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New Bounds for Truthful Scheduling on Two Unrelated Selfish Machines

2019; Springer Science+Business Media; Volume: 64; Issue: 2 Linguagem: Inglês

10.1007/s00224-019-09927-x

1433-0490

Olga Kuryatnikova, Juan C. Vera,

Machine Learning and Algorithms

We consider the minimum makespan problem for n tasks and two unrelated parallel selfish machines. Let Rn be the best approximation ratio of randomized monotone scale-free algorithms. This class contains the most efficient algorithms known for truthful scheduling on two machines. We propose a new Min − Max formulation for Rn, as well as upper and lower bounds on Rn based on this formulation. For the lower bound, we exploit pointwise approximations of cumulative distribution functions (CDFs). For the upper bound, we construct randomized algorithms using distributions with piecewise rational CDFs. Our method improves upon the existing bounds on Rn for small n. In particular, we obtain almost tight bounds for n = 2 showing that |R2 − 1.505996| < 10− 6.