I received B.S. and M.S. degrees from Bilkent University, Ankara, Turkey, in 2002 and 2005, in electrical and electronics engineering. I received Ph.D. degree from Osaka University, Japan, in information science and technology, in 2008. I was the recipient of Monbukagakusho Graduate Scholarship from the Ministry of Education, Science, Sports and Culture of Japan between 2005–2008. I am a Specially Appointed Associate Professor at Osaka University. My research interests are network function virtualization (NFV), neural networks, optical networks, traffic engineering and simulation. I have worked on several government funded projects on computer networks and telecommunications in Europe and Japan.I received the Best M.S. Thesis Award from IEEE student branch in Turkey in 2005. I was the recipient of "Best Paper Award" at the First International Conference on Evolving Internet (INTERNET 2009). I am the editor of IEEE Technology Policy & Ethics Newsletter of IEEE Future Directions Technical Community. I am serving in the steering committee of International Conference on Evolving Internet. I am a Senior Member of IEEE.
Ph.D. in Information Science and Technology
Dissertation: Design and Performance Evaluation of Small-buffered Optical Packet Switched Networks
M.S. in Electrical and Electronics Engineering on Computer Networks Area
Thesis: Combined Use of Prioritized AIMD and Flow-Based Traffic Splitting for Robust TCP Load Balancing
Summer School organized by European Union
Topic: Routing and Multi-Layer Traffic Engineering in Next Generation IP Networks
B.S. in Electrical and Electronics Engineering
Specially Appointed Associate Professor
Specially Appointed Researcher
Specially Appointed Assistant Professor
Specially Appointed Researcher
• Research Assistant
• Teaching Assistant
• Administrator of computer systems of Department of Electrical and Electronics Engineering
• Administrator of Bilkent University Information Networks Laboratory
My research topics and some selected journal publications are as follows:
One of the models in the literature for modeling the behavior of the brain is the Bayesian attractor model, which is a kind of machine-learning algorithm. According to this model, the brain assigns stochastic variables to possible decisions (attractors) and chooses one of them when enough evidence is collected from sensory systems to achieve a confidence level high enough to make a decision. We introduce a software defined networking (SDN) application based on a brain-inspired Bayesian attractor model for identification of the current traffic pattern for the supervision and automation of Internet of things (IoT) networks that exhibit a limited number of traffic patterns. In a real SDN testbed, we demonstrate that our SDN application can identify the traffic patterns using a limited set of fluctuating network statistics of edge link utilization.
We propose a multipath Network Function Visualization (NFV) orchestration for optimum placement of VNFs and service chains by a two stage linear programming optimization that increases the resiliency against DDoS attacks. Under a DDoS attack, SDN controller switches the routes affected by the attack to the secondary paths with filter VNFs for filtering DDoS traffic, which increases the DDoS traffic absorption rate of the network instantly. As the severity of DDoS decreases, the operator gains time to calculate and apply a solution specifically optimized for the DDoS pattern.
I was involved in Yuragi project of Osaka University, which proposes applying mathematical neural network models derived from biological systems to computer networks. Yuragi models the gene regulatory network of cells as an attractor selection based neural network for controlling VNT (virtual network topology) in optical networks. The attractor VNT candidates are stored in a Hopfield network, which is a kind of recurrent artificial neural network. I designed heuristic algorithms for designing attractor VNTs for Yuragi that are robust against large scale network failures.
I designed a hybrid optical network architecture, which uses both path (circuit) and packet switching. It carries the long TCP flows over path switching wavelengths in order to maximize average the throughput of the flows. It dynamically changes the ratio of path and packet switching wavelengths network-wide according to the traffic characteristics. I implemented a large scale optical packet and path(circuit) switching network simulator for this research. To the best of my knowledge it is currently the fastest and most scalable simulator for packet level simulation of TCP flows on optical networks in the literature. I have simulated mesh optical networks with 80Gbps link speed using around 10^10 TCP flows.
Using a M∕G∕c∕c Markov chain queuing model I proposed an analytical framework for calculating the path (circuit) blocking probabilities and reservation delays in path switching optical WDM networks. There are numerous analytical models in the literature, but to the best of my knowledge my analysis is the most accurate one in the literature.
In my Ph.D. thesis I challenged the famous rule of thumb, which states that network routers require a buffer size of Bandwidth*RTT in order to achieve high utilization with TCP flows. I modelled the packet drop rate of different buffer architectures with TCP traffic by analysis and simulation. I proposed novel network architectures, which greatly decrease the buffering requirements of both electronic and optical networks.
We studied the effects of the number of justifiers on TCP performance in an optical burst switching (OBS) network. Our simulations show that increasing the number of assemblers per destination reduces the negative effects of synchronization between TCP flows occurring as a result of burst losses. I designed and implemented an advanced optical burst network simulator for this project and released its source code on the Internet available here. Some independent survey papers selected my simulator as the most advanced OBS simulator among all related free and commercial simulators.
I proposed a QoS based network architecture for electronic networks using TCP load balancing traffic engineering methodology and a random early rerouting algorithm that controls the queuing delay difference between the two alternative paths. I showed that avoiding packet reordering by flow level splitting significantly improves the network performance.
My M.S. Thesis on this project won the Best M.S. Thesis Award of Electrical and Electronics Department of Bilkent University.
The source code of OBS (optical burst switching) extension that I implemented for ns-2 simulator is here. The new version is updated for ns2.35. It was presented and used in "nOBS: an ns2 based simulation tool for performance evaluation of TCP traffic in OBS networks" journal paper available here.
The ns-2 simulator module and simulation scripts that I implemented for the simulations in the paper "Combined use of prioritized AIMD and flow-based traffic splitting for robust TCP load balancing," are here. The paper is here.
The source code and executables of the encoder and the differential cryptanalysis key finder programs that I implemented for the MacGuffin block cipher algorithm are here. The encoder program creates 100000 random cyphertext plaintext pairs by encrypting with 8-block MacGuffin cipher algorithm with 128-bit input key and the findkey program calculates the secret subkeys of the encrypted ciphertexts by differential cryptanalysis.
Advanced Network Architecture Laboratory,
Graduate School of Information Science and Technology,
1-5 Yamadaoka, Suita,
Osaka 565-0871, Japan