New Bounds for Truthful Scheduling on Two Unrelated Selfish Machines

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

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...

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...

Preemptive Scheduling on Selfish Machines

We consider the problem of scheduling on parallel uniformly related machines, where preemptions are allowed and the machines are controlled by selfish agents. Our goal is to minimize the makespan, whereas the goal of the agents is to maximize their profit. We show that a known algorithm is monotone and can therefore be used to create a truthful mechanism for this problem which achieves the opti...