Makau S. Mutua

Work place: Department of Computer Science, Meru University of Science and Technology, Meru, Kenya

E-mail: smutua@must.ac.ke

Website:

Research Interests: Neural Networks, Computer Architecture and Organization, Computer Networks, Data Structures and Algorithms

Biography

Dr. Makau S. Mutua holds PhD in Systems Analysis and Integration, a Master in Information Technology and Bachelor of Science in Computer Science. He is a Senior lecturer in Meru University of Science and Technology and currently serving as the dean of the School of Computing and Informatics. He is an established scholar and academician with several publications in refereed journals and book chapters. His research interests are Neural Networks, Computer Networks and Data Science.

Author Articles
An Enhanced List Based Packet Classifier for Performance Isolation in Internet Protocol Storage Area Networks

By Joseph Kithinji Makau S. Mutua Gitonga D. Mwathi

DOI: https://doi.org/10.5815/ijitcs.2021.05.05, Pub. Date: 8 Oct. 2021

Consolidation of storage into IP SANs (Internet protocol storage area network) has led to a combination of multiple workloads of varying demands and importance. To ensure that users get their Service level objective (SLO) a technique for isolating workloads is required. Solutions that exist include cache partitioning and throttling of workloads. However, all these techniques require workloads to be classified in order to be isolated. Previous works on performance isolation overlooked the classification process as a source of overhead in implementing performance isolation. However, it’s known that linear search based classifiers search linearly for rules that match packets in order to classify flows which results in delays among other problems especially when rules are many. This paper looks at the various limitation of list based classifiers. In addition, the paper proposes a technique that includes rule sorting, rule partitioning and building a tree rule firewall to reduce the cost of matching packets to rules during classification. Experiments were used to evaluate the proposed solution against the existing solutions and proved that the linear search based classification process could result in performance degradation if not optimized. The results of the experiments showed that the proposed solution when implemented would considerably reduce the time required for matching packets to their classes during classification as evident in the throughput and latency experienced.

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