In this paper, a new approach is proposed to predict the fractal behavior of a distributed network traffic. In this research, traffic traces are collected from a distributed network operated by NETRESEC an independent software vendor with focus on the network security field, network forensics and analysis of network traffic. A traffic analysis on packet, connection, protocol and application layers are taken into consideration. Apart from it, an investigation of self-similar and long-range dependent behavioral characteristic is made prior to the collection of traffic traces. Traffic prediction plays an important role in guaranteeing Quality of Service (QoS) in distributed networks due to the diversity of services in a realtime network application. Traffic prediction can be useful for dynamic routing, congestion control and prevention, autonomous traffic engineering, proactive management of the network etc. The forecasting methods can be broadly classified into two categories: linear prediction and nonlinear prediction models. Hence, the idea behind this research is to propose a Multiple Regression-booster equation based on the correlation structure to have a more accurate predicted traffic data result than using the later nonlinear prediction models involving Neural Networks. The traffic is sniffed and exported to NeuroSolutions builder, SPSS and then examined. Further, the exported and dissected traffic data is fed as input to train the neural network to let it predict the resultant fractal behavior of the distributed network traffic and an equation is proposed to derive the ultimate close network traffic prediction in SPSS.
On-board image compression has been a growing trend in most recent satellite missions. Since majority of satellite applications deal with imagery; compression of images due to limited on-board data storage mediums has become a necessity. The idea of treating satellite imageries as fractals and then encoding them provides an efficient way of conserving bandwidth and per-bit storage costs. Fractal encoding is characterized by slow encoding times which somehow had hindered its popularity in spite of its impressive compression ratio scaling many orders as compared to JPEG. In order to circumvent this handicap, Fractal compression is implemented using powerful GPUs (Graphical Processor Units) that are capable of reaching astronomical computing speeds of around 900 GFLOPS (Quadro Graphic Cards from Nvidia TM ) with internal memory bandwidth ranging to 100 GB/s. This astounding parallel capability is probed to be used on board systems, providing much needed boost for image compression. As the decoding part of compressed fractal images is almost instantaneous, this part can be handled without any specific hardware at the ground station level. Further, the issue of on-board data storage mechanisms is discussed with emphasis on use of HDD instead of SSD and flash memories. In sum, the prime aim is to provide a seamless image compression mechanism coupled with decompression at ground station level thus providing real-time streaming of satellite images from satellite to the ground. General TermsFractal Image Compression, Graphical Processing Unit (GPU), Satellite images, On-board systems, et. al.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.