Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item FQ-PIE Queue Discipline in the Linux Kernel: Design, Implementation and Challenges(Institute of Electrical and Electronics Engineers Inc., 2019) Ramakrishnan, G.; Bhasi, M.; Saicharan, V.; Monis, L.; Patil, S.D.; Tahiliani, M.P.Proportional Integral controller Enhanced (PIE) is an Active Queue Management (AQM) mechanism to address the bufferbloat problem. AQM mechanisms tackle bufferbloat by dropping or marking packets before the buffers fill up, but typically do not ensure fairness between responsive and unresponsive flows that share the same bottleneck link i.e., unresponsive flows can starve responsive flows when they co-exist. Recently, there has been an active interest in integrating flow protection mechanisms with AQM mechanisms to collectively tackle the problem of bufferbloat and fairness. There exist two such algorithms: Flow Queue Controlled Delay (FQ-CoDel) and Flow Queue Proportional Integral Controller Enhanced (FQ-PIE) that integrate flow protection with AQM mechanisms. Flow protection is achieved by dividing the incoming flows into separate queues and then applying CoDel/PIE algorithm on respective queues. Although FQ-CoDel is available in the mainline of Linux, there does not exist a model for FQ-PIE. In this paper, we discuss the design and implementation of FQ-PIE in the Linux kernel. We test and evaluate our proposed model of FQ-PIE in different scenarios by comparing the results obtained from it to those obtained for PIE and FQ-CoDel. Besides evaluating the fairness among responsive and unresponsive flows, we also evaluate the fairness among different types of responsive flows, such as when CUBIC TCP shares the same bottleneck link as TCP BBR. We also assess the benefits of integrating flow protection with AQM mechanisms in terms of reducing the latency for thin, latency sensitive flows when they coexist with thick, latency tolerant flows. © 2019 IEEE.Item Collaborative Filtering for Book Recommendation System(Springer, 2020) Ramakrishnan, G.; Saicharan, V.; Chandrasekaran, K.; Rathnamma, M.V.; Ramana, V.V.Collaborative filtering is one of the most important techniques in the market nowadays. It is prevalent in almost every aspect of the internet, in e-commerce, music, books, social media, advertising, etc., as it greatly grasps the needs of the user and provides a comfortable platform for the user to find what they like without searching. This method has a few drawbacks; one of them being, it is based only on the explicit feedback given by the user in the form of a rating. The real needs of a user are also demonstrated by various implicit indicators such as views, read later lists, etc. This paper proposes and compares various techniques to include implicit feedback into the recommendation system. The paper attempts to assign explicit ratings to users depending on the implicit feedback given by users to specific books using various algorithms and thus, increasing the number of entries available in the table. © 2020, Springer Nature Singapore Pte Ltd.