An Adaptive Scoring Job Scheduling algorithm for grid computing

Chang, Ruay-Shiung; Lin, Chih‐Yuan; Lin, Chun-Fu

doi:10.1016/j.ins.2012.04.019

Cited by 53 publications

(35 citation statements)

References 13 publications

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“…The process of categorization of algorithm depends on the process of work started and load balancing policy. The policy of load balancing divided into three section one is data sender, data receiver and combination of both [25][26] [29]. The process of load balancing technique categorized into two sections one is static and other is dynamic.…”

Section: Fig 1: Job Allocation Processes In Public Cloud Computing Mmentioning

confidence: 99%

Performance Evaluation of Job Allocation and Load Balancing Methods Using Meta-Heuristic Function in Public Cloud Network

Sahu¹,

Sheetalani²

2017

IOSR JCE

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Section: Fig 1: Job Allocation Processes In Public Cloud Computing Mmentioning

confidence: 99%

Performance Evaluation of Job Allocation and Load Balancing Methods Using Meta-Heuristic Function in Public Cloud Network

Sahu¹,

Sheetalani²

2017

IOSR JCE

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“…In order to utilize the power of grid completely, we need an efficient job scheduling algorithm to assign jobs to resources in a grid. Chang et al (2012) propose an Adaptive Scoring Job Scheduling algorithm (ASJS) for the grid environment. Compared to other methods, it can decrease the completion time of submitted jobs, which may compose of computing-intensive jobs and data-intensive jobs.…”

Section: Literature Surveymentioning

confidence: 99%

Modified Ant Colony Optimization for Workflow Scheduling in Cloud Enviornment

Mohana¹,

Saroja²,

Venkatachalam³

2015

IJCT

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Cloud computing is a type of parallel and distributed system consisting of a collection of interconnected and virtual computers. This technological trend has enabled the realization of a new computing model called cloud computing, in which shared resources, information,software & other devices are provided according to client requirement at specific time, are provided as general utilities that can be leased and released by users through the Internet in an on-demand fashion.Cloud workflow scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it.Allocation of resources to a large number of workflows in a cloud computing environment presents more difficulty than in network computational environments.A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. In this work, modified ant colony optimization for cloud task scheduling is proposed. The goal of modification is to enhance the performance of the basic ant colony optimization algorithm and optimize the task execution time in view of minimizing the makespan of a given tasks set.

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“…This juncture does not have any impact on replication logic but mainly on the implementation details of transaction protocols. (2) Technology standard: SOR adapts web service whereas DOR may uses Common Object Request Brokers (CORBA), especially in the context of grid computing [9]. Differential standards do not impact the logic of replication but differs on the implementation of relevant middleware.…”

Section: Service Oriented Replication Versus Data Oriented Replicationmentioning

confidence: 99%

Scalable service-oriented replication with flexible consistency guarantee in the cloud

Chen

Bahsoon

Tawil

2014

Information Sciences

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a b s t r a c tReplication techniques are widely applied in and for cloud to improve scalability and availability. In such context, the well-understood problem is how to guarantee consistency amongst different replicas and govern the trade-off between consistency and scalability requirements. Such requirements are often related to specific services and can vary considerably in the cloud. However, a major drawback of existing service-oriented replication approaches is that they only allow either restricted consistency or none at all. Consequently, service-oriented systems based on such replication techniques may violate consistency requirements or not scale well. In this paper, we present a Scalable Service Oriented Replication (SSOR) solution, a middleware that is capable of satisfying applications' consistency requirements when replicating cloud-based services. We introduce new formalism for describing services in service-oriented replication. We propose the notion of consistency regions and relevant service oriented requirements policies, by which trading between consistency and scalability requirements can be handled within regions. We solve the associated sub-problem of atomic broadcasting by introducing a Multi-fixed Sequencers Protocol (MSP), which is a requirements aware variation of the traditional fixed sequencer approach. We also present a Region-based Election Protocol (REP) that elastically balances the workload amongst sequencers. Finally, we experimentally evaluate our approach under different loads, to show that the proposed approach achieves better scalability with more flexible consistency constraints when compared with the state-of-the-art replication technique.

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An Adaptive Scoring Job Scheduling algorithm for grid computing

Cited by 53 publications

References 13 publications

Performance Evaluation of Job Allocation and Load Balancing Methods Using Meta-Heuristic Function in Public Cloud Network

Performance Evaluation of Job Allocation and Load Balancing Methods Using Meta-Heuristic Function in Public Cloud Network

Modified Ant Colony Optimization for Workflow Scheduling in Cloud Enviornment

Scalable service-oriented replication with flexible consistency guarantee in the cloud

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