Predicting Service Level Agreement Violations in Cloud using Machine Learning techniques

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Date
2019
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Publisher
IEEE
Abstract
When discussing about Service Level Agreement contract design it is very important to deal with violations in the Cloud. This means that if the scheduling process doesn't take in consideration some workloads (it cannot schedule them) this will generate issues in the interaction between Cloud Service Customer(s) and the Cloud Service Provider. In our article we deal with estimating the actual workload that is sent to be scheduled on the Cloud Service Infrastructure (CSI). The estimation is based on four different machine learning techniques: Hidden Markov Models, Neural Networks, K Nearest Neighbor and Support Vector Machines. We chose the four different learning techniques to show that workload prediction can be done in various ways. The results show with a good enough confidence that we can predict the actual load on the Cloud Service Infrastructure.
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Keywords
Cloud, SLA violations, machine learning, Hidden Markov Model, Neural Networks, K Nearest Neighbor, Support Vector Machines
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