Minimizing file downloading time in stochastic peer to peer networks
Table Of Contents
<p> </p><p>Title page – – – – – – – – i</p><p>Certification – – – – – – – – ii</p><p>Approval page – – – – – – – – iii</p><p>Dedication – – – – – – – – iv</p><p>Acknowledgement – – – – – – – v</p><p>Abstract – – – – – – – – – vi</p><p>Table of contents – – – – – – – vii</p><p>
Chapter ONE
</p><p>1.0 INTRODUCTION – – – – – – 1</p><p>1.1 Statement of the problem – – – – 2</p><p>1.2 Purpose of the study – – – – – 3</p><p>1.3 Aims and objectives – – – – – 4</p><p>1.4 Scope of study – – – – – – 5</p><p>1.5 Limitations of the study – – – – – 6</p><p>1.6 Definition of terms. – – – – – – 7</p><p>Chapter TWO
</p><p>2.0 LITERATURE REVIEW – – – – – 9</p><p>Chapter THREE
</p><p>3.0 METHOD FOR FACT FINDINGS AND DETAILED DISCUSSIONS ON THE SUBJECT MATTER – -16</p><p>3.1 Methodologies for fact-finding – – – – 16</p><p>3.2 Discussion – – – – – – – 17</p><p>Chapter FOUR
</p><p>4.0 FUTURES, IMPLICATIONS AND CHALLENGES OF THE SUBJECT MATTER FOR THE SOCIETY-22</p><p>4.1 Futures – – – – – – – – 22</p><p>4.2 Implications – – – – – – – 24</p><p>4.3 Challenges – – – – – – – 26</p><p>Chapter FIVE
</p><p>5.0 SUMMARY,RECOMMENDATION AND CONCLUSION 28</p><p>5.1 summary- – – – – – – – 28</p><p>5.2 Recommendations – – – – – – 28</p><p>5.3 Conclusion – – – – – – – 29</p><p>References – – – – – – – 30</p> <br><p></p>Project Abstract
The peer-to-peer (P2P) file sharing applications are becoming increasingly popular and account for more than 70% of the interneta, cs bandwidth usage. Measurement studies show that a typical download of a file can take from minutes up to several hours depending on the level of network congestion or the service capacity fluctuation. In this paper, we consider two major factors that have significant impact on average download time, namely, the spatial heterogeneity of service capacities in different source peers and the temporal fluctuation in service capacity of a single source peer. We point out that the common approach of analyzing the average download time based on average service capacity is fundamentally flawed. We rigorously prove that both spatial heterogeneity and temporal correlations in service capacity increase the average download time in P2P networks and then analyze a simple, distributed algorithm to effectively remove these negative factors, thus minimizing the average download time. We show through analysis and simulations that it outperforms most of other algorithms currently used in practice under various network configurations.
Project Overview
1.0 INTRODUCTION
Peer-to-peer (P2P) technology is heavily used for content distribution applications. The early model for content distribution is a centralized one, in which the service provider simply sets up a serve and every user downloads files from it. In this type of network architecture (server-client), many users have to compete for limited resources in terms of bottleneck bandwidth or processing power of a single serve. As a result, each user may receive very poor performance. From a single user perspective, the duration of a download session, or the download time for that individual user is the most often used performance metric.
P2P technology tries to solve the issue of scalability by making the system distributed. Each computer (peer) in the network can act as both a server and a client at the same time. When a peer complete downloading some files from the network, it can become a server to service other peers in the network. It is obvious that as time goes on, the service capacity of the entire network will increase die to the increase in the number of servicing peers. With this increasing service capacity, theoretical studies have shown that the average download time for each user in the network is much shorter than that of a centralized network architecture in ideal cases (2), (3). In other words, users of a P2P network should enjoy much faster download.
1.1 STATEMENT OF PROBLEMS
Owing to:
i) The difficulties people face in locating a web application workshop.
Urgent need to contact a web application workshop at emergency time.
Blazingprojects Mobile App
📚 Over 50,000 Project Materials
📱 100% Offline: No internet needed
📝 Over 98 Departments
🔍 Software coding and Machine construction
🎓 Postgraduate/Undergraduate Research works
📥 Instant Whatsapp/Email Delivery
Related Research
Predicting Disease Outbreaks Using Machine Learning and Data Analysis...
The project topic, "Predicting Disease Outbreaks Using Machine Learning and Data Analysis," focuses on utilizing advanced computational techniques to ...
Implementation of a Real-Time Facial Recognition System using Deep Learning Techniqu...
The project on "Implementation of a Real-Time Facial Recognition System using Deep Learning Techniques" aims to develop a sophisticated system that ca...
Applying Machine Learning for Network Intrusion Detection...
The project topic "Applying Machine Learning for Network Intrusion Detection" focuses on utilizing machine learning algorithms to enhance the detectio...
Analyzing and Improving Machine Learning Model Performance Using Explainable AI Tech...
The project topic "Analyzing and Improving Machine Learning Model Performance Using Explainable AI Techniques" focuses on enhancing the effectiveness ...
Applying Machine Learning Algorithms for Predicting Stock Market Trends...
The project topic "Applying Machine Learning Algorithms for Predicting Stock Market Trends" revolves around the application of cutting-edge machine le...
Application of Machine Learning for Predictive Maintenance in Industrial IoT Systems...
The project topic, "Application of Machine Learning for Predictive Maintenance in Industrial IoT Systems," focuses on the integration of machine learn...
Anomaly Detection in Internet of Things (IoT) Networks using Machine Learning Algori...
Anomaly detection in Internet of Things (IoT) networks using machine learning algorithms is a critical research area that aims to enhance the security and effic...
Anomaly Detection in Network Traffic Using Machine Learning Algorithms...
Anomaly detection in network traffic using machine learning algorithms is a crucial aspect of cybersecurity that aims to identify unusual patterns or behaviors ...
Predictive maintenance using machine learning algorithms...
Predictive maintenance is a proactive maintenance strategy that aims to predict equipment failures before they occur, thereby reducing downtime and maintenance ...
