Scheduling in Distributed Computing Systems

Vidyarthi, Deo Prakash; Sarker, Biplab Kumer; Tripathi, Anil Kumar; Yang, Laurence T.

doi:10.1007/978-0-387-74483-4

Cited by 57 publications

(44 citation statements)

References 5 publications

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“…The completion time estimation depends on two factors: execution time 'E' of the module on a node and the intermodule communication time 'B' in form of data exchange between the modules of the job allocated on the nodes having a distance 'D' between them [7,14,20].…”

Section: Load Balancing Allocation (Lba) Methodsmentioning

confidence: 99%

“…Most of these models did scheduling with a single objective. In recent, several multiobjective optimization algorithms for the grid scheduling problem are reported in the literature [1,5,9,19,20].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The basic characteristic, such as job specialization, number of modules within the job and the module size that gives information on the length of the job is communicated to the cluster. It is presumed that during the job pre-processing, i.e., data flow analysis, the number of instructions and loop constructs in the modules are determined [14,17,20]. Job precedence graph (JPG) exhibits the modules precedence in the job for execution.…”

Section: The Proposed Modelmentioning

confidence: 99%

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An energy-efficient reliable grid scheduling model using NSGA-II

Kaushik

Vidyarthi

2015

Engineering with Computers

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parameters by effectively managing the available grid resources. In general, it is unusual to assume a single criterion for the decision making in a grid system which is governed by various related factors. The scheduling problem in the grid system may have several conflicting objectives, e.g., makespan, reliability, energy consumption, cost, security, etc., which need to be optimized simultaneously [1][2][3][4].The outcome values of different objectives, viz. execution time of any job, the energy consumption by the job and the reliability offered by the system to the job may vary from one allocation to another [5]. The distributed nature of grid resources and the dynamic nature of the grid workload make the makespan of the job unpredictable. For makespan minimization, it is warranted to effectively deal with the load balancing as well [6]. Further, the heterogeneous hardware components of the grid are more prone to failure and therefore reliability of such systems decreases with the increase in the number of geographically distributed resources [7]. The heterogeneity of the system demands varying energy consumption based on their hardware profile [8]. It necessitates choosing those nodes/machines that are able to execute the job quickly, reliably and with least energy consumption.Since these objectives are conflicting in nature, any approach adopted to optimize one objective may not result in the overall optimized solution with respect to other objectives. All the conflicting objectives need to be addressed simultaneously and the best way for this is to have the trade-off solutions with all the objectives [9, 10]. A good solution corresponding to all the objectives is desired. This solution may not offer the best of all the objectives, but certainly better when seen as a trade-off among the objectives. This approach is referred as multiobjective optimization technique [5]. As such, job scheduling in the grid is an NP-hard problem and traditional Abstract Job scheduling in computational grid is a complex problem and various heuristics and meta-heuristics have been proposed for the same. These approaches usually optimize specific characteristic parameters while allocating the jobs on the grid resources. Many a times, it is desired to optimize multiple parameters during job scheduling. Nondominated sorting genetic algorithm (NSGA-II) has been observed to be the best meta-heuristic to solve such multiobjective optimization problem. The proposed work applies NSGA-II for job scheduling in computational grid with three conflicting objectives: maximizing reliability of the system for job allocation, minimizing energy consumption and balancing the load on the system. Performance study of the proposed model is done by simulating it on some real data. The result indicates that the proposed model performs well with multiple objectives.

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Section: Load Balancing Allocation (Lba) Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: The Proposed Modelmentioning

confidence: 99%

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An energy-efficient reliable grid scheduling model using NSGA-II

Kaushik

Vidyarthi

2015

Engineering with Computers

Self Cite

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“…As shown in Fig. 1, WDC workload allocation can be viewed as a problem of mapping the application's task graph to the network's communication graph such that the operational objectives are optimized [2,7]. This section presents macroscopic power and energy consumption models of individual subsystems and the WDC network, which are derived from low level power consumption models [5,8].…”

Section: Wdc Network System Modelmentioning

confidence: 99%

“…A distributed application may be realized in terms of software components which may potentially be executed on physically separated processors, where the individual components interact with each other over communication links. The notion of cooperation among nodes, for the purpose of processing computational tasks of an application in a distributed manner, has been discussed previously in the context of traditional distributed computing [2], wireless sensor networks (WSNs) [3] and general wireless networks [4]. There, however, exist additional challenges in applying existing techniques for wireless distributed computing (WDC) where the communication between nodes occurs over wireless links characterized by non-deterministic and heterogeneous behavior.…”

Section: Introductionmentioning

confidence: 99%

Task allocation and scheduling in wireless distributed computing networks

Datla

Volos

Hasan

et al. 2011

Analog Integr Circ Sig Process

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Wireless distributed computing (WDC) is an enabling technology that allows radio nodes to cooperate in processing complex computational tasks of an application in a distributed manner. WDC research is being driven by the fact that mobile portable computing devices have limitations in executing complex mobile applications, mainly attributed to their limited resource and functionality. This article focuses on resource allocation in WDC networks, specifically on scheduling and task allocation. In WDC, it is important to schedule communications between the nodes in addition to the allocation of computational tasks to nodes. Communication scheduling and heterogeneity in the operating environment make the WDC resource allocation problem challenging to address. This article presents a task allocation and scheduling algorithm that optimizes both energy consumption and makespan in a heuristic manner. The proposed algorithm uses a comprehensive model of the energy consumption for the execution of tasks and communication between tasks assigned to different radio nodes. The algorithm is tested for three objectives, namely, minimization of makespan, minimization of energy consumption, and minimization of both makespan and energy consumption.

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Maximizing availability for task scheduling in computational grid using genetic algorithm

Prakash

Vidyarthi

2014

Concurrency and Computation

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Computational grid provides a wide distributed platform for high-end compute intensive applications. Grid scheduling is often carried out to schedule the submitted jobs on the nodes of the grid so that some characteristic parameter is optimized. Availability of the computational nodes is one of the important characteristic parameters and measures the probability of the node availability for job execution. This paper addresses the availability of the grid computational nodes for the job execution and proposes a model to maximize it. As such, the task scheduling problem in grid is nondeterministic polynomial-time hard, and often, metaheuristics techniques are applied to solve it. Genetic algorithm, a metaheuristic technique based on evolutionary computation, has been used to solve such complex optimization problem. This work proposes a technique for the grid scheduling problem using genetic algorithm with the objective to maximize availability. Simulation experiment, to evaluate the performance of the proposed algorithm, is conducted, and results reveal the effectiveness of the model. A comparative study has also been performed.

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Scheduling in Distributed Computing Systems

Cited by 57 publications

References 5 publications

An energy-efficient reliable grid scheduling model using NSGA-II

An energy-efficient reliable grid scheduling model using NSGA-II

Task allocation and scheduling in wireless distributed computing networks

Maximizing availability for task scheduling in computational grid using genetic algorithm

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