Multi‐organization scheduling approximation algorithms

Cohen, Johanne; Cordeiro, Daniel; Trystram, Denis; Wagner, Frederic H.

doi:10.1002/cpe.1752

Cited by 9 publications

(12 citation statements)

References 21 publications

(28 reference statements)

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“…Using tools from algorithmic game theory, we extend the notions of independence and selfishness of each participating organization. We first revisit previous results that were limited to sequential jobs and extend them to the more general case where the jobs are composed of parallel rigid jobs, showing that the characteristics of the workload actually defines how costly is the decentralization of the decision‐making process.…”

Section: Introductionmentioning

confidence: 90%

Coordination mechanisms for decentralized parallel systems

Cohen

Cordeiro

Trystram

2014

Concurrency and Computation

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International audienceOn resource sharing platforms, the execution of the jobs submitted by users is usually controlled by a centralized global scheduler. It determines efficient schedules regarding some common objective function that all organizations agree with (for instance, maximizing the utilization of the entire platform). However, in practice, each organization is mostly interested in the performance obtained for its own jobs. We study the price that the collectivity must pay in order to allow independence to selfish, self-governing organizations, so they can choose the best schedules for their own jobs. In other words, we are interested in analyzing the costs on the global performance inflicted by the decentralization of scheduling policies. We present a game-theoretic model for the problem and the associated coordination mechanisms developed to reduce the cost of the decentralization of the decision-making process. The main contribution is to show (in theory and practice) how to devise pure Nash equilibria configurations for every instance of the problem and to prove that the price paid by the collectivity depends on the local scheduling policy and on the characteristics of the workload executed on such platform

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Section: Introductionmentioning

confidence: 90%

Coordination mechanisms for decentralized parallel systems

Cohen

Cordeiro

Trystram

2014

Concurrency and Computation

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“…Often such a situation follows a global objective function which a central authority explicitly pursues, as far as possible under the actions of the agents. These kinds of scenarios have been considered in (Cohen et al 2011;Huynh Tuong et al 2012) where an indicator independent of the objectives of the agents, e.g., the overall performance of a workshop or the global revenue of a company, has to be optimized. However, in our setting no global objective function is manifestly taken into account.…”

Section: Introductionmentioning

confidence: 99%

Competitive multi-agent scheduling with an iterative selection rule

2017

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In this work we address a class of deterministic scheduling problems in which k agents compete for the usage of a single machine. The agents have their own objective functions and submit their tasks in successive steps to an external coordination subject, who sequences them by selecting the shortest task in each step. We look at the problem in two different settings and consider different combinations of cost functions. In a centralized perspective, generalizing previous results for the case with k = 2 agents, we characterize the set of Pareto efficient solutions as for a classical multicriteria optimization problems. On one hand we determine the number of Pareto efficient solutions and on the other hand we study the computational complexity of the associated decision problem. Then, we consider the problem from a single agent perspective. In particular, we provide a worst-case analysis on the performance of two natural heuristic algorithms, SPT and WSPT, that suggest to an agent how to sequence its own tasks when its objective is makespan, sum of completion times, or sum of weighted completion times.

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“…Therefore, the current efforts in the grid computing research focus on the design of new effective grid schedulers, that can simultaneously optimize the key grid objectives, such as makespan, flowtime, resource and cumulative energy utilization [11]. Energy efficient scheduling in CGs becomes a complex endeavor due to the multi-constraints, various optimization criteria and different priorities of the resource owners.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical genetic-based grid scheduling with energy optimization

et al. 2012

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An optimization of power and energy consumptions is the important concern for a design of modern-day and future computing and communication systems. Various techniques and high performance technologies have been investigated and developed for an efficient management of such systems. All these technologies should be able to provide good performance and to cope under an increased workload demand in the dynamic environments such as Computational Grids (CGs), clusters and clouds. In this paper we approach the independent batch scheduling in CG as a bi-objective minimization problem with makespan and energy consumption as the scheduling criteria. We use the Dynamic Voltage Scaling (DVS) methodology for scaling and possible reduction of cumulative power energy utilized by the system resources. We develop two implementations of Hierarchical Genetic Strategy-based grid scheduler (Green-HGS-Sched) with elitist and struggle replacement mechanisms. The proposed algorithms were empirically evaluated versus single-population Genetic Algorithms (GAs) and Island GA models for four CG size scenarios in static and dynamic modes. The simulation results show that proposed scheduling methodologies fairly reduce the energy usage and can be easily adapted to the dynamically changing grid states and various scheduling scenarios.

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Multi‐organization scheduling approximation algorithms

Cited by 9 publications

References 21 publications

Coordination mechanisms for decentralized parallel systems

Coordination mechanisms for decentralized parallel systems

Competitive multi-agent scheduling with an iterative selection rule

Hierarchical genetic-based grid scheduling with energy optimization

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