RPPS: A Novel Resource Prediction and Provisioning Scheme in Cloud Data Center

Fang, Wei; Lu, Zhihui; Wu, Jie; Cao, ZhenYin

doi:10.1109/scc.2012.47

Cited by 104 publications

(60 citation statements)

References 13 publications

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“…Fang et al [57] found it useful to predict the future CPU usage of VMs. However, they remark the computational cost of this technique, that includes the choice of p and q, the estimation of the coefficients of each term and other parameters.…”

Section: Review Of Proposalsmentioning

confidence: 99%

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A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments

2014

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Cloud computing environments allow customers to dynamically scale their applications. The key problem is how to lease the right amount of resources, on a pay-as-you-go basis. Application redimensioning can be implemented effortlessly, adapting the resources assigned to the application to the incoming user demand. However, the identification of the right amount of resources to lease in order to meet the required Service Level Agreement, while keeping the overall cost low, is not an easy task. Many techniques have been proposed for automating application scaling. We propose a classification of these techniques into five main categories: static thresholdbased rules, control theory, reinforcement learning, queuing theory and time series analysis. Then we use this classification to carry out a literature review of proposals for auto-scaling in the cloud.

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Section: Review Of Proposalsmentioning

confidence: 99%

“…They use a polynomial regression to estimate the expected number of requests for the next interval. Fang et al [57] focus on vertical scaling (CPU and memory) for regular changes in workload, whereas horizontal scaling is applied in order to handle sudden spikes and flash crowds.…”

Section: Review Of Proposalsmentioning

confidence: 99%

A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments

2014

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“…Besides, horizontal scaling can allow for much higher throughput albeit possibly at higher cost. (Fang et al, 2012) describe vertical scaling as a methodology most appropriate for regular adaptations to workload, whereas horizontal scaling is more appropriate in cases of sharp workload changes.…”

Section: Scale Up -Scale Downmentioning

confidence: 99%

Challenges and Research Directions in Big Data-driven Cloud Adaptivity

Τσαγκαρόπουλος

Papageorgiou

Apostolou

et al. 2018

Proceedings of the 8th International Conference on Cloud Computing and Services Science

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Abstract:Mainstream cloud technologies are challenged by real-time, big data processing requirements or emerging applications. This paper surveys recent research efforts on advancing cloud computing virtual infrastructures and real-time big data technologies in order to provide dynamically scalable and distributed architectures over federated clouds. We examine new methods for developing cloud systems operating in a real-time, big data environment that can sense the context of the application environment and can adapt the infrastructure accordingly. We describe research topics linked to the challenge of adaptivity such as situation awareness, context detection, service-level objectives, and the capability to predict extraordinary situations requiring remedying action. We also describe research directions for realising adaptivity in cloud computing and we present a conceptual framework that represents research directions and shows interrelations.

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“…1). NN and LR have been widely explored by several authors in building prediction models [13], [11], [19], and [9]. Recently SVM, a powerful classification technique [11] has been gaining significant popularity in time series and regression prediction [8], [10], and [14].…”

Section: Methodology and Toolsmentioning

confidence: 99%

“…In this work, focus will be on VM Provisioning. In trying to meet up with both client Service Level Agreement (SLA) for Quality of Service (QoS) and their own operating cost, cloud providers are faced with the challenges of underprovisioning and over-provisioning Under-provisioning often leads to SLA penalty resulting in revenue loss on the part of the cloud providers [7], [19], [20] and also a poor Quality of Experience (QoE) for the cloud clients. On the other hand, over-provisioning can lead to excessive energy consumption, culminating in high operating cost and waste of resources [7], [19], [20]; though this has no negative impact on the client.…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning Algorithms for Cloud Client Prediction Models in a Web VM Resource Provisioning Environment

Ajila

Bankole

2016

TMLAI

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In order to meet Service Level Agreement (SLA) requirements, efficient scaling of Virtual Machine (VM) resources in cloud computing needs to be provisioned ahead due to the instantiation time required by the VM. One way to do this is by predicting future resource demands. The existing research on VM resource provisioning are either reactive in their approach or use only non-business level metrics. In this research, a Cloud client prediction model for TPC-W benchmark web application is developed and evaluated using three machine learning techniques: Support Vector Regression (SVR), Neural Networks (NN) and Linear Regression (LR). Business level metrics for Response Time and Throughput are included in the prediction model with the aim of providing cloud clients with a more robust scaling decision choice. Results and analysis from the experiments carried out on Amazon Elastic Compute Cloud (EC2) show that Support Vector Regression provides the best prediction model for random-like workload traffic pattern.

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RPPS: A Novel Resource Prediction and Provisioning Scheme in Cloud Data Center

Cited by 104 publications

References 13 publications

A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments

A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments

Challenges and Research Directions in Big Data-driven Cloud Adaptivity

Using Machine Learning Algorithms for Cloud Client Prediction Models in a Web VM Resource Provisioning Environment

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