Scalability and Performance of Web Applications in a Compute Cloud

Chieu, T. C.; Mohindra, Ajay; Karve, Alexei

doi:10.1109/icebe.2011.63

Cited by 38 publications

(22 citation statements)

References 4 publications

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“…Another approach is to use a direct metric as the indicator when performing the auto scaling [13][14] [15]. One SLA-driven system [14] requires only the setting of a request response time between a load balancer and application servers.…”

Section: Related Workmentioning

confidence: 99%

Auto-Scaling Mechanism for Cloud Resource Management Based on Client-Side Turnaround Time

Liu

Yuan

Luo

et al. 2015

Advances in Intelligent Systems and Computing

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Currently, providers of Software as a service (SaaS) can use Infrastructure as a Service (IaaS) to obtain the resources required for serving customers. SaaS providers can save substantially on costs by using resource-management techniques such as auto scaling. However, in most current auto-scaling methods, server-side system information is used for adjusting the amount of resources, which does not allow the overall service performance to be evaluated. In this paper, a novel auto-scaling mechanism is proposed for ensuring the stability of service performance from the client-side of view. In the proposed mechanism, turnaround time monitors are deployed as clients outside the service, and the information collected is used for driving a dynamic auto-scaling operation. A system is also designed to support the proposed auto scaling mechanism. The results of experiments show that using this mechanism, stable service quality can be ensured and, moreover, that a certain amount of quality variation can be handled in order to allow the stability of the service performance to be increased.

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Section: Related Workmentioning

confidence: 99%

Auto-Scaling Mechanism for Cloud Resource Management Based on Client-Side Turnaround Time

Liu

Yuan

Luo

et al. 2015

Advances in Intelligent Systems and Computing

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“…[13] propose a set of reactive rules based on the number of active sessions. [12] extends the previous work following the RightScale approach: if all VMs have active sessions above the given upper threshold, a new VM is provisioned; if there are VMs with active sessions below a given lower threshold and with at least one VM that has no active session, the idle one will be shut down. Some work such as [9] and [25] use adaptive techniques to dynamically define threshold.…”

Section: Related Workmentioning

confidence: 99%

Combining time series prediction models using genetic algorithm to autoscaling Web applications hosted in the cloud infrastructure

Messias¹,

Estrella²,

Ehlers³

et al. 2015

Neural Comput & Applic

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In a cloud computing environment, companies have the ability to allocate resources according to demand. However, there is a delay that may take minutes between the request for a new resource and it is ready for using. This causes the reactive techniques, which request a new resource only when the system reaches a certain load threshold, are not suitable for the resource allocation process. To address this problem, it is necessary to predict requests that arrive at the system in the next period of time to allocate the necessary resources, before the system becomes overloaded. There are several time-series forecasting models to calculate the workload predictions based on history of monitoring data. However, it is difficult to know which is the best time series forecasting model to be used in each case. The work becomes even more complicated when the user does not have much historical data to be analyzed. Most related work, considers only single methods to evaluate the results of the forecast. Other work propose an approach that selects suitable forecasting methods for a given context. But in this case, it is necessary to have a significant amount of data to train the classifier. Moreover, the best solution may not be a specific model, but rather a combination of models. In this paper we propose an adaptive prediction method using genetic algorithms to combine time-series forecasting models. Our method does not require a previous phase of training, because it constantly adapts the extent to which the data is coming. To evaluate our proposal we use three logs extracted from real web servers. The results show that our proposal often brings the best result, and is generic enough to adapt to various types of time series.

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“…In [4], the authors present the important scalability issue of performance indicators. They present a case study on the scalability and performance of web applications in the cloud.…”

Section: Scalability Of Web Applications In a Compute Cloudmentioning

confidence: 99%

“…The scalability enhanced by scalability assuring schemes such as adding various resources should be proportional to the cost to apply the schemes." Another definition state that [4] "Scalability is the ability of an application to be scaled up to meet demand through replication and distribution of requests across a pool or farm of servers. "…”

Section: Cloud Scalability Issuesmentioning

confidence: 99%