Technique to Evaluate the Performance of SDN networks

Al-Sadi A.M., Al-Sherbaz A., Xue J., Turner S. (2019) 
Developing an Asynchronous Technique to Evaluate the Performance of SDN HP Aruba Switch and OVS. 
In: Arai K., Kapoor S., Bhatia R. (eds) Intelligent Computing. SAI 2018. Advances in Intelligent Systems and Computing, vol 857. Springer, Cham
https://doi.org/10.1007/978-3-030-01177-2_41

Abstract
Developers of Software Defined Network (SDN) faces a lack of or difficulty in getting a physical environment to test their inventions and developments that drives them to use a virtual environment for their experiments. This work addresses the differences between the SDN virtual environment and physical SDN switches, which leads to equip a more realistic SDN virtual environment. Consequently, this paper presents a precise performance evaluation and comparison of off-the-shelf SDN devices, HP Aruba 3810M, with Open Virtual Switch (OVS) inside Mininet emulator. This work examines the variability of the path delay, throughput, packet losses and jitter of SDN in a different windows size of the packets and network background loads. Our conducted experiments consider a number of protocols, such as ICMP, TCP and UDP. In order to evaluate the network latency accurately, a new asynchronous latency measurement technique is proposed. The developed technique shows more precise results in comparison to other techniques. Furthermore, the work focuses on extracting the flow-setup latency, caused by the external SDN controller when setting flow rules into the switch. The comparison of results shows dissimilarity in the behaviour of SDN hardware and the Mininet emulator. The SDN hardware exposed higher latency and flow-setup time due to extra resources of delay, which the emulator does not possess.

To read full paper: https://link.springer.com/chapter/10.1007/978-3-030-01177-2_41

All views and opinions are the author's and do not necessarily reflected those of any organisation they are associated with. Twitter: @scottturneruon

Impact of Intersection Shapes on Traffic Congestion on Cities

Al-Dabbagh M.S.M., Al-Sherbaz A., Turner S. (2019) 
The Impact of Road Intersection Topology on Traffic Congestion in Urban Cities. 
In: Arai K., Kapoor S., Bhatia R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 868. Springer, Cham
https://doi.org/10.1007/978-3-030-01054-6_83

Abstract
Due to the dramatic increase in population and the rapid developments of the automobile industry, tremendous issues have emerged in road traffic systems, such as traffic congestion. In some big cities, traffic congestion occurs due to several factors, such as roadworks, car accidents, and drivers’ behaviour. Consequently, it negatively impacts on environment and economy as well as on the behaviour of drivers and passengers. In this paper, we first identified the factors that may increase the journey time and listed the traffic congestion measurement metrics. Then, we investigate to reveal the relationship of road network topology to the traffic congestion level regarding travel time and the number of affected vehicles in case of traffic incidents. The open source traffic simulator SUMO (Simulation of Urban MObility) was used to simulate vehicular traffic and to generate traffic jams on the roadmap under various scenarios. These scenarios involve three road topologies which were: crossroads in Denver (CO, USA), roundabouts in Nantes (France), and hybrid topology, which is the combination of intersections and roundabouts, in Northampton town (UK). The results showed that the delayed time is less in the roundabout traffic map topology, and the number of affected vehicles in the case of traffic incidents is less likely to happen in the hybrid topology.

To read the full-text goto: https://link.springer.com/chapter/10.1007/978-3-030-01054-6_83


All views and opinions are the author's and do not necessarily reflected those of any organisation they are associated with. Twitter: @scottturneruon

Novel WebRTC Signalling Mechanism

Two recent papers discussing the work of Naktal Edan on WebRTC 

Edan N.M., Al-Sherbaz A., Turner S. (2019) WebNSM: A Novel Scalable WebRTC Signalling Mechanism for One-to-Many Bi-directional Video Conferencing. In: Arai K., Kapoor S., Bhatia R. (eds) Intelligent Computing. SAI 2018. Advances in Intelligent Systems and Computing, vol 857. Springer, Cham 
https://doi.org/10.1007/978-3-030-01177-2_40

Abstract
Web Real-Time Communication (WebRTC) has been interested in many developers for video conferencing. The major aim is to create a scalable WebRTC signalling mechanism called (WebNSM) for bi-directional video conferencing via Ethernet of LAN and WAN networks. WebNSM was designed for many users over star topology (one-to-many) using Socket.io (API) mechanism and various JavaScript methods to handle the default channel among peers and to gain a full duplex connection among the main broadcaster and participants. WebNSM has a novelty while it provides bi-directional video conferencing for undefined users, opens one/multiple rooms using the same server, determines room initiator, keeps a session productive even another participant leaves, joins an existing session or renegotiate new session, and does not allow unknown peer to join the session. Also, an evaluation of WebNSM, bandwidth consumption, CPU performance, memory usage, Quality of Experience (QoE), maximum links and RTP calculation was achieved. Moreover, this paper highlights the limitations of resources and using star topology for WebRTC video conferencing.

To read the full paper: https://link.springer.com/chapter/10.1007/978-3-030-01177-2_40#citeas 




Edan N.M., Al-Sherbaz A., Turner S. (2019) Performance Evaluation of Resources Management in WebRTC for a Scalable Communication. In: Arai K., Kapoor S., Bhatia R. (eds) Intelligent Computing. SAI 2018. Advances in Intelligent Systems and Computing, vol 857. Springer, Cham
https://doi.org/10.1007/978-3-030-01177-2_48

Abstract
Web Real-Time Communication (WebRTC) offers peer-to-peer communications without any plug-ins. However, WebRTC cannot provide scalability because of its method that depends on a single server or due to the resource limitations and network topology in the architectural of the WebRTC. This paper aims to design a real environment using MATLAB simulation tools to specify the limitations of resources in WebRTC for bi-directional video conferencing, such as CPU performance, bandwidth consumption and Quality of Experience (QoE) using different topologies such as mesh, star and hybrid (a combination of unidirectional/star & bi-directional/mesh). Moreover, several CPU cores like i3, i5, i7, Xeon, i9 and Xeon Phi, as well as bandwidths: 0.5, 1, 5, 10, 30, 50, 100, 500 and 1000 (Mb/s) were considered to achieve and expand the scalability. In this implementation, the factors of real-time implementation were used. Thus, the utilized measurements were already validated while MATLAB presents coefficient with 95% confidence bound. Additionally, this paper highlights the obstructions are preventing scalability in WebRTC using a centralized server. This illustration is beneficial for interested developers who intend to use WebRTC duplex video conferencing among undefined users and different topologies. Furthermore, our simulation-based’ performance evaluation shows the efficiency of the hybrid topology in decreasing the bandwidth overhead and CPU load in WebRTC.

To read the full paper: https://link.springer.com/chapter/10.1007/978-3-030-01177-2_48


All views and opinions are the author's and do not necessarily reflected those of any organisation they are associated with. Twitter: @scottturneruon

Michael - International Changemaker

Dr Michael Opku Agyeman wins International Changemaker of the Year for his work in Ghana.

A second winner for FAST!! Congratulations Dr Michael Opoku Agyeman, International Changemaker of the Year! @scottturneruon @NewsKate @Grifster96

— John M Sinclair (@DeanofFAST) November 12, 2018

Our third group of winners! International Changemaker, Students of the Year, Changemaker Community Initiative of the Year, and SU Changemakers of the Year. Congrats everyone! #ChangemakerAwards2018 pic.twitter.com/loBbtFKXZh

— Changemaker Hub (@Changemaker_Hub) November 12, 2018

Congratulations Michael - well-deserved award. Another great example of the impact of the Computing Subject team at the University of Northampton


Related links.

• Dark-silicon Era for Many-core Architectures
• Design space exploration of cache memory
• Computing for Social Good 7: Importance of Student Volunteering
• Teaching award from the Students - Aldo wins
• Mark Johnson - Lecturer of the year
• University helps inspires next generation to get into computer coding

All views and opinions are the author's and do not necessarily reflected those of any organisation they are associated with. Twitter: @scottturneruon

BCS Northampton Talk: Digital Aviation – e-enabled Aircraft and Implications

20th November 2018 - Digital Aviation – e-enabled Aircraft and Implications

Presented By: Dr Fan, Ip-Shing

The latest generation of aircraft integrates new Internet of Things(IoT) devices and enhanced information capability which can provide information at great speed and volume. Some of the information is already part of the flight data being generated and captured for flight operations. Some data relates to the health of the aircraft, systems and sub-systems that traditionally is for reliability engineering; and of great interest to fleet engineering and MROs. The massive e-enablement of aircraft subsystems heralds the age of Digital Aviation. Issues could be reported in real-time to airline operations and MRO stations to plan solutions and solve issues as soon as the aircraft lands. Effective exploitation of this information requires the necessary information communication and processing equipment, as well as the human skills to take advantage of the information. This presentation explains the implications of the Connected Aircraft to the airlines, airports, MRO and the aviation stakeholders.

Dr Ip-Shing Fan leads the research of information exploitation in the Cranfield IVHM Centre, part of the Cranfield Digital Aviation team. The Cranfield approach balances the viewpoints of the OEM, asset operators and maintenance providers. Fan focuses on creating business value from the information generated by e-enabled aircraft for the purpose of through life optimisation of asset operations, maintenance and spares logistics. The Cranfield toolset has been developed and tested with use cases from aerospace, maritime, vehicles, as well as land based engineering assets

Room: Creative Hub, Town Hall (Ground Floor Space – the Southernmost end of the building) The University of Northampton, University Drive, Northampton, NN1 5PH.

Location: The University of Northampton, University Drive, Northampton, NN1 5PH. (Sat Nav doesn't work, the suggestion is putting Nunn Mills Road, Northampton, into your Sat Nav)

Parking: The new University campus is very security conscious, so it is suggested that people park nearby. The suggested locations include:

-Bedford Road car park just east of the Nunn Mills Road. 

- Alternatively, Morrison's Car Park (be aware of the parking time limit) and the Multi-Storey near the Theatre both are 5-10min work from the venue. Check on google maps before visiting.

Usual start time of 7:30pm arrive at 7pm.

All views and opinions are the author's and do not necessarily reflected those of any organisation they are associated with. Twitter: @scottturneruon

Computational Model to Recognise Emotions of Students with Aspergers

New paper

A. Dawood, S. Turner and P. Perepa, 
"Affective Computational Model to Extract Natural Affective States of Students with Asperger Syndrome (AS) in Computer-based Learning Environment.," in IEEE Access.
doi: 10.1109/ACCESS.2018.2879619

Abstract: 
This study was inspired by looking at the central role of emotion in the learning process, its impact on students’ performance; as well as the lack of affective computing models to detect and infer affective-cognitive states in real time for students with and without Asperger Syndrome (AS). This model overcomes gaps in other models that were designed for people with autism, which needed the use of sensors or physiological instrumentations to collect data. The model uses a webcam to capture students’ affective-cognitive states of confidence, uncertainty, engagement, anxiety, and boredom. These states have a dominant effect on the learning process. The model was trained and tested on a natural-spontaneous affective dataset for students with and without AS, which was collected for this purpose. The dataset was collected in an uncontrolled environment and included variations in culture, ethnicity, gender, facial and hairstyle, head movement, talking, glasses, illumination changes and background variation. The model structure used deep learning (DL) techniques like convolutional neural network (CNN) and long short-term memory (LSTM). DL is the-state-of-art tool that used to reduce data dimensionality and capturing non-linear complex features from simpler representations. The affective model provide reliable results with accuracy 90.06%. This model is the first model to detected affective states for adult students with AS without physiological or wearable instruments. For the first time, the occlusions in this model, like hand over face or head were considered an important indicator for affective states like boredom, anxiety, and uncertainty. These occlusions have been ignored in most other affective models. The essential information channels in this model are facial expressions, head movement, and eye gaze. The model can serve as an aided-technology for tutors to monitor and detect the behaviors of all students at the same time and help in predicting negative affective states during learning process.
keywords: {Autism;Computational modeling;Instruments;Tools;Face recognition;Physiology;Affective Model;Affective-Cognitive States;Autism;Asperger Syndrome;AS;CNN;Deep Learning;LSTM},
URL: https://ieeexplore.ieee.org/document/8522016


All views and opinions are the author's and do not necessarily reflected those of any organisation they are associated with. Twitter: @scottturneruon