Truthful Approximation Mechanisms for Scheduling Selfish Related Machines

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...

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 algorithms for scheduling selfish tasks on parallel machines

We consider the problem of designing truthful mechanisms for scheduling selfish tasks (or agents)—whose objective is the minimization of their completion times—on parallel identical machines in order to minimize the makespan. A truthful mechanism can be easily obtained in this context (if we, of course, assume that the tasks cannot shrink their lengths) by scheduling the tasks following the inc...

Randomized Truthful Algorithms for Scheduling Selfish Tasks on Parallel Machines

A unified approach to truthful scheduling on related machines

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 lp norm of the machine loads vector. Previously, this kind of result was only known for the makespan ob...