A coordinator for scaling elastic applications across multiple clouds

Calheiros, Rodrigo N.; Toosi, Adel Nadjaran; Vecchiola, Christian; Buyya, Rajkumar

doi:10.1016/j.future.2012.03.010

Cited by 96 publications

(50 citation statements)

References 31 publications

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“…Running Data Mining and performing Business data analysis for business [6] are allowable to customers. Cloud computing can not only lessen the cost and constraint for automation and computerization by people and enterprises but can also offered reduced infrastructure maintenance fee, user access and efficient management [7].…”

Section: Importance Of Machine Learning In Ecommerce Domainmentioning

confidence: 99%

Predictive Model for Multiclass Classification of E-Commerce Data: An Azure Machine Learning Approach

Fegade¹,

Gupta²,

Namdeo³

2017

IJCA

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Section: Importance Of Machine Learning In Ecommerce Domainmentioning

confidence: 99%

Predictive Model for Multiclass Classification of E-Commerce Data: An Azure Machine Learning Approach

Fegade¹,

Gupta²,

Namdeo³

2017

IJCA

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“…Additionally, IACRS [25] proposes a multimetric group cloud-ready heuristic algorithm that would deal with compute, network and storage metric statistics simultaneously while performing scaling decisions. InterCloud [26] advocates an effective resource scaling approach seen as to distribute workload appropriately across multiple independent cloud data centers without compromising service quality of service (QoS) aspects.…”

Section: Related Workmentioning

confidence: 99%

OCSO: Off-the-cloud service optimization for green efficient service resource utilization

Fang

Liu

et al. 2014

J Cloud Comp

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Many efforts have been made in optimizing cloud service resource management for efficient service provision and delivery, yet little research addresses how to consume the provisioned service resources efficiently. Meanwhile, typical existing resource scaling management approaches often rest on single monitor category statistics and are driven by certain threshold algorithms, they usually fail to function effectively in case of dealing with complicated and unpredictable workload patterns. Fundamentally, this is due to the inflexibility of using static monitor, threshold and scaling parameters. This paper presents Off-the-Cloud Service Optimization (OCSO), a novel user-side optimization solution which specifically deals with service resource consumption efficiency from the service consumer perspective. OCSO rests on an intelligent resource scaling algorithm which relies on multiple service monitor metrics plus dynamic threshold and scaling parameters. It can achieve proactive and continuous service optimizations for both real-world IaaS and PaaS services, through OCSO cloud service API. From the two series of experiments conducted over Amazon EC2 and ElasticBeanstalk using OCSO prototype, it is demonstrated that the proposed approach can make significant improvement over Amazon native automated service provision and scaling options, regardless of scaling up/down or in/out.

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“…To scale the application in intercloud, to improve the performance in case of resource limitations in cloud computing and to provide the quality of service to the user as per the user service level agreement [27] proposed an architecture for cloud co-coordinator. The proposed co-coordinator enables scaling of application across multiple, independent Cloud data centers, following market-based trading and an extensible design that allows its adoption in different public and private Clouds.…”

Section: B Study On Performance Toolsmentioning

confidence: 99%

Study of Response Time in Cloud Computing

Singh¹

2014

IJIEEB

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Abstract-Performance of a cloud is a critical issue and dependent on various factors that include load balancing used to distribute the load at data center. Proliferation of the cloud paradigm is profoundly dependent on the performance of the cloud. Therefore, to examine the cloud performance, it is significant to identify the factors that govern cloud performance. This work is focused to identify the factors that may have significant impact on the cloud performance. To identify the performance factors, we have reviewed the major publications of the last five years as well as the major real time cloud performance tools offered. Study revealed that response time is the major factor that has the significant impact on cloud computing performance. Finally, we have conducted an experiment to establish the dependency of response time on various load balancing techniques and number of data center. Experiment revealed that the round robin is directly proportional to the number of data center

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A coordinator for scaling elastic applications across multiple clouds

Cited by 96 publications

References 31 publications

Predictive Model for Multiclass Classification of E-Commerce Data: An Azure Machine Learning Approach

Predictive Model for Multiclass Classification of E-Commerce Data: An Azure Machine Learning Approach

OCSO: Off-the-cloud service optimization for green efficient service resource utilization

Study of Response Time in Cloud Computing

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