Abstract
There is an increase in demand for cloud computing services, and optimizing the resources is crucial. Conventional algorithms are not adequate to address issues with the cloud, including uneven task distribution in virtual machines or inefficient resource delivery to cloud customers. To significantly enhance the outcome of cloud apps and address the aforementioned difficulties, it is necessary to investigate further sophisticated ways. This study looks into the most recent machine learning techniques that can handle the difficulties in a cloud context. A paradigm monitoring and identification of errors in Virtual Machine (VM) resource utilization is put forth in this study. The suggested methodology called IF-SVM (Isolation Forest-Support Vector Machine) is able to identify the errors. The VM workload trace from PlanetLab is used to test and train the samples. During this process, the concepts of ML techniques such as SVM and IF and the VM resource matrix is used. F1-score of 0.87 is able to achieve by using the SVM for the time series of 1 h and 0.91 F1-score for Isolation Forest for the 50 VMs. This result demonstrates the effectiveness of both methods for the model, but SVM outperforms well in terms of classification in comparison with Isolation Forest.
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References
Boutaba R, Zhang Q, Zhani MF (2014) Virtual machine migration in cloud computing environments: benefits, challenges, and approaches. In: Communication infrastructures for cloud computing: IGI global, pp 383–408
Priyanka H, Cherian M (2020) Efficient utilization of resources of virtual machines through monitoring the cloud data center. In: Bindhu V, Chen J, Tavares J (eds) International conference on communication, computing and electronics systems. Lecture notes in electrical engineering, vol 637. Springer, Singapore
Priyanka H, Cherian M (2022) Energy-efficient resource management of virtual machine in cloud infrastructure. In: Buyya R, Garg L, Fortino G, Misra S (eds) New frontiers in IoT and cloud computing for post-COVID-19 era. Springer Nature, Switzerland. ISBN: 978-3-031-05527-0. https://doi.org/10.1007/978-3-031-05528-7_4
Prashanthi M (2016) Analysis of security issues in virtualization cloud computing. Int J Comput Sci Mob Comput IJCSMC 5(8):274–281
Nguyen H, Tan Y, Gu X (2011) Pal: Propagationaware anomaly localization for cloud hosted distributed applications. In: Managing large-scale systems via the analysis of system logs and the application of machine learning techniques, pp 1–8
Modi CN, Patel DR, Patel A, Muttukrishnan R (2012) Bayesian classifier and snort based network intrusion detection system in cloud computing. In: 2012 third international conference on computing, communication and networking technologies (ICCCNT’12). IEEE, pp 1–7
Junaid M, Sohail A, Rais RNB, Ahmed A, Khalid O, Khan IA, Hussain SS, Ejaz N (2020) Modeling an Optimized Approach for Load Balancing in Cloud. IEEE Access 8:173208–173226
Tiwari T, Turk A, Oprea A, Olcoz K, Coskun AK (2017) User-profile-based analytics for detecting cloud security breaches. In: 2017 IEEE international conference on big data (big data). IEEE, pp 4529–4535
Mehmood Y, Shibli MA, Habiba U, Masood R (2013) Intrusion detection system in cloud computing: Challenges and opportunities. In: 2013 2nd national conference on information assurance (NCIA). IEEE, pp 59–66
Priyanka H, Cherian M (2021) Effective utilization of resources through optimal allocation and opportunistic migration of virtual machines in cloud environment. Int J Cloud Appl Comput (IJCAC) 11(3):72–91. https://doi.org/10.4018/IJCAC.2021070105. (Web of Science, SCI indexed)
Priyanka H, Cherian M (2022) Metaheuristic based resource management approach towards minimization of energy consumption in cloud data centres. J Optoelectron Laser 41.7:676–687
ScikitLearn (23-03-2021) Novelty and outlier detection. Available: https://scikitlearn.org/stable/modules/outlier_detection.html
ScikitLearn. One class SVM. Available: https://scikitlearn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html
Liu FT, Ting KM, Zhou Z-H (2012) Isolation based anomaly detection. ACM Trans Knowl Discov Data 6(1):1–39
Chang C-C, Lin C-J (2001) LIBSVM: a library for support vector machines. ACM Trans Intell Syst 2(3):1–27; Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RCJNc (2001) Estimating the support of a high-dimensional distribution 13(7):1443–1471
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Priyanka, H., Rao, M.G., Apoorva, M.S. (2023). Detecting Anomalies in the Virtual Machine Using Machine Learning Techniques. In: Kumar, A., Ghinea, G., Merugu, S. (eds) Proceedings of the 2nd International Conference on Cognitive and Intelligent Computing. ICCIC 2022. Cognitive Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-99-2742-5_13
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DOI: https://doi.org/10.1007/978-981-99-2742-5_13
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