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@article{155320,
        author = {Roopa R. Gaur  and Sushama C. Budruk  and Nutan V. Patil },
        title = {Survey on Cloud Load Prediction using Machine Learning Techniques },
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {1},
        pages = {792-796},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=155320},
        abstract = {Cloud computing is technology which allows on demand accessibility of different computer resources, computing power and data storage without active presence of user. For any application Resources provisioning is main important challenging work in cloud computing environment. Resource need to be allocated dynamically in cloud platform, on the basis of workload behavior of different applications. Any alteration, in resources provisioning it may leads waste of energy, storage and cost. In Addition to, it creates Service Level Agreements (SLA) transgression and dropping of Quality of Service (QoS). Hence any violation to commitment made for SLA the service provider has to pay penalty and it leads to customer dissatisfaction. Hence workload prediction of cloud environment is necessary for this purpose there are different technology to predict cloud work load, in this paper we have proposed different machine leaning techniques such as Linear regression, SVM and artificial neural network which creates magic in predictions of workload. Cloud computing along with machine learning could leads to more benefits. This paper presented characteristics and importance of several prediction schemes to enhance workload management system in cloud environment.},
        keywords = {Cloud Computing, Workload, Prediction Model, Quality of Service, Service Level Agreement.},
        month = {},
        }