An Efficient Predictive technique to Autoscale the Resources for Web applications in Private cloud

Radhika, E. G.; Sadasivam, G. Sudha; Naomi, J. Fenila

doi:10.1109/aeeicb.2018.8480899

Cited by 14 publications

(11 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In such cases, SVM and Linear Regression could perform poorly. EG Radhika et al showed how Auto-Regressive Integrated Moving Average (ARIMA) and Recurrent Neural Network Long Short Term Memory (RNN-LSTM) techniques are used for predicting the future workload [2]. Furthermore, the RNN-LSTM deep learning approach had the lowest error rate when evaluating the performance metrics of the two techniques.…”

Section: B Proactive Scaling Trendsmentioning

confidence: 99%

“…The predictor analyzes history with the help of the following techniques: (1) double exponential smoothing -DES, (2) weighted moving average -WMA, and (3) Fibonacci numbers. However, all such approaches [2], [4], [5] used simulated workloads or utilized open-source applications like RUBiS [21], RUBBoS [22], and Olio [23], which do not reflect actual user interaction compared to utilizing industrial applications.…”

Section: B Proactive Scaling Trendsmentioning

confidence: 99%

“…Service elasticity is the capability to automatically provide and de-provide resources to meet the present service demand as nearly as feasible at each moment to adjust the system to workload changes [1]. In addition, service elasticity reduces power consumption due to its avoidance of over-provisioning resources [2]. Service elasticity is a concept adopted by most service providers to satisfy peak demand periods and guarantee Quality of Service (QoS) to the users during the service lifetime.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Proactive Auto-Scaling Approach of Production Applications Using an Ensemble Model

2023

View full text Add to dashboard Cite

The resource usage behaviors of application workloads are currently the primary concern of cloud providers offering hosting services. These services should be able to adapt to workload changes by automatically provisioning and de-provisioning resources so that, at all times, the existing resources in a system match the current service demand. Such behavior can be achieved manually by hiring a DevOps team to manage the application's resources. Another option would be automating the resource provisioning processing using automated rules. Once such rules are met, the hosting environment will scale the resources accordingly. However, managing a DevOps team or creating flaky rules can lead to overscaling application resources. This work proposes a new approach: a proactive auto-scaling framework built on an ensemble model. Such a model utilizes several machine learning techniques to scale application resources to match resource demand before the need arises. We evaluated our solution against three real production applications hosted on Cegedim Cloud Hosting Environment, an industrial environment serving several cloud applications from various domains, and against other machine learning models used in similar proactive auto-scaling experiments mentioned in past work. The experimentation results show that predicting application resources like CPU or RAM is feasible. Moreover, even in production environments, our ensemble model performs optimally in the CPU case and is near the optimal model when predicting RAM resources.

show abstract

Section: B Proactive Scaling Trendsmentioning

confidence: 99%

Section: B Proactive Scaling Trendsmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Proactive Auto-Scaling Approach of Production Applications Using an Ensemble Model

2023

View full text Add to dashboard Cite

show abstract

“…There have been several efforts for building predictive autoscaling methods using machine learning techniques. For example, Radhika et al [25] presents a predictive autoscaling method which uses a recurrent neural network (RNN) and autoregressive integrated moving average (ARIMA) to predict the CPU and memory usage and autoscale the application resources based on the prediction. Maryam et al [26] presents a survey on the predictive autoscaling methods and discuss the different machine learning algorithms used by the researcher for the dynamic resources provisioning.…”

Section: Related Workmentioning

confidence: 99%

Burst-Aware Predictive Autoscaling for Containerized Microservices

Abdullah

Iqbal

Berral

et al. 2022

IEEE Trans. Serv. Comput.

View full text Add to dashboard Cite

Autoscaling methods are used for cloud-hosted applications to dynamically scale the allocated resources for guaranteeing Qualityof-Service (QoS). The public-facing application serves dynamic workloads, which contain bursts and pose challenges for autoscaling methods to ensure application performance. Existing State-of-the-art autoscaling methods are burst-oblivious to determine and provision the appropriate resources. For dynamic workloads, it is hard to detect and handle bursts online for maintaining application performance. In this paper, we propose a novel burst-aware autoscaling method which detects burst in dynamic workloads using workload forecasting, resource prediction, and scaling decision making while minimizing response time service-level objectives (SLO) violations. We evaluated our approach through a trace-driven simulation, using multiple synthetic and realistic bursty workloads for containerized microservices, improving performance when comparing against existing state-of-the-art autoscaling methods. Such experiments show an increase of ×1.09 in total processed requests, a reduction of ×5.17 for SLO violations, and an increase of ×0.767 cost as compared to the baseline method.

show abstract

“…Recently, several researchers designed and evaluated predictive autoscaling methods. For example, Radhika et al [34] present the predictive autoscaling method. The authors use Auto-Regressive Integrated Moving Average (ARIMA) and Recurrent Neural Network-Long Short Term Memory (RNN-LSTM) to predict the future workload using historical CPU and memory utilization and then scale the application resources.…”

Section: Related Workmentioning

confidence: 99%

Online Workload Burst Detection for Efficient Predictive Autoscaling of Applications

et al. 2020

View full text Add to dashboard Cite

Autoscaling methods are employed to ensure the scalability of cloud-hosted applications. The public-facing applications are prone to receive sudden workload bursts, and the existing autoscaling methods do not handle the bursty workloads gracefully. It is challenging to detect the burst online from the incoming dynamic workload traffic, and then identifying appropriate resources to address the burst without overprovisioning is even harder. In this paper, we address this challenge by investigating the appropriate method for online burst detection and then proposed a novel predictive autoscaling method to use burst detection for satisfying specific response time requirements. We compared the proposed method with multiple state-of-the-art baseline autoscaling methods under multiple realistic and synthetic bursty workloads for a benchmark application. Our experimental results show a 60.8% average decrease in response time violations as compared to the baseline method. INDEX TERMS Autoscaling, predictive, SLO violations, response time, workload, burstiness, online burst detection.

show abstract

An Efficient Predictive technique to Autoscale the Resources for Web applications in Private cloud

Cited by 14 publications

References 2 publications

Proactive Auto-Scaling Approach of Production Applications Using an Ensemble Model

Proactive Auto-Scaling Approach of Production Applications Using an Ensemble Model

Burst-Aware Predictive Autoscaling for Containerized Microservices

Online Workload Burst Detection for Efficient Predictive Autoscaling of Applications

Contact Info

Product

Resources

About