An Approach for Dynamic Scaling of Resources in Enterprise Cloud

Kanagala, Kartheek; Sekaran, K. Chandra

doi:10.1109/cloudcom.2013.167

Cited by 12 publications

(9 citation statements)

References 8 publications

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“…In [14] and [17], if workload goes across the upper threshold for a pre-specified duration, they start to scale up. During this period, Service Level Agreement (SLA) will be violated and some penalty has to be incurred by providers.…”

Section: B Evaluation Resultsmentioning

confidence: 99%

“…Performances of the proposed algorithms have been compared with two auto-scaling approaches, which are proposed by Kanagala et al [14], and Hasan et al [17].…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Kanagala and Sekaran [14] have proposed Thresholdbased auto-scaling approach, which minimizes violation of service level agreement by considering virtual machine turnaround time and virtual machine stabilization time during adapting thresholds. Thresholds are dynamically specified by using double exponential smoothing.…”

Section: (Ijacsa) International Journal Of Advancedmentioning

confidence: 99%

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Automatic Cloud Resource Scaling Algorithm based on Long Short-Term Memory Recurrent Neural Network

Shahin¹

2016

ijacsa

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Abstract-Scalability is an important characteristic of cloud computing. With scalability, cost is minimized by provisioning and releasing resources according to demand. Most of current Infrastructure as a Service (IaaS) providers deliver thresholdbased auto-scaling techniques. However, setting up thresholds with right values that minimize cost and achieve Service Level Agreement is not an easy task, especially with variant and sudden workload changes. This paper has proposed dynamic threshold based auto-scaling algorithms that predict required resources using Long Short-Term Memory Recurrent Neural Network and auto-scale virtual resources based on predicted values. The proposed algorithms have been evaluated and compared with some of existing algorithms. Experimental results show that the proposed algorithms outperform other algorithms.

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Section: B Evaluation Resultsmentioning

confidence: 99%

“…Performances of the proposed algorithms have been compared with two auto-scaling approaches, which are proposed by Kanagala et al [14], and Hasan et al [17].…”

Section: Performance Evaluationmentioning

confidence: 99%

Section: (Ijacsa) International Journal Of Advancedmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Cloud Resource Scaling Algorithm based on Long Short-Term Memory Recurrent Neural Network

Shahin¹

2016

ijacsa

View full text Add to dashboard Cite

show abstract

“…Several prediction techniques have been used to predict cloud resource provisioning. However, most of current approaches do not consider multi-seasonality in cloud workload, and most of them use prediction techniques that do not have ability to model more than one seasonal cycle [4][9][10] [11].…”

Section: Related Workmentioning

confidence: 99%

“…Many researchers have exploited Exponential smoothing methods to predict future resource provisioning for cloud computing applications [4] [5]. However, most of them have used double exponential smoothing, which cannot model workloads if there are seasonalities.…”

Section: Introductionmentioning

confidence: 99%