{"title":"On Scheduling Mechanisms Beyond the Worst Case","authors":"Yansong Gao, Jie Zhang","doi":"10.1007/s00453-024-01277-6","DOIUrl":null,"url":null,"abstract":"<div><p>The problem of scheduling unrelated machines has been studied since the inception of algorithmic mechanism design (Nisan and Ronen, Algorithmic mechanism design(extended abstract). In: Proceedings of the Thirty First Annual ACM Symposium on Theory of Computing (STOC), pp. 129–140, 1999. It is a resource allocation problem that entails assigning <i>m</i> tasks to <i>n</i> machines for execution. Machines are regarded as strategic agents who may lie about their execution costs so as to minimize their time cost. To address the situation when monetary payment is not an option to compensate the machines’ costs, Koutsoupias (Theory Comput Syst 54:375–387, 2014) devised two <i>truthful</i> mechanisms, K and P respectively, that achieves an approximation ratio of <span>\\(\\frac{n+1}{2}\\)</span> and <i>n</i>, for social cost minimization. In addition, no truthful mechanism can achieve an approximation ratio better than <span>\\(\\frac{n+1}{2}\\)</span>. Hence, mechanism K is optimal. While the approximation ratio provides a strong worst-case guarantee, it also limits us to a comprehensive understanding of mechanism performance on various inputs. This paper investigates these two scheduling mechanisms beyond the worst case. We first show that mechanism K achieves a smaller social cost than mechanism P on every input. That is, mechanism K is pointwise better than mechanism P. Next, for each task, when machines’ execution costs are independent and identically drawn from a task-specific distribution, we show that the average-case approximation ratio of mechanism K converges to a constant determined by the task-specific distribution. This bound is tight for mechanism K. For a better understanding of this distribution-dependent constant, on the one hand, we estimate its value by plugging in a few common distributions; on the other, we show that this converging bound improves a known bound (Zhang in Algorithmica 83(6):1638–1652, 2021)) which only captures the single-task setting. Last, we find that the average-case approximation ratio of mechanism P converges to the same constant.\n</p></div>","PeriodicalId":50824,"journal":{"name":"Algorithmica","volume":"87 1","pages":"1 - 21"},"PeriodicalIF":0.9000,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s00453-024-01277-6.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Algorithmica","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s00453-024-01277-6","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
The problem of scheduling unrelated machines has been studied since the inception of algorithmic mechanism design (Nisan and Ronen, Algorithmic mechanism design(extended abstract). In: Proceedings of the Thirty First Annual ACM Symposium on Theory of Computing (STOC), pp. 129–140, 1999. It is a resource allocation problem that entails assigning m tasks to n machines for execution. Machines are regarded as strategic agents who may lie about their execution costs so as to minimize their time cost. To address the situation when monetary payment is not an option to compensate the machines’ costs, Koutsoupias (Theory Comput Syst 54:375–387, 2014) devised two truthful mechanisms, K and P respectively, that achieves an approximation ratio of \(\frac{n+1}{2}\) and n, for social cost minimization. In addition, no truthful mechanism can achieve an approximation ratio better than \(\frac{n+1}{2}\). Hence, mechanism K is optimal. While the approximation ratio provides a strong worst-case guarantee, it also limits us to a comprehensive understanding of mechanism performance on various inputs. This paper investigates these two scheduling mechanisms beyond the worst case. We first show that mechanism K achieves a smaller social cost than mechanism P on every input. That is, mechanism K is pointwise better than mechanism P. Next, for each task, when machines’ execution costs are independent and identically drawn from a task-specific distribution, we show that the average-case approximation ratio of mechanism K converges to a constant determined by the task-specific distribution. This bound is tight for mechanism K. For a better understanding of this distribution-dependent constant, on the one hand, we estimate its value by plugging in a few common distributions; on the other, we show that this converging bound improves a known bound (Zhang in Algorithmica 83(6):1638–1652, 2021)) which only captures the single-task setting. Last, we find that the average-case approximation ratio of mechanism P converges to the same constant.
自算法机制设计(Nisan and Ronen,《算法机制设计(扩展摘要)》)提出以来,人们就开始研究不相关机器的调度问题。参见:美国计算机学会第三十一届计算理论年会论文集,第129-140页,1999。这是一个资源分配问题,需要将m个任务分配给n台机器执行。机器被视为战略代理,它们可能会谎报自己的执行成本,以最小化自己的时间成本。为了解决货币支付不是补偿机器成本的选择的情况,Koutsoupias(理论计算系统54:375-387,2014)设计了两个真实的机制,分别为K和P,实现了社会成本最小化的近似比率\(\frac{n+1}{2}\)和n。此外,没有任何真实的机制可以获得比\(\frac{n+1}{2}\)更好的近似比率。因此,机制K是最优的。虽然近似比提供了强有力的最坏情况保证,但它也限制了我们对各种输入下机制性能的全面理解。本文研究了这两种最坏情况下的调度机制。我们首先证明,在每一种投入上,机制K比机制P获得的社会成本更小。也就是说,机制K在点上优于机制p。接下来,对于每个任务,当机器的执行成本独立且相同地从特定任务分布中提取时,我们证明了机制K的平均情况近似比收敛于由特定任务分布决定的常数。对于机制k,这个界是紧的。为了更好地理解这个与分布相关的常数,一方面,我们通过代入一些常见分布来估计它的值;另一方面,我们证明了这个收敛的边界改进了一个已知的边界(Zhang in Algorithmica 83(6):1638 - 1652,2021)),它只捕获了单任务设置。最后,我们发现机构P的平均情况近似比收敛于同一常数。
期刊介绍:
Algorithmica is an international journal which publishes theoretical papers on algorithms that address problems arising in practical areas, and experimental papers of general appeal for practical importance or techniques. The development of algorithms is an integral part of computer science. The increasing complexity and scope of computer applications makes the design of efficient algorithms essential.
Algorithmica covers algorithms in applied areas such as: VLSI, distributed computing, parallel processing, automated design, robotics, graphics, data base design, software tools, as well as algorithms in fundamental areas such as sorting, searching, data structures, computational geometry, and linear programming.
In addition, the journal features two special sections: Application Experience, presenting findings obtained from applications of theoretical results to practical situations, and Problems, offering short papers presenting problems on selected topics of computer science.