An Approach to Optimized Resource Scheduling Algorithm for Open-Source Cloud Systems

Zhong, Han; Tao, Kun; Zhang, Xuejie

doi:10.1109/chinagrid.2010.37

Cited by 93 publications

(33 citation statements)

References 9 publications

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“…They have used Improved Genetic algorithm (IGA) for automated scheduling policy. Their approach shows an increase in the utilization rate of the resources in the cloud [8].…”

Section: Literature Surveymentioning

confidence: 98%

An Efficient Job Classification Technique to Enhance Scheduling in Cloud to Accelerate the Performance

Vaidehi

Nair

Suma

2014

ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India- Vol I

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Abstract.As cloud computing is becoming more ubiquitous with increasing espousal of advanced technologies, more and more efficient techniques are required to enhance the system performance. The computing systems in the cloud comprise heterogeneity of components or resources. Challenge here is efficient scheduling and resource allocation to jobs requesting the computing devices in order to achieve customer satisfaction. Retention of customer satisfaction is one of the primary factors for an organization to exist. To achieve the aforementioned goal, it is required to implement the principles of software engineering in every task that is accomplished in the organization. Thus, with organizations marching towards cloud environment, the jobs are initially clustered or grouped and subsequently scheduled for the resource arbitration and allocation. This paper focuses on clustering or grouping of jobs. The unsupervised technique or the clustering of the jobs is done based on the logistic regression approach. As this approach is more robust and the parameters considered for classification are more independent, simulation evidence suggests the classification technique cluster the jobs more effectively and provide a consistent utilization of the available resources. This ensures that the non functional requirement of availability of jobs to their customers is achieved thereby enhancing the business performance.

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“…They have used Improved Genetic algorithm (IGA) for automated scheduling policy. Their approach shows an increase in the utilization rate of the resources in the cloud [8].…”

Section: Literature Surveymentioning

confidence: 98%

An Efficient Job Classification Technique to Enhance Scheduling in Cloud to Accelerate the Performance

Vaidehi

Nair

Suma

2014

ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India- Vol I

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“…Several research projects tackle the task scheduling issue in cloud for many applications like workflow and e-learning applications [21][22][23][24], but no resource optimization mechanism is provided in order to guarantee both efficient task scheduling and resource usage. Finally to the best of our knowledge, these two issues have been recently addressed in [25].…”

Section: State Of the Art Reviewmentioning

confidence: 99%

Genetic-based algorithms for resource management in virtualized IVR applications

et al. 2014

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Interactive Voice Response (IVR) is a technology that allows automatic human-computer interactions, via a telephone keypad or voice commands. The systems are widely used in many industries, including telecommunications and banking. Virtualization is a potential technology that can enable the easy development of IVR applications and their deployment on the cloud. IVR virtualization will enable efficient resource usage by allowing IVR applications to share different IVR substrate components such as the key detector, the voice recorder and the dialog manager. Resource management is part and parcel of IVR virtualization and poses a challenge in virtualized environments where both processing and network constraints must be considered. Considering several objectives to optimize the resource usage makes it even more challenging. This paper proposes IVR virtualization task scheduling and computational resource sharing (among different IVR applications) strategies based on genetic algorithms, in which different objectives are optimized. The algorithms used by both strategies are simulated and the performance measured and analyzed.

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“…For maximizing the resource utilization, Zhong et al [2010] designed a super GA scheduler that was able to allocate the VMs efficiently, permitting the maximum usage of physical resource. Therefore, the used PMs were on the maximal utilization ratio and the number of PMs was reduced.…”

Section: Scheduling For Energy Conservationmentioning

confidence: 99%

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

et al. 2015

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A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org.

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An Approach to Optimized Resource Scheduling Algorithm for Open-Source Cloud Systems

Cited by 93 publications

References 9 publications

An Efficient Job Classification Technique to Enhance Scheduling in Cloud to Accelerate the Performance

An Efficient Job Classification Technique to Enhance Scheduling in Cloud to Accelerate the Performance

Genetic-based algorithms for resource management in virtualized IVR applications

Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches

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