Performance analysis of data centre network (DCN) architectures using virtual network embedded simulator (VNE-Sim)

Abstract:
The unprecedented expansion of the Internet has led to the introduction of the massive Data Centers that provide the infrastructure for Cloud Computing [1]. Additionally, Software Defined Network (SDN) has aided prevailing Internet architecture with network virtualization to support Cloud Computing [2]. Virtual Network Embedding (VNE) algorithms are leveraged by researchers for implementing virtualization of Data Center Networks (DCN) as it improves network scalability, resource utilization, more importantly, lowers the implementation cost in DCN's [3]. VNE-Sim enables users to define network elements and emulate the infrastructure for both single and batch request processing approaches with good memory management facilities [4]. In this project, we will delve into the details of the VNE-Sim discrete event simulator, generate variety of DCN topologies, and analyze the performance of the VNE algorithms on new DCN topology (DCell topology) using the Boston University Representative Internet Topology Generator (BRITE)[5]and Fast Network Simulation Setup (FNSS)[6].

References:
[1] Y. Liu, J. K. Muppala, M. Veeraraghavan, D. Lin, and M. Hamdi, Data Center Networks - Topologies, Architectures, and Fault-Tolerance Characteristics. Springer Briefs in Computer Science, Springer, 2013.
[2] S. Haeri and Lj. Trajkovic, "VNE-Sim: a virtual network embedding simulator," in Proc. Simutools, Prague, Czech Republic, Aug. 2016, pp. 112-117.
[3] H. Ben Yedder, Q. Ding, U. Zakia, Z. Li, S. Haeri, and Lj. Trajkovic, "Comparison of virtualization algorithms and topologies for data center networks," in Proc. The 26th International Conference on Computer Communications and Networks (ICCCN 2017), 2nd Workshop on Network Security Analytics and Automation (NSAA), Vancouver, Canada, Aug. 2017.
[4] S. Haeri and Lj. Trajkovic, "Virtual network embedding via Monte-Carlo tree search," IEEE Transactions on Cybernetics, vol. 47, no. 2, pp. 1-12, Feb. 2017.
[5] (2018, Aug.) Boston University Representative Internet Topology Generator. [Online]. Available: http://www.cs.bu.edu/brite/.
[6] L. Saino, C. Cocora, and G. Pavlou, "A toolchain for simplifying network simulation setup," in Proc. the 6th International ICST Conference on Simulation Tools and Techniques, SimuTools '13, Brussels, pp. 82-91, ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 2013,

Performance analysis of Youtube video streaming on LTE using ns-3

Abstract:
Video streaming is quickly becoming the most common use case for mobile data traffic globally. The dominant real-time entertainment service supplier, YouTube, with 23.4% of the daily traffic in North America [1], employs HTTP adaptive streaming. This is made possible with the high Quality of Service (QoS) and the high bandwidth capabilities of LTE (Long Term Evolution). [2] Given the popularity of YouTube streaming using LTE in North America, it is worthwhile to study the performance of YouTube video streaming in LTE using a simulator. The discrete event simulator ns-3 is used in this project because it already possesses an LTE module [4] and because an HTTP Adaptive Streaming generator framework has been proposed in [5] for YouTube video streaming. Prados-Garzon et al. studied the performance of YouTube service in LTE using a Matlab simulation in [2]. Our motivation in developing a simulation of YouTube service in LTE using ns-3 is that ns-3 is a free, open-source simulator that is commonly-used for computer networks performance analysis.

References:
[1] Global Internet Phenomena Sandvine, 05-Sep-2012.
[2] C. Callegari et al., "Experimental analysis of ViLTE service," IEEE Access, vol. 6, pp. 21129-21139, 2018. doi: 10.1109/ACCESS.2018.2821701 URL: http://ieeexplore.ieee.org.proxy.lib.sfu.ca/stamp/stamp.jsp?tp=&arnumber=8329138&isnumber=8274985
[3] "LTE module of ns-3." [Online]. Available: https://www.nsnam.org/docs/models/html/lte.html. [Accessed: 08-Feb-2020].
[4] William David Diego Maza, "A framework for generating HTTP adaptive streaming traffic in ns-3," in Proc. SIMUTools - 9th EAI International Conference on Simulation Tools and Techniques - 2016, ACM SIGSIM, Aug 2016, Prague, Czech Republic.
[5] J. Prados-Garzon, P. N. Ameigeiras, J. Navarro-Ortiz, and J. M. Lopez-Soler, "imulation-based performance study of YouTube service in 3G LTE," 2013.

Simulation and analysis on different mobile wireless ad hoc networks (MANET) routing protocols using Riverbed Modeler

Abstract:
Mobile ad hoc network (MANET) is a decentralized wireless network without pre-existing infrastructure. MANET routing protocols should adjust to changes in the network topology and maintain routing information so that data can be forwarded to their destinations. This project describes the simulation and performance comparison of different routing protocols which are Dynamic Source Routing (DSR), Ad Hoc On-demand Distance Vector (AODV) and Temporally Ordered Routing Algorithm (TORA) protocol. Using Riverbed Modeler, different scenarios such as varying node density at an area can be analyzed by criteria such as end-to-end throughput, end-to-end delay, packet loss, and packet delivery ratio.

References:
[1] N. Adam, "Effect of node density on performances of three MANET routing protocols - IEEE Conference Publication", Ieeexplore.ieee.org, 2020. [Online]. Available: https://ieeexplore.ieee.org/document/5503051/. [Accessed: 06- Feb- 2020]
[2] Y. Fan, "OPNET-based Network of MANET Routing Protocols DSR Computer Simulation - IEEE Conference Publication", Ieeexplore.ieee.org, 2020. [Online]. Available: https://ieeexplore.ieee.org/document/5571674/. [Accessed: 06- Feb- 2020]
[3] M. Fazeli and H. Vaziri, "Assessment of Throughput Performance Under OPNET Modeler Simulation Tools in Mobile Ad Hoc Networks (MANETs) - IEEE Conference Publication", Ieeexplore.ieee.org, 2020. [Online]. Available: https://ieeexplore.ieee.org/document/6005694/. [Accessed: 06- Feb- 2020]
[4] H. Singh, "Performance Investigation of Reactive AODV and Hybrid GRP Routing Protocols under Influence of IEEE 802.11n MANET- IEEE Conference Publication", Ieeexplore.ieee.org, 2020. [Online]. Available: https://ieeexplore.ieee.org/document/7079101/. [Accessed: 06- Feb- 2020]
[5] M. Rajput, "Comparison of Ad-hoc reactive routing protocols using OPNET modeler- IEEE Conference Publication", Ieeexplore.ieee.org, 2020. [Online]. Available: https://ieeexplore.ieee.org/document/5643454/. [Accessed: 06- Feb- 2020]

Detecting Internet hackers and attacks

Abstract:
We are browsing many websites on mobile devices and computers through public websites everyday. Some of them may contain risky information or commands that could attack our computers and steal personal data from us, so the security of the internet is a significant problem that needs to be faced nowadays. Wirshark is a powerful software which is a free and open-source packet analyzer. It could provide all useful information such as communications protocol and troubleshooting about every link we browsed. It could detect many protocols and never lose or dropout packet. We are going to detect attacks based on the feature of communication from Wireshark. Spy the packet that is malicious.

References:
[1] Mike Chapple, "Using Wireshark to monitor and secure your network," 2008. Available: https://www.computerweekly.com/news/1280099499/Using-Wireshark-to-monitor-and-secure-your-n etwork [Accessed: March 8 2020]
[2] Kody, "Spy on traffic from a smartphone with Wireshark," 2019 Available: https://null-byte.wonderhowto.com/how-to/spy-traffic-from-smartphone-with-wireshark-0198549/ [Accessed: March 8 2020]
[3] "SolarWinds MSP, Type of Network Security," 2019. Available: https://www.solarwindsmsp.com/blog/types-of-network-security [Accessed: March 8 2020]
[4] Debojyoti Sengupta, "Detection and analysis of SYN flood DDoS attack using Wireshark." Available: https://www.academia.edu/6009916/DETECTION_AND_ fic_and_Checksum_Error_in_Network_using_Wireshark ANALYSIS_OF_SYN_FLOOD_DDOS_ATTACK_USING_WIRESHARK [Accessed: March 8 2020]
[5] Gajendra Singh and Amit Kuraria, "Detection of malicious traffic and checksum error in network using Wireshark." Available: https://www.academia.edu/15441090/Detection_of_Malicious_Traf [Accessed: March 8 2020]

Performance of cloud gaming using Riverbed Modeler

Abstract:
Among every generation since the 1960's, video games have been tremendously prominent, evolving every decade with life-like graphics, powerful systems and a larger verity of games to choose from. With the arrival of cloud technology, game accessibility and ubiquity have a bright future as games can be hosted in a centralized server and accessed through the Internet by a thin client on a wide variety of devices with modest capabilities.[1] Cloud gaming on "Gaming onDemand" enables playing visually rich games locally irrespective of the s processing power because the game is hosted on multiple servers and rendered back to the user, frame-by-frame, from powerful machines. This is very helpful for users with less powerful devices that are otherwise incapable of playing high quality games. With this in mind, cloud gaming is not limited to location; servers and players can be located anywhere in the world with networks linking them together. With this configuration, the quality of service in terms of performance and game responsiveness will lag and diminish the Quality of Experience (QoE). QoE describes the service given to the user by the client. Due to these potential advantages, many companies like OnLive, G-Cluster [2], StreamMyGame, Gaikai and T5-Labs are offering cloud gaming services. Also, Sony's PS Now & Nividia's GeForce Now already offer their subscribers a limited library of popular games at 1080p 60fps.[3] Using Riverbed Modeler tools, we design a network configuration to study various scenarios to identify the feasibility of game streaming over Wi-Fi.

References:
References:
[1] L. Royyala, "Cloud gaming simulation". [Online] Available: https://sites.google.com/view/cloudgamingproject.[Accessed: 7th Feb,2020]
[2] R. E. Ewelle, Y. Francillette, G. Mahdi, and A. Gouach, "Network traffic adaptation for cloud games," International Journal on Cloud Computing: Services and Architecture (IJCCSA) ,vol. 3, no. 5, October 2013.
[3] B. Mariano and S. G. M. Koo, "Is cloud gaming the future of the gaming industry?", in Proc. IEEE ICUFN 2015, Sapporo, Japan, July 2015, pp. 969-972.
[4] S. Abdulazeez, A. Rhalibi, and D. Jumeily, "Simulation of massively online games communication using OPNET custom application", in Proc. ISCC, 2016.
[5] "Google Hybrid and Multi-Cloud Network," [Online] Available: https://cloud.google.com/solutions/hybrid-and-multi-cloud-network-topologies. [Accessed 8th Feb, 2020] Topologies Inc.