The issues of scalability and energy efficiency in mega data centers are growing because the east-west traffic is growing at a very high rate and the traditional electrical packet-switched (EPS) networks are constrained. Optical circuit switching (OCS) has come to be a promising complement to the packet switching because it offers high capacity data transport with improved energy-efficiency to support large and steady data traffic. The paper is a systematically assessed comparison of EPS-only data center architectures, OCS-assisted, and hybrid EPS-OCS data center architectures based on a coherent experimental system. The evaluation of performance is done in terms of throughput, flow completion time, energy per delivered bit, optical circuit utilization, and blocking behavior to real traffic workload. The findings indicate that hybrid EPSOCS architectures achieve much better aggregate throughput and lower energy use without compromising low latency with respect to short and latency-sensitive flows. The optical circuits are utilized highly with little blocking, but the inefficiency is still evident when there is a dynamic traffic. In sum, it can be concluded that optical circuit switching is the most effective when embedded in traffic-conscious hybrid constructions that can provide a viable and scalable approach to next-generation mega data centers networks
Prerana Nilesh Khairnar, R. Mulajkar, Shailesh Kulkarni et al.· Journal of Intelligent Decis...· 0 citations
Recommendation systems play an important role in helping users to discover relevant items out of large collections of content. However, the widespread use of information about people's personal preferences raises concerns about privacy. In this research paper, we examine the combinatorial testing strategies to enhance privacy protection in recommendation engines without the detriment of prediction performance. The approach proposed includes a systematic evaluation of combinations of recommendation algorithms and privacy mechanisms by using the MovieLens 20M dataset. The data set has 20,000,263 user ratings and 465,564 tag applications on 27,278 movies created by 138,493 users between January 1995 and March 2015, which currently serves as a popular benchmark for recommender system research. The proposed combinatorial privacy testing model is compared with collaborative filtering, matrix factorization and deep neural recommendation methods. Experimental results show that the performance of the proposed model reaches better results with accuracy of 94.18%, precision of 93.40%, recall of 92.96% and F1-score of 93.18%. The results demonstrate that combinatorial privacy test can be effectively used to increase the reliability of recommendations or protect the information of users.
Prerana Nilesh Khairnar, Gujjala Srinath, A. B. Pawar et al.· Journal of Intelligent Decis...· 0 citations
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