Baris Yamansavascilar

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, implementation of a robust fault tolerance scheme for SDN-based networks is crucial. Existing SDN data plane fault tolerance approaches can be classified as reactive and proactive which may or may not rely on the controller, respectively. However, none of them qualifies as a complete solution, providing only partial remedies. In this work, we propose Dynamic Protection with Quality of Alternative Paths (DPQoAP) that considers not only the existing faults within the network but also the quality of alternative paths. As a result, we can sustain the QoS throughout the network after the recovery. We also investigate how application based parameters are affected by link failures. To this end, we explore the change in Quality of Experience (QoE) caused by link failures under different cases using Dynamic Adaptive Streaming over HTTP (DASH) for video streaming. On the other hand, even though DASH is proposed as a solution to improve the QoE affected by the dynamic conditions of the networks, it remains insufficient to handle the congested links that show the symptoms of a link failure. Thus, we apply the data plane fault tolerance approach in SDN to improve the QoE of DASH clients in the case of congestion as well as the failure. The performance of the proposed solutions is evaluated through various experiments considering the QoS and QoE parameters. It is observed that DPQoAP enhances the efficiency of the networking operations and adaptability of the applications.

In this paper, we develop a novel throughput-maximizing OFDMA scheduler for the multi-user MAC framework for the IEEE 802.11ax networks. The scheduler works both in the downlink and uplink directions and assigns resource units to stations using a linear programming technique considering load of each client, possible resource unit configurations, modulation-coding scheme of each client, and ageing factor of each client’s load. The performance of the proposed scheduler has been evaluated using the NS3 simulator and compared against the legacy MAC layer mechanism of IEEE 802.11 protocol (i.e., DCF/EDCA). Simulation results show that our proposed throughput-maximizing scheduler increases the total throughput in the network as well as decrease the average end-to-end delay regardless of the number of stations connected to the access point by prioritizing the traffic of clients connected via high modulation-coding schemes.

Since tremendous amount of traffic is generated in modern networks as a result of the mobile devices and video streaming, the congestion issue is faced more frequently in the networks. Accordingly, failures and performance losses in networks due to congestion result in deteriorated Quality of Experience (QoE) from the end user perspective that may cause financial and reputation loss for the service provider. Even though the new video streaming paradigm, Dynamic Adaptive Streaming over HTTP (DASH), is proposed as a solution for the changing condition of the networks, it is not sufficient considering the heavily loaded links that show the symptoms of link failures. Therefore, the flexible implementation of the data plane fault tolerance scheme that can be applied for other problems like congestion in networks is crucial. Thus, in this study, we apply the data plane fault tolerance approach in the Software-Defined Network to improve the QoE of DASH clients in the case of congestion rather than the failure. To detect the congestion in the network level, we use the Bidirectional Forwarding Protocol (BFD) that is originally implemented for link failures. In our experiments, we investigate the effect of the BFD interval, video segment size, and traffic load on QoE parameters. Our results show that if the fault tolerance approach is applied using a small BFD interval with a large segment size, QoE parameters are noticeably enhanced considering the non-applied case.

Recent developments in Internet technology have led to an increased importance of network traffic classification. In this study, we used machine-learning methods for the identification of applications using network traffic classification. Contrary to existing studies, which classify applications into categories like FTP, Instant Messaging, etc., we tried to identify popular end-user applications such as Facebook, Twitter, Skype and many more individually. We are motivated by the fact that individual identification of applications is of high importance for network security, QoS enforcement, and trend analysis. For our tests, we used UNB ISCX Network Traffic dataset and our internal dataset, consisting of 14 and 13 well-known applications respectively. In our experiments, we evaluated four classification algorithms, namely J48, Random Forest, k-NN, and Bayes Net. With the complete set of 111 features, k-NN gave the best result for the ISCX Dataset as 93.94% of accuracy using the value of k as 1, and Random Forest gave the best result for the internal dataset as 90.87% of accuracy. During the course of this study, the initial numbers of features were successfully reduced to two sets of 12 features specific to each dataset without a compromise to the success. Moreover, we observed a 2% increase in the success rate for the internal dataset. We believe that individual application identification by applying machine-learning methods is a viable solution and currently we are investigating a two-tier approach to make it more resilient to in category confusion.

Recent developments in Internet technology have led to an increased importance of Software-Defined Networks (SDN). Due to advantages of this new network model that controls the network centrally, many service providers and vendors expect that traditional networks should be superseded by SDN. However, because of their centralized nature, they are vulnerable in terms of reliability and fault-tolerance issues both on data and control planes. Thus, developing such a fault-tolerant SDN design is quite important. In this study, fault tolerance on the data plane is targeted by considering various network and performance measurements. In the experiments, the impact of the topology size, frequency of packets, and the number of flows in the current route on the recovery time is tested. Moreover, local and global recovery approaches are compared.

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 recognize the daily activities of a person. 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. For our test scenarios, we built a dataset including a tri-axial accelerometer sensor data of 7 daily activities: walking, jogging, sitting, lying, standing, walking upstairs and walking downstairs. We collected these activities with a sampling rate of 80 Hz by using 5 seconds window size. Then, we downsampled the collected data to 40 Hz, 20 Hz, 10 Hz, 5 Hz and 1 Hz by using 4, 3, 2, and 1 seconds window sizes, respectively. Thus, we could evaluate the variation of the accuracy and CPU utilization. Our test results showed that when the sampling rate increases, both the accuracy and the CPU utilization become greater. Moreover, we observed that, for a fixed sampling rate, when window size increases, CPU utilization decreases.