EDF rule is able to compute good assignments, often with shorter algorithm runtime w.r.t. the other queue-based algorithms. Further Tabu search optimization results in higher QoS and machine usage while keeping the algorithm runtime reasonable