Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Cloud performance evaluation using fuzzy logic(Institute of Electrical and Electronics Engineers Inc., 2015) Saxena, G.; Nanath, K.Cloud computing is the latest form of evolution of distributed computing which eradicates the need to own the expensive hardware resources, as the resources can be easily obtained on cloud in pay-per-usage manner. This proves to be very cost efficient to users as they no longer need to depend on the hardware resources to satisfy their needs. For measuring cloud performance there exist a few approaches, yet there is scope for experimenting and developing new approaches for evaluating cloud efficiency. This paper attempts to measure cloud performance by using fuzzy logic by taking into consideration different performance parameters of the cloud. A metric for analyzing the performance of the cloud is derived upon, by considering various performance parameters and proves to be helpful in comparing various cloud services. The method proposed in this paper is very flexible and easy to understand. The results provide useful information and directions for further research in this new emerging field. © 2015 IEEE.Item An iterative MapReduce framework for sports-based tweet clustering(Association for Computing Machinery acmhelp@acm.org, 2015) Saxena, G.; Santurkar, S.In recent years, social media has evolved into a vital source for real-time information. Sports is one of the most popular topics on social media and attracts the attention of users all over the world. However, a large amount of data is generated on a daily basis, making it difficult for the fans to follow the topics of their interest. Clustering of these posts can resolve this issue by retrieving unambiguous and distinct topics. MapReduce is a programming paradigm that is very effective in designing distributed applications that can be deployed on the cloud. Clustering algorithms are generally iterative in nature. The performance gain offered by MapReduce cannot be completely realized by these algorithms due to the inherent architectural bottlenecks associated with iterative tasks. Twister is a MapReduce-based framework designed to minimize these bottlenecks. In this paper, we propose a distributed framework that gathers sports-related tweets and clusters them into distinct topics using the DB-SCAN algorithm customized for Twister. The accuracy of the framework was analysed using the precision-recall scoring mechanism to determine the set of DBSCAN and framework parameters that result in the best set of clusters. The performance of our framework is evaluated based on our clustering results and simulations using the MRSim simulator. We expect that this framework could be used as a model for performing topic detection over generic tweets. We have used the domain of sports to establish the proof of this concept. © 2015 ACM.Item Analysis of ring topology for NoC architecture(Institute of Electrical and Electronics Engineers Inc., 2016) Kamath, A.; Saxena, G.; Talawar, B.In recent years, Network on Chips (NoCs) have provided an efficient solution for interconnecting various heterogeneous intellectual properties (IPs) on a System on Chip (SoC) in an efficient, flexible and scalable manner. Virtual channels in the buffers associated with the core helps in introducing the parallelism between the packets as well as in improving the performance of the network. However, allocating a uniform size of the buffer to these channels is not always suitable. The network efficiency can be improved by allocating the buffer variably based on the traffic patterns and the node requirements. In this paper, we use ring topology as an underlying architecture for the NoC. The percentage of packet drops has been used as a parameter for comparing the performance of different architectures. Through the results of the simulations carried out in SystemC, we illustrate the impact of including virtual channels and variable buffers on the network performance. As per our results, we observed that varied buffer allocation led to a better performance and fairness in the network as compared to that of the uniform allocation. © 2015 IEEE.