A Resource Utilization Prediction Model for Cloud Data Centers Using Evolutionary Algorithms and Machine Learning Techniques

Malik, Sania Abdul; Tahir, Muhammad; Sardaraz, Muhammad; Alourani, Abdullah

doi:10.3390/app12042160

Cited by 42 publications

(26 citation statements)

References 28 publications

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“…This modal cannot locate the grouped data. Malik et al [18] promoted the use of machine learning techniques and functional link neural networks to build efficient systems for predicting customer multiresource cloud data centre consumption. To solve the under-and overprovisioning problems, overprovisioning of services results in higher expenses and increased energy use.…”

Section: Literature Surveymentioning

confidence: 99%

Consumption Behavior Prediction Based on Multiobjective Evolutionary Algorithm

Jaharudin

Song

2022

Journal of Sensors

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Consumption behavior prediction reveals customer attributes, personal preferences, and intrinsic laws. Organizations would benefit from knowing further about customer needs and business desires by monitoring client behavior to provide more precise recommendations and boost acquisition rates. The economics of the customer, buyer groupings, and product quality are only a few of the numerous variables that influence customer behavior. The key issue that has to be resolved at this time is how to filter out useful information from these vast amounts of data to forecast customer behavior. For customer consumption behavior prediction and analysis with an advanced quantitative research process, we proposed the multiobjective evolutionary algorithm, which significantly boosts the accuracy of consumption behavior predictions. The dataset is initially gathered based on consumer preferences and behaviors as the essential information for the entire prediction model. Min-max normalization is used as a component of the preprocessing of the data to get the elimination of redundant and superfluous data. The Word2vec model is utilized for feature extraction, and boosted ant colony optimization (BACO) is employed to choose the best features. Utilizing the suggested multiobjective evolutionary algorithm (MOEA), the predictions are made. The suggested system’s performance is assessed, and the metrics are contrasted with more established methods. The findings demonstrate that the suggested MOEA technique performs well than the traditional ML, XGB, AI, and HNB algorithm methods in terms of accuracy (95 percent), quality of prediction (97 percent), precision (99 percent), recall (93 percent), F 1 -score (98 percent), and prediction time (50 seconds). Hence, the outcomes show that the regression model is sustainable. The suggested consumption behavior prediction system has demonstrated its efficiency in boosting profitability.

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Section: Literature Surveymentioning

confidence: 99%

Consumption Behavior Prediction Based on Multiobjective Evolutionary Algorithm

Jaharudin

Song

2022

Journal of Sensors

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“…S. Malik et al (2022) [64], proposed Evolutionary Algorithms and Machine Learning Methods to Predict Resource Utilization in cloud data centers. The primary goal was to resolve the over-and under-provisioning problems.…”

Section: Virtual Machine Management Using Metaheuristic Methodsmentioning

confidence: 99%

A systematic review on effective energy utilization management strategies in cloud data centers

2022

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Data centers are becoming considerably more significant and energy-intensive due to the exponential growth of cloud computing. Cloud computing allows people to access computer resources on demand. It provides amenities on the pay-as-you-go basis across the data center locations spread over the world. Consequently, cloud data centers consume a lot of electricity and leave a proportional carbon impact on the environment. There is a need to investigate efficient energy-saving approaches to reduce the massive energy usage in cloud servers. This review paper focuses on identifying the research done in the field of energy consumption (EC) using different techniques of machine learning, heuristics, metaheuristics, and statistical methods. Host CPU utilization prediction, underload/overload detection, virtual machine selection, migration, and placement have been performed to manage the resources and achieve efficient energy utilization. In this review, energy savings achieved by different techniques are compared. Many researchers have tried various methods to reduce energy usage and service level agreement violations (SLAV) in cloud data centers. By using the heuristic approach, researchers have saved 5.4% to 90% of energy with their proposed methods compared with the existing methods. Similarly, the metaheuristic approaches reduce energy consumption from 7.68% to 97%, the machine learning methods from 1.6% to 88.5%, and the statistical methods from 5.4% to 84% when compared to the benchmark approaches for a variety of settings and parameters. So, making energy use more efficient could cut down the air pollution, greenhouse gas (GHG) emissions, and even the amount of water needed to make power. The overall outcome of this review work is to understand different methods used by researchers to save energy in cloud data centers.

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“…In [16], the authors explore the efficiency of neural networks to predict multi-resource utilization. They propose a model which uses a hybrid Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to train network weights and uses a Functional Link Neural Network (FLNN) for prediction (a few minutes ahead).…”

Section: Cloud Resource Usage Planningmentioning

confidence: 99%

Anomaly detection in the context of long-term cloud resource usage planning

Nawrocki

Sus

2022

Knowl Inf Syst

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This paper describes a new approach to automatic long-term cloud resource usage planning with a novel hybrid anomaly detection mechanism. It analyzes existing anomaly detection solutions, possible improvements and the impact on the accuracy of resource usage planning. The proposed anomaly detection solution is an important part of the research, since it allows greater accuracy to be achieved in the long term. The proposed approach dynamically adjusts reservation plans in order to reduce the unnecessary load on resources and prevent the cloud from running out of them. The predictions are based on cloud analysis conducted using machine learning algorithms, which made it possible to reduce costs by about 50%. The solution was evaluated on real-life data from over 1700 virtual machines.

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A Resource Utilization Prediction Model for Cloud Data Centers Using Evolutionary Algorithms and Machine Learning Techniques

Cited by 42 publications

References 28 publications

Consumption Behavior Prediction Based on Multiobjective Evolutionary Algorithm

Consumption Behavior Prediction Based on Multiobjective Evolutionary Algorithm

A systematic review on effective energy utilization management strategies in cloud data centers

Anomaly detection in the context of long-term cloud resource usage planning

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