Baris Yamansavascilar

Research Scientist

Deep Learning Systems for Edge Computing

Sept. 15, 2019 - Present

The improvements in the edge computing technology pave the road for diversified applications that demand real-time interaction. However, due to the mobility of the end-users and the dynamic edge environment, it becomes challenging to handle the task offloading with high performance. Considering the dynamics of the edge computing networks, the policy for task orchestration should be learned from the environment in order to make accurate decisions. Thus, we investigate the deep learning solutions including Deep Reinforcement Learning (DRL) and Federated Learning (FL) that are able to meet the dynamic needs of different application types under various loads in edge computing. This research is the main research subject for my PhD studies.

Distributed Data Processing and Applications for Mobile Edge Computing

Aug. 1, 2017 - June 1, 2018

Considering the tremendous amount of data emerging by mobile devices each day, Mobile Edge Computing (MEC) solutions would have critical importance. To this end, this research project aimed at finding possible effective solutions. This project was funded by Netas.

Service Management on Multitier IT Architecture

Jan. 1, 2017 - Jan. 1, 2019

The goal of this project was to bring cloudlets providing service with minimal latency, and cloud servers, which can service when there is a need for high processing power while latency is not so significant, together in a multi-tier architecture. This project was funded by Bogazici University Scientific Research Projects (BAP).

Fault Tolerance in SDN Data Plane

Sept. 15, 2016 - June 15, 2019

Failures in networks result in service disruptions which may cause deteriorated Quality of Service (QoS) for the end-users. Since SDN is becoming the mainstream paradigm for networks, the implementation of a robust fault tolerance scheme for SDN-based networks is crucial. Moreover, that robust fault tolerance scheme should ensure the stability for QoS and Quality of Experience (QoE). To this end, we investigated

  • A novel dynamic protection method considering the quality of alternative paths.

  • How video quality and thus QoE would fluctuate when a failure occurs.

  • The effect of the detection of the congestion in the link layer operating Bidirectional Forwarding Detection (BFD) rather than the application layer.

  • The impact of BFD intervals on the QoE parameters along with the video segment size to come up with a feasible operational range where the fault tolerance approach is beneficial for the congestion case.

Note that this study was my thesis subject in my MS studies. 

Deep Learning Systems for Edge Computing

Sept. 15, 2019 - Present

The improvements in the edge computing technology pave the road for diversified applications that demand real-time interaction. However, due to the mobility of the end-users and the dynamic edge environment, it becomes challenging to handle the task offloading with high performance. Considering the dynamics of the edge computing networks, the policy for task orchestration should be learned from the environment in order to make accurate decisions. Thus, we investigate the deep learning solutions including Deep Reinforcement Learning (DRL) and Federated Learning (FL) that are able to meet the dynamic needs of different application types under various loads in edge computing. This research is the main research subject for my PhD studies.

Advanced Threat Detection Using Machine Learning Techniques

July 1, 2015 - July 1, 2016

The project aimed at detecting and preventing Intrusion and Advanced Persistent Thread (APT) attacks via manifesting anomalies in network flows and system logs using machine learning algorithms. DeterLab Testbed was used as a development and test platform for the various work packages of the project. Moreover, it was funded by The Scientific and Technological Research Council of Turkey (TUBITAK).

Network Traffic Classification Using Machine Learning Techniques

Sept. 15, 2014 - June 15, 2015

Recent developments on the Internet have led to increase the importance of network traffic classification. Classification of large-scale network traffic can provide much important information for network management, QoS and network security. In this study, the influence of different network speeds to accuracy was evaluated and a comparison between packet-based and time-based approaches was performed.

 

Activity Recognition on Smartphones

Sept. 15, 2013 - June 15, 2014

Great hardware and software capabilities of mobile devices allow us to research new scientific fields. Activity recognition is one of the main research areas for smartphones. Built-in sensors of a standard smartphone, such as accelerometer, magnetometer, gyroscope, enable us to predict the daily activities of a person. However, continuous samplings from each sensor increase the CPU utilization and cause the battery to die sooner. In this study, we focused on the window sizes and the sampling rates in order to observe how they affect to the accuracy and CPU utilization.

Fault Tolerance in SDN Data Plane

Sept. 15, 2016 - June 15, 2019

Failures in networks result in service disruptions which may cause deteriorated Quality of Service (QoS) for the end-users. Since SDN is becoming the mainstream paradigm for networks, the implementation of a robust fault tolerance scheme for SDN-based networks is crucial. Moreover, that robust fault tolerance scheme should ensure the stability for QoS and Quality of Experience (QoE). To this end, we investigated

  • A novel dynamic protection method considering the quality of alternative paths.

  • How video quality and thus QoE would fluctuate when a failure occurs.

  • The effect of the detection of the congestion in the link layer operating Bidirectional Forwarding Detection (BFD) rather than the application layer.

  • The impact of BFD intervals on the QoE parameters along with the video segment size to come up with a feasible operational range where the fault tolerance approach is beneficial for the congestion case.

Note that this study was my thesis subject in my MS studies. 

OFDMA Scheduler Development for IEEE 802.11ax Dual Band Wireless Access Point with MU-MAC Frame Support

March 1, 2019 - March 1, 2020

This project aimed at implementing an MU MAC OFDMA scheduler on an AP that supports IEEE 802.11ax standard (Wifi 6). The expectation from the scheduler was to consider the fairness and efficiency between HE STAs regarding their uplink and downlink traffic. The project was funded by AirTies Wireless Networks.

Fault Tolerance in SDN Data Plane

Sept. 15, 2016 - June 15, 2019

Failures in networks result in service disruptions which may cause deteriorated Quality of Service (QoS) for the end-users. Since SDN is becoming the mainstream paradigm for networks, the implementation of a robust fault tolerance scheme for SDN-based networks is crucial. Moreover, that robust fault tolerance scheme should ensure the stability for QoS and Quality of Experience (QoE). To this end, we investigated

  • A novel dynamic protection method considering the quality of alternative paths.

  • How video quality and thus QoE would fluctuate when a failure occurs.

  • The effect of the detection of the congestion in the link layer operating Bidirectional Forwarding Detection (BFD) rather than the application layer.

  • The impact of BFD intervals on the QoE parameters along with the video segment size to come up with a feasible operational range where the fault tolerance approach is beneficial for the congestion case.

Note that this study was my thesis subject in my MS studies. 

Network Traffic Classification Using Machine Learning Techniques

Sept. 15, 2014 - June 15, 2015

Recent developments on the Internet have led to increase the importance of network traffic classification. Classification of large-scale network traffic can provide much important information for network management, QoS and network security. In this study, the influence of different network speeds to accuracy was evaluated and a comparison between packet-based and time-based approaches was performed.

 

OFDMA Scheduler Development for IEEE 802.11ax Dual Band Wireless Access Point with MU-MAC Frame Support

March 1, 2019 - March 1, 2020

This project aimed at implementing an MU MAC OFDMA scheduler on an AP that supports IEEE 802.11ax standard (Wifi 6). The expectation from the scheduler was to consider the fairness and efficiency between HE STAs regarding their uplink and downlink traffic. The project was funded by AirTies Wireless Networks.