Joseph Kithinji

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

E-mail: joskithinji2014@gmail.com

Website:

Research Interests: Computational Science and Engineering, Computer systems and computational processes, Data Structures and Algorithms, Mathematics of Computing

Biography

Joseph Kithinji is a Computer Science PhD candidate, School of Computing and Informatics at Meru University, Kenya. He holds an MSc degree in Data communication from KCA University and a BSc (honors) degree in Computer Science & Mathematics from Maseno University. He has ten years of teaching experience.

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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