Tight Analysis of Relaxed Multi-organization Scheduling Algorithms

Cordeiro, Daniel; Dutot, Pierre-François; Mounié, Grégory; Trystram, Denis

doi:10.1109/ipdps.2011.112

Cited by 8 publications

(4 citation statements)

References 18 publications

(31 reference statements)

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“…This problem is denoted by (1 + α)-MOSP [17] when it is assumed that each organization accepts to increase the maximum completion time of its tasks by a factor at most (1 + α). A 3 2 -approximate algorithm for 2-MOSP has been given [9]. Other work include additional constraints on the machines [4].…”

Section: Cmax(s )mentioning

confidence: 99%

Efficiency and equity in the multi organization scheduling problem

Durand¹,

Pascual²

2021

Theoretical Computer Science

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We consider the multi organization scheduling problem (MOSP) [20]: given N organizations owning, each of them, one set of tasks and machines, the aim is to compute a schedule which gathers all the tasks on all the machines, and such that the makespan is minimized. A rationality constraint must be fulfilled: no organization should increases its makespan (the completion time of its last task) compared to the case where it schedules its own tasks (and only its own tasks) on its own machines. We show that cooperation (sharing machines and tasks) can benefit to all the organizations simultaneously, since they may decrease their makespans by a factor of N . We present an algorithm which is (1 + )-approximate, while the makespan of each organization is increased by a factor at most (1 + ). We also study to which extent the rationality constraint (or a relaxed constraint) increases the makespan, compared to problem (P ||C max ) where there is no such a constraint. Finally, we introduce a new problem, which focus on equity: the aim is to return a schedule which fulfills the rationality constraint and which maximizes the factor by which each organization has decreased its makespan. We give an optimal algorithm for this problem in a particular case, and show that it is NP-hard and hard to approximate in the general case. We complete this paper by an efficient heuristic for this problem.

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Section: Cmax(s )mentioning

confidence: 99%

Efficiency and equity in the multi organization scheduling problem

Durand¹,

Pascual²

2021

Theoretical Computer Science

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“…Note that in this model the utilities are not transferable. This setting has been introduced by Pascual et al [13] for a scheduling problem where the resources are the machines and the clients are the tasks (the aim of each organization is to minimize the last completion time of its tasks), and has been followed by several other papers [3][4][5]11]. The authors give approximation algorithms and experimental results for this problem.…”

Section: Modelmentioning

confidence: 99%

Network sharing by two mobile operators: beyond competition, cooperation

2015

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In this paper, we study the sharing of a radio access network infrastructure by two mobile operators. Knowing the possible locations of the base stations, each operator chooses to invest or not on a base station, and its aim is to maximize its profit. We characterize the existence of Nash equilibria in such a game and we measure their quality with respect to the maximization of the overall profit (with the price of anarchy/stability). We then show how to obtain a solution in which each operator earns at least as much as it would have earned in any Nash equilibrium. Finally we conduct experiments on randomly generated instances and on real data.

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“…In the context of grid computing, Cordeiro et al [13] consider organizations that share clusters to distribute peak workloads among all the participants. Each cluster is associated with one agent and the global objective function is to minimize the makespan.…”

Section: Parallel Machinesmentioning

confidence: 99%

Solving multi-agent scheduling problems on parallel machines with a global objective function

2014

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In this study, we consider a scheduling environment with m (m ≥ 1) parallel machines. The set of jobs to schedule is divided into K disjoint subsets. Each subset of jobs is associated with one agent. The K agents compete to perform their jobs on common resources. The objective is to find a schedule that minimizes a global objective function f 0 , while maintaining the regular objective function of each agent, f k , at a level no greater than a fixed value,This problem is a multi-agent scheduling problem with a global objective function. In this study, we consider the case with preemption and the case without preemption. If preemption is allowed, we propose a polynomial time algorithm based on a network flow approach for the unrelated parallel machine case. If preemption is not allowed, we propose some general complexity results and develop dynamic programming algorithms.

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Tight Analysis of Relaxed Multi-organization Scheduling Algorithms

Cited by 8 publications

References 18 publications

Efficiency and equity in the multi organization scheduling problem

Efficiency and equity in the multi organization scheduling problem

Network sharing by two mobile operators: beyond competition, cooperation

Solving multi-agent scheduling problems on parallel machines with a global objective function

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