Copula-based Randomized Mechanisms for Truthful Scheduling on Two Unrelated Machines

An Improved Randomized Truthful Mechanism for Scheduling Unrelated Machines

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-appr...

Randomized Truthful Algorithms for Scheduling Selfish Tasks on Parallel Machines

Deterministic Truthful Approximation Mechanisms for Scheduling Related Machines

We consider the problem of scheduling jobs on parallel related machines owned by selfish agents. We provide deterministic polynomial-time (2 + ǫ)-approximation algorithms and suitable payment functions that yield truthful mechanisms for several NP-hard restrictions of this problem. Up to our knowledge, our result is the first non-trivial polynomial-time deterministic truthful mechanism for this...

Truthful Mechanisms for Scheduling Selfish Related Machines Using ACO

Task scheduling is a major challenge in parallel and distributed systems. Task scheduling techniques in distributed systems are usually based on trusting the Accuracy of the information about the status of resources. In a commercial multiCloud environment, individual providers are focused towards increasing their own profits and do not care about the utility of users and other providers. In suc...

Scheduling Unrelated Machines by Randomized Rounding

In this paper, we provide a new class of randomized approximation algorithms for parallel machine scheduling problems. The most general model we consider is scheduling unrelated machines with release dates (or even network scheduling) so as to minimize the average weighted completion time. We introduce an LP relaxation in time-indexed variables for this problem. The crucial idea to derive appro...