CPU Load Prediction Using Support Vector Regression and Kalman Smoother for Cloud

Hu, Rongdong; Jiang, Jingfei; Liu, Guangming; Wang, Lixin

doi:10.1109/icdcsw.2013.60

Cited by 19 publications

(7 citation statements)

References 10 publications

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“…Gaussian Process Regression [227] has been proposed to predict the future workload of the tasks, allowing the deployment of new, delay sensitive applications and reducing energy consumption, blocking of requests and latency. The dynamic characteristics of applications and the complex Edge/Cloud Computing environment, have been modeled with the Support Vector Regressor [228] and the K-Nearest Neighbor Regressor [229] for future load prediction and energy efficient utilization of Edge servers respectively.…”

Section: Machine Learningmentioning

confidence: 99%

Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

et al. 2021

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Next generation communication networks are expected to accommodate a high number of new and resource-voracious applications that can be offered to a large range of end users. Even though end devices are becoming more powerful, the available local resources cannot cope with the requirements of these applications. This has created a new challenge called task offloading, where computation intensive tasks need to be offloaded to more resource powerful remote devices. Naturally, the Cloud Computing is a well-tested infrastructure that can facilitate the task offloading. However, Cloud Computing as a centralized and distant infrastructure creates significant communication delays that cannot satisfy the requirements of the emerging delay-sensitive applications. To this end, the concept of Edge Computing has been proposed, where the Cloud Computing capabilities are repositioned closer to the end devices at the edge of the network. This paper provides a detailed survey of how the Edge and/or Cloud can be combined together to facilitate the task offloading problem. Particular emphasis is given on the mathematical, artificial intelligence and control theory optimization approaches that can be used to satisfy the various objectives, constraints and dynamic conditions of this end-to-end application execution approach. The survey concludes with identifying open challenges and future directions of the problem at hand.

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Section: Machine Learningmentioning

confidence: 99%

Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

et al. 2021

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“…We have compared the proposed model with other benchmark models for validation. For Performance validation, we have chosen Bayesian [16], artificial neural network (ANN), and support vector machine (SVM) [17]. Error value comparison is shown in Fig.…”

Section: Evaluation Of Resultsmentioning

confidence: 99%

A Novel Feature Extraction Model for Large-Scale Workload Prediction in Cloud Environment

Shishira

Kandasamy

2021

SN COMPUT. SCI.

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In an enterprise cloud environment, it is difficult to handle an extensive number of loads. Serving the request in very less time leads to resource allocation problem. It is better to have prior knowledge of the incoming loads to auto-scale the resources. A novel architecture is proposed for the better prediction of workloads in the cloud environment. The proposed feature extraction model considers three essentials for managing cloud resources, i.e., CPU, Disk, and Memory. The model with the very nominal error achieved an accuracy of 98.72%. The proposed model is contrasted with other conventional predictive models for validation.

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“…In the IAAS cloud environment, some of the authors have used the ARIMA model [17] to predict the future application workload behavior to calculate the required VM configuration in the data center. Hu et al [18] developed a multi-step ahead CPU load prediction method based on the support vector regression and Kalman smoothing technique. Jiang et al [19] addressed VM types and request timestamps.…”

Section: Related Workmentioning

confidence: 99%

Time series-based workload prediction using the statistical hybrid model for the cloud environment

Devi

Valli²

2022

Computing

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Resource management is addressed using infrastructure as a service. On demand, the resource management module effectively manages available resources. Resource management in cloud resource provisioning is aided by the prediction of central processing unit (CPU) and memory utilization. Using a hybrid ARIMA-ANN model, this study forecasts future CPU and memory utilization. The range of values discovered is utilized to make predictions, which is useful for resource management. In the cloud traces, the ARIMA model detects linear components in the CPU and memory utilization patterns. For recognizing and magnifying nonlinear components in the traces, the artificial neural network (ANN) leverages the residuals derived from the ARIMA model. The resource utilization patterns are predicted using a combination of linear and nonlinear components. From the predicted and previous history values, the Savitzky-Golay filter finds a range of forecast values. Point value forecasting may not be the best method for predicting multi-step resource utilization in a cloud setting. The forecasting error can be decreased by introducing a range of values, and we employ as reported by Engelbrecht HA and van Greunen M (in: Network and Service Management (CNSM), 2015 11th International Conference, 2015) OER (over estimation rate) and UER (under estimation rate) to cope with the error produced by over or under estimation of CPU and memory utilization. The prediction accuracy is tested using statistical-based analysis using Google's 29-day trail and BitBrain (BB).

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CPU Load Prediction Using Support Vector Regression and Kalman Smoother for Cloud

Cited by 19 publications

References 10 publications

Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions

A Novel Feature Extraction Model for Large-Scale Workload Prediction in Cloud Environment

Time series-based workload prediction using the statistical hybrid model for the cloud environment

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