Investigation of Cloud Computing Based Big Data on Machine Learning Algorithms.

Year 2021, , 670 - 682, 07.06.2021

Muhammed Yıldırım , Ahmet Çınar , Emine Cengil

https://doi.org/10.17798/bitlisfen.897573

Cited By: 2

Abstract

Cloud computing technology is a model that allows access to a common pool of configurable computing resources whenever and wherever. With the developing technology, the use of this model is increasing day by day. There are many benefits of cloud computing to its users. The data that users keep in their data sets is the simplest example of this. With the cloud technology, the size of the data stored in databases is also increasing. For this reason, cloud technology and big data concepts are intertwined due to the large amount of data stored in databases. It is of great importance that the obtained data is evaluated by machine learning methods and produces results that can be used for technical and commercial purposes. In this study, first of all, cloud technology, the big data brought by this technology and the classification of these data with machine learning methods and algorithms have been examined. Then the studies in the literature were evaluated.

Keywords

Big Data, Cloud Computing, Resource Allocation, Machine Learning.

Investigation of Cloud Computing Based Big Data on Machine Learning Algorithms

Abstract

Bulut bilişim teknolojisi, yapılandırılabilir bilişim kaynaklarından oluşan ortak bir havuza, istenildiği zaman ve her yerden erişme imkânı veren bir modeldir. Gelişen teknolojiyle birlikte bu modelin kullanımı gün geçtikçe artmaktadır. Bulut bilişimin kullanıcılarına sunduğu birçok fayda mevcuttur. Kullanıcıların veri setlerinde tuttuğu veriler bunun en basit örneğidir. Bulut teknolojisiyle birlikte veri tabanlarında tutulan verilerin boyutu da artmaktadır. Bu sebeple veri tabanlarında tutulan yüksek miktarda ki veriler yüzünden bulut teknolojisi ile büyük veri kavramları iç içe girmiş durumdadır. Elde edilen verilerin makina öğrenmesi yöntemleriyle değerlendirilmesi teknik ve ticari amaçlarla kullanılabilecek şekilde sonuçlar üretmesi büyük bir önem arz etmektedir. Bu çalışmada öncelikle bulut teknolojisi, bu teknolojinin getirmiş olduğu büyük veriler ve bu verilerin makine öğrenmesi yöntemleri ve algoritmaları ile sınıflandırılması incelenmiştir. Daha sonra literatürde yapılan çalışmalar değerlendirilmiştir.

Keywords

Büyük Veri, Bulut Bilişim, Kaynak Tahsisi, Makine Öğrenmesi

References

• Aceto G., Persico V., Pescapé A. 2020. Industry 4.0 and health: Internet of things, big data, and cloud computing for healthcare 4.0. Journal of Industrial Information Integration,18: 100129.
• Mrozek D., Koczur A., Małysiak-Mrozek B. 2020. Fall detection in older adults with mobile IoT devices and machine learning in the cloud and on the edge. Information Sciences, 537: 132-147.
• Yildirim M., Cinar A. 2020. A deep learning based hybrid approach for COVID-19 disease detections. Traitement du Signal, 37 (3): 461-468.
• Morariu C., Morariu O., Răileanu S., Borangiu T. 2020. Machine learning for predictive scheduling and resource allocation in large scale manufacturing systems. Computers in Industry, 120: 103244.
• Tang S., He B., Yu C., Li Y., Li K. 2020. A survey on spark ecosystem: Big data processing infrastructure, machine learning, and applications. IEEE Transactions on Knowledge and Data Engineering.
• Namasudra S., Devi D., Kadry S., Sundarasekar R., Shanthini A. 2020. Towards DNA based data security in the cloud computing environment. Computer Communications, 151: 539-547.
• Sunyaev A. 2020. Cloud computing. In Internet computing. Springer, Cham, 195-236.
• Soh J., Copeland M., Puca A., Harris M. 2020. Overview of Azure Infrastructure as a Service (IaaS) Services. In Microsoft Azure, Apress, Berkeley, CA., 21-41.
• Caiza G., Saeteros M., Oñate W., Garcia M.V. 2020. Fog computing at industrial level, architecture, latency, energy, and security: A review. Heliyon, 6 (4): e03706.
• Liu S., Chan F.T., Yang J., Niu B. 2018. Understanding the effect of cloud computing on organizational agility: An empirical examination. International Journal of Information Management, 43: 98-111.
• De la Prieta F., Rodríguez-González S., Chamoso P., Corchado J.M., Bajo J. 2019. Survey of agent-based cloud computing applications. Future Generation Computer Systems, 100: 223-236.
• Kholidy H.A. 2020. An intelligent swarm based prediction approach for predicting cloud computing user resource needs. Computer Communications, 151: 133-144.
• Hassan H., El-Desouky A.I., Ibrahim A., El-Kenawy E.S.M., Arnous R. 2020. Enhanced QoS-based model for trust assessment in cloud computing environment. IEEE Access, 8: 43752-43763.
• Taha A.A., Ramo W., Alkhaffaf H.H.K. 2021. Impact of external auditor–cloud specialist engagement on cloud auditing challenges. Journal of Accounting & Organizational Change. https://doi.org/10.1108/JAOC-08-2020-0111.
• Kurdi H., Alsalamah S., Alatawi A., Alfaraj S., Altoaimy L., Ahmed S.H. 2019. HealthyBroker: a trustworthy blockchain-based multi-cloud broker for patient-centered ehealth services. Electronics, 8 (6): 602.
• Tamimi A.A., Dawood R., Sadaqa L. 2019. Disaster recovery techniques in cloud computing. In 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT), IEEE, 845-850.
• Hajji M.A., Mezni H. 2018. A composite particle swarm optimization approach for the composite saas placement in cloud environment. Soft Computing, 22 (12): 4025-4045.
• Zaitsev D., Luszczek P. 2020. Docker container based PaaS cloud computing comprehensive benchmarks using LAPACK. In CMIS, 323-337.
• Sanaj M.S., Prathap P.J. 2020. Nature inspired chaotic squirrel search algorithm (CSSA) for multi objective task scheduling in an IAAS cloud computing atmosphere. Engineering Science and Technology, an International Journal, 23 (4): 891-902.
• Pratama I.P.A.E. 2021. The implementation of Container as a Service (CaaS) cloud using openSUSE kubic. Global Journal of Engineering and Technology Advances, 6 (1): 001-009.
• Namasudra S. 2021. Data access control in the cloud computing environment for bioinformatics. International Journal of Applied Research in Bioinformatics (IJARB), 11 (1): 40-50.
• Tavbulatova Z.K., Zhigalov K., Kuznetsova S.Y., Patrusova A.M. 2020. Types of cloud deployment. In Journal of Physics: Conference Series, IOP Publishing, 1582 (1): 012085).
• Qureshi A., Sharma A. 2021. Cloud Computing: The New World of Technology. In Proceedings of Second International Conference on Smart Energy and Communication, Springer, Singapore, 55-60.
• Xu Y., Sun S., Cui J., Zhong H. 2020. Intrusion-resilient public cloud auditing scheme with authenticator update. Information Sciences, 512: 616-628.
• Talaat M., Alsayyari A.S., Alblawi A., Hatata A.Y. 2020. Hybrid-cloud-based data processing for power system monitoring in smart grids. Sustainable Cities and Society, 55: 102049.
• Stergiou C.L., Plageras A.P., Psannis K.E., Gupta B.B. 2020. Secure machine learning scenario from big data in cloud computing via internet of things network. In Handbook of computer networks and cyber security, Springer, Cham, 525-554.
• Ionescu L., Andronie M. 2021. Big Data Management and Cloud Computing: Financial Implications in the Digital World. In SHS Web of Conferences, Vol: 92, EDP Sciences.
• Cengil E., Çinar A. 2020. Göğüs Verileri Metrikleri Üzerinden Kanser Sınıflandırılması. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 11 (2): 513-519.
• Yildirim M., Cinar A. 2020. Classification of Alzheimer's Disease MRI Images with CNN Based Hybrid Method. Ingénierie des Systèmes d'Information, 25 (4).
• Uçkan T., Hark C., Karci A. 2021. SSC: Clustering of Turkish texts by spectral graph partitioning. Politeknik Dergisi, https://doi.org/10.2339/politeknik.684558.
• Kim H.C., Park J.H., Kim D.W., Lee J. 2020. Multilabel naïve Bayes classification considering label dependence. Pattern Recognition Letters, 136: 279-285.
• Li L.L., Zhao X., Tseng M.L., Tan R.R. 2020. Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm. Journal of Cleaner Production, 242: 118447.
• Özdemir A., Şahan M.H. 2020. Radiologic features of symptomatic cholelithiasis: a current perspective. Journal of Health Sciences and Medicine, 3 (4): 466-472.
• Yaşar Ş., Çolak C. 2020. A Proposed Model Can Classify the Covid-19 Pandemic Based on the Laboratory Test Results. The Journal of Cognitive Systems, 5 (2): 60-63.
• Kaçmaz A., Yildiz K., Buldu A. 2020. An Application on Technology Addiction with C4. 5 Classification Algorithm. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 9 (4): 1756-1765.
• Naji D.M., Akin M.K., Cabalar A.F. 2021. Evaluation of seismic site classification for Kahramanmaras City, Turkey. Environmental Earth Sciences, 80 (3): 1-17.
• Jiao S., Gao Y., Feng J., Lei T., Yuan X. 2020. Does deep learning always outperform simple linear regression in optical imaging?. Optics express, 28 (3): 3717-3731.
• Yildirim M., Çinar A. 2019. Simultaneously Realization of Image Enhancement Techniques on Real-Time Fpga. In 2019 International Artificial Intelligence and Data Processing Symposium (IDAP), IEEE, 1-6.
• Yildirim M., Çinar A. 2019. Use of Fpga for Real-Time K-Means Clustering Algorithm. International Journal of Engineering Science and Application, 3 (3): 130-136.
• Wang X., Xu W., Jin Z. 2017. A hidden Markov model based dynamic scheduling approach for mobile cloud telemonitoring. In 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), IEEE, 273-276.
• Osadchiy T., Poliakov I., Olivier P., Rowland M., Foster E. 2019. Recommender system based on pairwise association rules. Expert Systems with Applications, 115: 535-542.
• Powell T. 2018. Sequential algorithms and the computational content of classical proofs. arXiv preprint arXiv: 1812.11003.
• Levchenko O., Kolev B., Yagoubi D.E., Shasha D., Palpanas T., Valduriez P., Masseglia, F. 2019. Distributed algorithms to find similar time series. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer, Cham, 781-785.
• Çinar A., Yildirim M. 2020. Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture. Medical hypotheses, 139: 109684.
• Yildirim M., Çinar A. 2019. Classification of White Blood Cells by Deep Learning Methods for Diagnosing Disease. Revue d'Intelligence Artificielle, 33 (5): 335-340.
• Zhang Y., Yao J., Guan H. 2017. Intelligent cloud resource management with deep reinforcement learning. IEEE Cloud Computing, 4 (6): 60-69.
• Barnes J. 2015. Azure machine learning. Microsoft Azure Essentials. 1st ed, Microsoft.
• Botchkarev A. 2018. Evaluating performance of regression machine learning models using multiple error metrics in Azure Machine Learning Studio Available at SSRN 3177507.
• Rajagopal S., Hareesha K.S., Kundapur P.P. 2020. Performance analysis of binary and multiclass models using azure machine learning. International Journal of Electrical & Computer Engineering, 10 (1): 2088-8708.
• Abdelaziz A., Elhoseny M., Salama A.S., Riad A.M. 2018. A machine learning model for improving healthcare services on cloud computing environment. Measurement, 119: 117-128.
• Tuli S., Tuli S., Tuli R., Gill S.S. 2020. Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing. Internet of Things, 11: 100222.
• Wang J.B., Wang J., Wu Y., Wang J.Y., Zhu H., Lin M., Wang J. 2018. A machine learning framework for resource allocation assisted by cloud computing. IEEE Network, 32 (2): 144-151.
• Zhang J., Xie N., Zhang X., Yue K., Li W., Kumar D. 2018. Machine learning based resource allocation of cloud computing in auction. Comput. Mater. Continua, 56 (1): 123-135.
• Chiba Z., Abghour N., Moussaid K., Rida M. 2019. Intelligent approach to build a Deep Neural Network based IDS for cloud environment using combination of machine learning algorithms. Computers & Security, 86: 291-317.
• Zekri M., El Kafhali S., Aboutabit N., Saadi Y. 2017. DDoS attack detection using machine learning techniques in cloud computing environments. In 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech) IEEE, 1-7.