Department of Computer Science and Information Systems

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Author: search.filters.author.Shekhawat, Virendra SinghSubject: search.filters.subject.Machine Learning

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    Datacenter Workload Classification and Characterization: An Empirical Approach
    (IEEE, 2018) Shekhawat, Virendra Singh; Gautam, Avinash
    Datacenter traffic has increased significantly due to rising number of web applications on Internet. These applications have diverse Quality of Service (QoS) requirements making datacenter management a complex task. For a datacenter the amount of resources required for a given resource type (computing, memory, network and storage) is termed as workload. In cloud datacenters, workload classification and characterization is used for resource management, application performance management, capacity sizing, and for estimating the future resource demand. An accurate estimation of future resource demand helps in meeting QoS requirements and ensure efficient resource utilization. Thus modeling and characterization of datacenter workloads becomes necessary to meet performance requirements of applications in a cost-efficient manner. In this paper, a methodology to classify datacenter workloads and characterize them based on resource usage is proposed. Two different workloads have been used, one is Google Cluster Trace (GCT) dataset and other is Bit Brains Trace (BBT) dataset. Seven different machine learning algorithms for workload classification have been used. Workload distribution is estimated in a mix of heterogeneous applications for both GCT and BBT. The seven machine learning algorithms have been compared on the basis of their classification accuracy. Finally, an algorithm to estimate the importance of different attributes for classification is proposed in this paper.
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    A Machine Learning Approach for Traffic Flow Provisioning in Software Defined Networks
    (IEEE, 2020) Shekhawat, Virendra Singh
    With the recent surge of machine learning and artificial intelligence, many research groups are applying these techniques to control, manage, and operate networks. Soft-ware Defined Networks (SDN) transform the distributed and hardware-centric legacy network into an integrated and dynamic network that provides a comprehensive solution for managing the network efficiently and effectively. The network-wide knowledge provided by SDN can be leveraged for efficient traffic routing in the network. In this work, we explore and illustrate the applicability of machine learning algorithms for selecting the least congested route for routing traffic in a SDN enabled network. The proposed method of route selection provides a list of possible routes based on the network statistics provided by the SDN controller dynamically. The proposed method is implemented and tested in Mininet using Ryu controller.