Just-in-Time Server Provisioning Using Virtual Machine Standby and Request Prediction

Machida, Fumio; Kawato, Masahiro; Maeno, Yoshiharu

doi:10.1109/icac.2008.13

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…Therefore, it is important that minimize the number of movement of the already allocated machines and provide optimal resource sites for VM at the beginning. Also the presented researches [1], [2] did not consider properties of applications. It is required to provide and manage resources more efficiently.…”

Section: Related Workmentioning

confidence: 99%

Application-specific Cloud Provisioning Model Using Job Profiles Analysis

Kim

2012

2012 IEEE 14th International Conference on High Performance Computing and Communication &Amp; 2012 IEEE 9th International Confe

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The rapid advent of computing technology enables scientists to expand their research infrastructure over supercomputer-on-demand using a resource leasing service on pay-per-use basis. This infrastructure service is called as Science cloud which provides uniform user interface to scientific experiments over large-scale heterogeneous resources. In spite of the strength of conveniences, it is difficult to manage the experiments to guarantee optimal performance of jobs since the execution environment is based on virtualization technology which has additional performance overheads.This paper proposes a cloud resource provisioning model using statistical analysis of job profiles for computational science. In this model, we utilize job profiles which are generated from application executions and identify features of application by applying statistical analysis. The analysis is performed using PCA and the most effective factors are inferred from profiles. The effective factors are used for picking a job profile being referred and then VM type is determined using the most effective job s property. An application is executed on VM of the chosen cluster and its performance result is incorporated into job profile set with purpose of evaluating profile's credit. Our simulation results show this model enables scientists to take advantages of cloud computing without performance degradation.

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Section: Related Workmentioning

confidence: 99%

Application-specific Cloud Provisioning Model Using Job Profiles Analysis

Kim

2012

2012 IEEE 14th International Conference on High Performance Computing and Communication &Amp; 2012 IEEE 9th International Confe

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“…PROVISIONING METHOD In the proposed system architecture, provisioning method has been used to predict the future usage of computing resources, that provides useful suggestions to users for optimizing cloud configurations based on the configuration model proposed [7]. This paper applies trend analysis to the historical data to extract the underlying trend patterns.…”

Section: Aximize : Ef F (T)mentioning

confidence: 99%

A Cost-Effective Intelligent Configuration Model in Cloud Computing

Tsai

Chen

2012

2012 32nd International Conference on Distributed Computing Systems Workshops

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Cloud computing environments provide a resource pool for customers for their processing, storage and networking needs. It is necessary for customers to choose desirable configuration from vast resources available in the cloud. This paper proposes a heuristic model to choose a cloud configuration for efficient use. Trend analysis in data mining is used to predict the future trend and assist provisioning. The model is experimented with Intel power 32 core machine and in Azure cloud. Both environments demonstrated that the proposed model provided effective solutions.

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“…The increasing idle time of resources lead to the resource utilization rate lower than 20%. To utilize idle resources efficiently, server provisioning was proposed [1] and it becomes cloud service with virtualization technique. One of the cloud computing service forms is Infrastructure as a Service (IaaS) and it provides infrastructure resources by configuring the server, storage, and network into virtualized resources reacting to on-demand requests.…”

Section: Introductionmentioning

confidence: 99%

A Cost-Efficient Job Scheduling Algorithm in Cloud Resource Broker with Scalable VM Allocation Scheme

Ren¹,

Kim²,

Kang³

et al. 2012

KIPS Transactions on Software and Data Engineering

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Cloud service users request dedicated virtual computing resource from the cloud service provider to process jobs in independent environment from other users. To optimize this process with automated method, in this paper we proposed a framework for workflow scheduling in the cloud environment, in which the core component is the middleware called broker mediating the interaction between users and cloud service providers. To process jobs in on-demand and virtualized resources from cloud service providers, many papers propose scheduling algorithms that allocate jobs to virtual machines which are dedicated to one machine one job. With this method, the isolation of being processed jobs is guaranteed, but we can't use each resource to its fullest computing capacity with high efficiency in resource utilization. This paper therefore proposed a cost-efficient job scheduling algorithm which maximizes the utilization of managed resources with increasing the degree of multiprogramming to reduce the number of needed virtual machines; consequently we can save the cost for processing requests. We also consider the performance degradation in proposed scheme with thrashing and context switching. By evaluating the experimental results, we have shown that the proposed scheme has better cost-performance feature compared to an existing scheme.

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Just-in-Time Server Provisioning Using Virtual Machine Standby and Request Prediction

Cited by 6 publications

References 15 publications

Application-specific Cloud Provisioning Model Using Job Profiles Analysis

Application-specific Cloud Provisioning Model Using Job Profiles Analysis

A Cost-Effective Intelligent Configuration Model in Cloud Computing

A Cost-Efficient Job Scheduling Algorithm in Cloud Resource Broker with Scalable VM Allocation Scheme

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