Determining suitable execution environment based on dynamic workload during early stages of software development life cycle: a simulation approach

Geetha, D. Evangelin; Kumar, T. V. Suresh; Kanth, K. Rajani

doi:10.1504/ijcse.2012.048228

Cited by 2 publications

(3 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Information proxies can be used to reduce dissemination overheads. 9.Mere increase in the number of VMs does not affect the performance in a highly scalable environment.…”

Section: Observationsmentioning

confidence: 99%

See 1 more Smart Citation

Impact of Resource Management and Scalability on performance of Cloud Applications - A Survey

Ganesh¹,

D²,

T³

2016

IJCSEA

View full text Add to dashboard Cite

Cloud computing facilitates service providers to rent their computing capabilities for deploying applications depending on user requirements. Applications of cloud have diverse composition, configuration and deployment requirements. Quantifying the performance of applications in Cloud computing environments is a challenging task. In this paper, we try to identify various parameters associated with performance of cloud applications and analyse the impact of resource management and scalability among them.

show abstract

“…Information proxies can be used to reduce dissemination overheads. 9.Mere increase in the number of VMs does not affect the performance in a highly scalable environment.…”

Section: Observationsmentioning

confidence: 99%

“…Also it was determined that an execution environment can achieve the defined performance goal based on dynamic workload. [9] Network resources is one of the central resources in cloud computing. Chrysa Papagianni et al, discusses allocation of virtual resources and describes that network performance, is key to the cloud performance.…”

Section: Literature Surveymentioning

confidence: 99%

Impact of Resource Management and Scalability on performance of Cloud Applications - A Survey

Ganesh¹,

D²,

T³

2016

IJCSEA

View full text Add to dashboard Cite

show abstract

“…These relationships characterise normal operation and can help detect anomalies. Geetha et al (2012) presented a mathematical model for performance calculation and simulated the model that supports a decision maker involved in performance analysis for a given distributed application.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic-based workload forecasting and service redeployment for multi-tenant services

Liu

et al. 2016

IJHPCN

View full text Add to dashboard Cite

This paper presents a two-stage service migration decision method which combines business workload forecasting with real-time load sensing, and thus adds business forecasting to previous load balancing approaches that rely solely upon real-time load sensing. The migration decision procedure and the detailed causal analysis algorithms based on Bayesian networks are also given. After the critical business indicators have been obtained from causal analysis, business fluctuation related with the critical indicators can be forecasted by using Markov chain method. And then, the migration decision can be made based on the forecasting results and the real-time load information together. We evaluate the migration decision method through three sets of experiments. We found that by migrating service on a shared multi-tenant service environment, the QoS requirement can be assured dynamically and the capability of workloads increases under same resource cost, which is helpful in optimised deploying for multi-tenant applications. include service computing and data engineering. This paper is a revised and expanded version of a paper entitled 'Probabilistic-based workload forecasting and service redeployment for multi-tenant services' presented at The

show abstract

Determining suitable execution environment based on dynamic workload during early stages of software development life cycle: a simulation approach

Cited by 2 publications

References 32 publications

Impact of Resource Management and Scalability on performance of Cloud Applications - A Survey

Impact of Resource Management and Scalability on performance of Cloud Applications - A Survey

Probabilistic-based workload forecasting and service redeployment for multi-tenant services

Contact Info

Product

Resources

About