Cloud client prediction models for cloud resource provisioning in a multitier web application environment

Bankole, Akindele

doi:10.22215/etd/2013-06383

Cited by 10 publications

(17 citation statements)

References 31 publications

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“…Authors in [12] followed scaling decision making based on the proactive behavior using a learner machine; they considered VM behavior as well as SLA criteria. They [13] expanded their experiments with a more intense workload and a wider timeframe and the previous results were repeated; they only used proactive methods.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, the proactive methods faces its own challenges. Some studies [12,13] which have been carried out solely based on predictions face the problem of multiple incorrect decisions.…”

Section: Introductionmentioning

confidence: 99%

“…In the cloud environment, this limitation is known as service level agreement (SLA) which is signed between ASP and CSP after negotiating on QoS. Therefore, some studies [6,[11][12][13] have considered the parameter of response time. In [6], resource utilization level is considered as the QoS parameter, and we used this parameter in our categories of analyzers.…”

Section: Introductionmentioning

confidence: 99%

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SLA-aware resource allocation for application service providers in the cloud

Aslanpour

Dashti

2016

2016 Second International Conference on Web Research (ICWR)

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With the advent of cloud computing and the possibility of renting virtual resources on an as-needed basis, application services providers (ASPs) entered the realm of information technology in order to provide web-based applications. They rent virtual machines from cloud service providers in order to host their applications; however, it is possible that due to the dynamic nature of the workload, these resources may not be sufficient to meet the real needs of the ASP. In this point, the issue of automatic optimal resource allocation scaling is raised. With the aid of the scalability property of cloud computing, this issue can be improved in order to decrease the costs and ensure the quality of service. Researchers try to resolve this issue using reactive and proactive methods.In this study, a new framework is proposed which carries out automatic optimal resource allocation with the help of a combinatory method based on reactive and proactive scaling which is sensitive to past and present fluctuations. In this framework, the possibility of supervising resources is provided through two analyzers: the behavior of the virtual machine and the SLA behavior. The parameters affecting our two-sided decision making include virtual machine's CPU utilization and the response time. In order to implement the proposed framework, the user requests for a website with a synthetic workload based on the real pattern of cloud environment were enumerated using CloudSim tool. The results show that compared to the one-sided methods, our proposed two-sided method is able to improve the costs of resource provision, the quality of service, scaling decision making overhead, and the resource utilization fluctuation control. This scaling mechanism can pave the way for vertical scaling as well as the utilization of Amazon's Spot machines.

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Section: Related Workmentioning

confidence: 99%

“…Therefore, the proactive methods faces its own challenges. Some studies [12,13] which have been carried out solely based on predictions face the problem of multiple incorrect decisions.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SLA-aware resource allocation for application service providers in the cloud

Aslanpour

Dashti

2016

2016 Second International Conference on Web Research (ICWR)

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“…Although the authors consider SaaS services, the proposed model only considers IaaS services prediction to estimate the number of services needed based on users' requests. In [4], a predictive approach to cloud resources (CPU, Storage, memory, etc) is presented to help scaling decisions ahead of time. This helps predicting future demands on resources and compensate for the delay in starting up VMs.…”

Section: Related Workmentioning

confidence: 99%

“…response time, reliability, availability) are the most typical non-functional requirements that play a major role in determining the best offered services [1]. Several important work tackle the problem of cloud services selection in recent years [2,3,4]. These work focus on a single cloud layer or service model (e.g.…”

Section: Introductionmentioning

confidence: 99%

End-to-End Performance Prediction for Selecting Cloud Services Solutions

Karim¹,

Miri

2015

2015 IEEE Symposium on Service-Oriented System Engineering

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In cloud computing, in order to select or recommend the best service solutions to end users, the end-toend QoS requirements (e.g. response time and throughput) have to be computed. A typical cloud solution is a combination of multiple component services such as IaaS, SaaS, PaaS, etc. In a simplified case, there could be two components-software services and infrastructure services. The software service alone can satisfy end user's functional requirements (e.g. business objectives); however, the end-to-end QoS requirements require a collaboration of the multiple components at multiple cloud layers. In this paper, we consider the multilayered cloud architecture for computing the end-to-end performance values for cloud solutions. We propose a new method for measuring cloud component services similarity and predicting the end-toend performance values of cloud solutions. In this method, the historical performance data of cloud component services is used based on users' past invocations. To evaluate our method and show its effectiveness, series of experiments are conducted. The experimental results demonstrate that our cloud multilayers based method produces better prediction accuracy than other prediction approaches that consider one cloud layer.Services' providers tend to claim their services' QoS values when publishing their services. However, in a cloud solution, performances of individual component services vary over time due to different reasons such as the nature of the hosting infrastructure services, the communication environment (traffic congestion) and the case of multitasking VMs. As a result, end users may perceive different values from the ones claimed by services' providers.A more robust approach to solve the problem is to predict the end-to-end performance values of cloud solutions for end users. Using a prediction approach, we can exploit the available historical data to compute the end-to-end perfroamnce. The performance of a cloud solution is predicted by analyzing the behaviors of combinations of cloud component services in terms of their performance at multiple layers. For instance, if two SaaS services are

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FAHP approach for autonomic resource provisioning of multitier applications in cloud computing environments

2018

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Recent advancements in web-based application, especially in cloud computing environment, allows cloud service providers to deploy and provide web-based services in the form of multitier applications. One of the most suitable infrastructure for the running of multitier applications is a cloud computing infrastructure. Since the arrival rate of users to the multitier applications varies over the time, deciding about the right amount of resources required to handle the each tier of multitier applications is not trivial, and it depends on the current workload of its each tier. Therefore, it is necessary to automatically provision resources to deal with fluctuating demands of the multitier applications. In this paper, we propose a hybrid resource provisioning approach for multitier applications based on a combination of the concept of the autonomic computing and the fuzzy analytical hierarchy process approach. Moreover, we present a framework based on MAPE-k control loop for autonomous resource provisioning of multitier applications in cloud computing environments. The effectiveness of the proposed approach under real and synthetic workloads was evaluated. The experimental results indicate that the proposed solution outperforms in terms of allocated virtual machines, response time, and cost compared with the other approaches. KEYWORDS autonomic computing, cloud computing, FAHP, multitier applications, resource provisioning, workload prediction INTRODUCTIONCloud computing is one of the most popular technologies in the businesses, educational institutions, governments, and the research community that has become an integral part of many users are heavily dependent on cloud-based applications for their day-to-day activities in both professional and personal lives. 1 Recent advancements in the web-based application, especially in cloud computing environment, allows cloud service providers to deploy and provide web-based services in the form of multitier applications. As the cloud is elastic, it can be used in places where the varying load is one of the main characteristics. [2][3][4] It can also be used where on-demand access is required. Similarly, because of its property of multitenancy, it can be used in places where several applications are to be operated. 5,6 Therefore, one of the most suitable infrastructures for the running of multitier applications is cloud computing infrastructures. On the other hand, since the arrival rate of users to the multitier applications varies over the time, deciding about the right amount of resources Softw Pract Exper. 2018;48:2147-2173.wileyonlinelibrary.com/journal/spe

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Cloud client prediction models for cloud resource provisioning in a multitier web application environment

Cited by 10 publications

References 31 publications

SLA-aware resource allocation for application service providers in the cloud

SLA-aware resource allocation for application service providers in the cloud

End-to-End Performance Prediction for Selecting Cloud Services Solutions

FAHP approach for autonomic resource provisioning of multitier applications in cloud computing environments

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