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20163
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Subject: Computer Applications × Thesis Type: M.Tech. ×
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    ThesisItemOpen Access
    Load balancing the network traffic in the nth mode of IPtables
    (Punjab Agricultural University, Ludhiana, 2016) Anupama; Gupta, O.P.
    Load balancing is the most crucial in order to develop the network applications. It optimizes the network interfaces that are present in the cluster. We know, everything belongs to internet these days. Various Servers are connected together with different IP addresses to form a network, to run many applications that are used by the users in daily life. With the increase in the popularity of the web, network traffic increases and need for fast access to the internet is on demand. For smooth services, redundancy of the servers is also required. A single server is not able handle all the traffic which results into a server crash. In this, Network is divided into different paths through which the transmission of packets is done. They are distributed on all the interfaces simultaneously. The network is always available without any delay, by detecting host failures and automatically redistributing the network traffic to other working servers. The performance of per-packet load balancing on different servers is investigated. Simulation results reveal that it is always beneficial to use load balancing and backup of other servers are always present in case of any failure in network.
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    ThesisItemRestricted
    Management of Bandwidth with Congestion Control in Packet Networks
    (Punjab Agricultural University, Ludhiana, 2016) Walia, Guneet Kaur; Gupta, O.P.
    With the growth of internet, the user’s requirements in terms of scale, functionality of network and performance of internet also increases. It becomes very important to guarantee the efficiency, stability and performance of a given network as per the user’s requirements. It is known that the data transfer between computers takes place in the form of packets. Given the network architecture of the underlying network, the performance of the network relies on control methods and effective flow management measures taken. Therefore congestion control ought to be the required premise. The support for congestion control is provided by Transmission Control Protocol (TCP). Many Active Queue Management (AQM) algorithms have been proposed that helps in congestion avoidance, the most basic of them is Random Early Detection (RED) Algorithm. Due to drawbacks of RED, many enhancements of RED were proposed. Dynamic gentle Random Early Detection (DGRED) is the latest enhancement of RED which has been implemented in Network Simulator-3 (NS-3). A network scenario consisting of 6 nodes has been taken. The performance of both the algorithms has been evaluated by configuring the routers and the results have been compared. Jitter parameter has been further testified by later on modifying the topology by adding another node. The topology modification brings out better jitter results.
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    ThesisItemOpen Access
    Development of a program for grading of spherical fruits using image processing technique
    (Punjab Agricultural University, Ludhiana, 2016) Simrandeep Kaur; Salam Din
    Agriculture is one of the largest economic sectors and it plays the major role in economic development of India. Fruits and vegetables are important for the healthy life. Fruits are the perfect sources for providing our body with all the necessary nutrients and vitamins. There are different varieties of fruits but Apple is one of the economically and culturally most important fruit crops and contributes significantly to human daily consumption. Still for grading the traditional inspection of fruits is performed by human experts, which is considered to be time consuming, tedious, labor intensive and expensive. So there is a need for automated system for accurate, fast and quality fruits grading. In this, method used for grading of apple fruit using the RGB image and apple are graded based on their outer surface. In order to grading a spherical fruit, we extract various external feature of a fruit like color, shape, size and Texture. The system uses RGB images of the fruit. From these image, it automatically extract the external features of the fruit Based on the extracted features it classifies fruit into two categories. The classification of apple fruit using the extracted features is done with the help of support vector machines (SVMs), classification is done and found accuracy of 100%.