The cloud computing load forecasting algorithm based on wavelet support vector machine

Zhong, Wei; Zhuang, Yi; Sun, Jian; Gu, Jun

doi:10.1145/3014812.3014852

Cited by 11 publications

(6 citation statements)

References 12 publications

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“…Similarly, another research by Zubairu et al on the acceptance and awareness of cloud computing in Nigeria found that the library management system is the most useful area in academic libraries that utilizes cloud computing, followed by the social network [66]. Another research also proposed a load forecast model to support cloud computing transactions across all data centres that allow seamless cloud operations for all adopters of the cloud technology [43].…”

Section: Overview Of Cloud Computing Adoption In Academic Libraries O...mentioning

confidence: 99%

Exploring the empirical studies of cloud computing adoption in anglophone West African countries’ academic libraries: a review

Yakubu

Kassim²,

Husin

2023

International Conference on Mathematical and Statistical Physics, Computational Science, Education, and Communication (ICMSCE 2

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Cloud computing offer an important role in almost all online transactional activities that involve a substantial exchange of information. However, studies have revealed that despite the important roles of cloud computing, most libraries in higher educational institutions in some West African Countries have not adopted the technology. Additionally, existing empirical studies on the adoption of cloud computing in English-speaking countries of West African academic libraries are low and vary from one country to another as well as from one geo-political region to the other. This necessitates the need to explore the existing empirical studies which were carried out on the adoption of cloud computing level and its impact in academic libraries in West Africa such as Ghana, Gambia, Sierra Leone, Liberia and Nigeria. Several online academic databases were used as sources of data for this study using relevant keywords. As such, this study reviewed the existing empirical research on cloud computing acceptance within West Africa which revealed varying results concerning cloud computing acceptance in academic libraries. results revealed that the majority of the existing studies of cloud computing in academic libraries were found to be focused in Nigeria, and the findings further revealed that the Southern and Eastern parts of Nigeria have more studies compared to the other parts. Based on this outcome, this study recommends additional empirical study to be conducted on cloud computing adoption among the Anglophone Countries of West African academic libraries, as the factors for technology adoption may likely differ from one area to the other.

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Section: Overview Of Cloud Computing Adoption In Academic Libraries O...mentioning

confidence: 99%

Exploring the empirical studies of cloud computing adoption in anglophone West African countries’ academic libraries: a review

Yakubu

Kassim²,

Husin

2023

International Conference on Mathematical and Statistical Physics, Computational Science, Education, and Communication (ICMSCE 2

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“…Classification and regression are other divisions of supervised learning [23]. Supervised ML allows the collection or production of data output from previous experience and helps optimize performance criteria [25]. Also, it helps solve various types of real-world computation issues.…”

Section: Machine Learning Based Cloud Computing Energy Optimization A...mentioning

confidence: 99%

“…A lot of computation time is required for supervised learning training. Unlike unsupervised learning, it cannot cluster or classify data by independently determining its features [25].…”

Section: Machine Learning Based Cloud Computing Energy Optimization A...mentioning

confidence: 99%

Machine Learning Centered Energy Optimization In Cloud Computing: A Review

Puso,

Sigwele,

Mustapha

2023

IJEEI

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The rapid growth of cloud computing has led to a significant increase in energy consumption, which is a major concern for the environment and economy. To address this issue, researchers have proposed various techniques to improve the energy efficiency of cloud computing, including the use of machine learning (ML) algorithms. This research provides a comprehensive review of energy efficiency in cloud computing using ML techniques and extensively compares different ML approaches in terms of the learning model adopted, ML tools used, model strengths and limitations, datasets used, evaluation metrics and performance. The review categorizes existing approaches into Virtual Machine (VM) selection, VM placement, VM migration, and consolidation methods. This review highlights that among the array of ML models, Deep Reinforcement Learning, TensorFlow as a platform, and CloudSim for dataset generation are the most widely adopted in the literature and emerge as the best choices for constructing ML-driven models that optimize energy consumption in cloud computing.

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“…However, this proposed model does not address workload prediction. Another paper [78] combines the wavelet transform with the support vector machine (WSVM) to come with the advantage of analyzing the cycle and frequency of the input signal by replacing the kernel function of SVM by wavelet function. Experimental results indicate that the proposed WSVM cloud computing load forecasting outperforms the standard SVM and also ARIMA model.…”

Section: Supervised and Unsupervised Machine Learning Techniquesmentioning

confidence: 99%

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

et al. 2020

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Given the perpetual surging of cloud services' requests, energy consumption of cloud data centers with their related CO 2 emissions still represents major issues. Efficient use of cloud's resources becomes then the driven force ensuring both, the satisfaction of service-level agreements and the sobriety of cloud's energy consumption. This paper purports to survey for the first time, a comprehensive literature of some actual challenges facing various cloud's resources utilization scheduling approaches dealing with energy conservation. Indeed, cloud resources involve not only computing servers, but also a wide set of intra-and inter-cloud network's resources to be considered. These resources are mainly provisioned through two wellknown technologies: virtualization and/or containerization. As existing studies in the area of cloud resources provisioning are generally categorized into different groups, this research depicts a complete taxonomy of energy-efficient cloud resources scheduling. In this same perspective, we survey some recent efforts made in energy-efficient virtual and containerized resources, by emphasizing first the most used reactive then proactive techniques for managing the whole cloud resources energy efficiency scheduling.

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The cloud computing load forecasting algorithm based on wavelet support vector machine

Cited by 11 publications

References 12 publications

Exploring the empirical studies of cloud computing adoption in anglophone West African countries’ academic libraries: a review

Exploring the empirical studies of cloud computing adoption in anglophone West African countries’ academic libraries: a review

Machine Learning Centered Energy Optimization In Cloud Computing: A Review

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

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