Muge Sayit

Bio: Muge Sayit is an academic researcher from Ege University. The author has contributed to research in topics: Network packet & Video quality. The author has an hindex of 7, co-authored 45 publications receiving 186 citations.

SDN for segment based flow routing of DASH

Abstract: In this paper we propose an SDN based dynamic path selection for HTTP-based video streaming. MPEG-DASH is a recently proposed standard allowing rate adaptation over HTTP. On the other hand, Software Defined Networking (SDN) is a new network architecture, which allows determining routes of packet flows by an external controller software. In this study we develop an optimization model aiming to obtain maximum throughput for DASH services by selecting the optimal paths for video packet flows over SDN. The simulations show that the clients in the proposed system receive better QoE in terms of video bitrate, outage duration and startup delay when compared to the clients running in Internet's best effort.

An SDN-assisted system design for improving performance of SVC-DASH

Abstract: Today, the most of the video streaming system provides quality adaptation and prefers to send their packets over HTTP. MPEG group has standardized Dynamic Adaptive HTTP Streaming (DASH) regarding this tendency on the adaptive HTTP streaming. Besides providing quality adaptation with a non-scalable codec, DASH standard also allows Scalable Video Coding (SVC) to adapt quality. Software Defined Networks (SDN) is a recently emerged networking paradigm. SDN enables to separate control and data plane of computer networks and hence provides flexibility to network operators to implement their own routing approaches. In this paper, we propose a system for increasing Quality of Experience (QoE) of SVC-DASH clients by utilizing SDN. Our experimental results show that the proposed system provides an increase in received video quality and decrease in outage duration and startup delay when compared to the performance of the client running over todays Internet implementing shortest path routing.

Towards QoS-aware routing for DASH utilizing MPTCP over SDN

Abstract: Multipath TCP (MPTCP) has gained great attention by the researchers and network application developers due to its features providing better bandwidth utilization and higher reliability recently. Utilizing MPTCP in the datacenters provides performance gain to the applications. If the underlying network has Software Defined Networking (SDN) architecture, the routing of the MPTCP subflows can be specialized. In this paper, we propose an architecture utilizing MPTCP in an SDN-based ISP in order to serve guaranteed services to the video streaming clients. The number of MPTCP subflows and routes of the subflows are determined based on a mixed integer linear programming model. The simulation results show that the proposed architecture provides increase in received throughput and decrease in duration of outages when it is compared to a general-purposed throughput maximization approach.

QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

Abstract: The highly demanding Over-The-Top (OTT) multimedia applications pose increased challenges to Internet Service Providers (ISPs) for assuring a reasonable Quality of Experience (QoE) to their customers due to lack of flexibility, agility and scalability in traditional networks. The future networks are shifting towards the cloudification of the network resources via Software Defined Networks (SDN) and Network Function Virtualization (NFV). This will equip ISPs with cutting-edge technologies to provide service customization during service delivery and offer QoE which meets customers’ needs via intelligent QoE control and management approaches. Towards this end, we provide in this paper a tutorial and a comprehensive survey of QoE management solutions in current and future networks. We start with a high-level description of QoE management for multimedia services, which integrates QoE modelling, monitoring, and optimization. This followed by a discussion of HTTP Adaptive Streaming (HAS) solutions as the dominant technique for streaming videos over the best-effort Internet. We then summarize the key elements in SDN/NFV along with an overview of ongoing research projects, standardization activities and use cases related to SDN, NFV, and other emerging applications. We provide a survey of the state-of-the-art of QoE management techniques categorized into three different groups: a) QoE-aware/driven strategies using SDN and/or NFV; b) QoE-aware/driven approaches for adaptive streaming over emerging architectures such as multi-access edge computing, cloud/fog computing, and information-centric networking; and c) extended QoE management approaches in new domains such as immersive augmented and virtual reality, mulsemedia and video gaming applications. Based on the review, we present a list of identified future QoE management challenges regarding emerging multimedia applications, network management and orchestration, network slicing and collaborative service management in softwarized networks. Finally, we provide a discussion on future research directions with a focus on emerging research areas in QoE management, such as QoE-oriented business models, QoE-based big data strategies, and scalability issues in QoE optimization.

Content Popularity Prediction Towards Location-Aware Mobile Edge Caching

Abstract: Mobile edge caching enables content delivery within the radio access network, which effectively alleviates the backhaul burden and reduces response time. To fully exploit edge storage resources, the most popular contents should be identified and cached. Observing that user demands on certain contents vary greatly at different locations, this paper devises location-customized caching schemes to maximize the total content hit rate. Specifically, a linear model is used to estimate the future content hit rate. For the case where the model noise is zero-mean, a ridge regression based online algorithm with positive perturbation is proposed. Regret analysis indicates that the proposed algorithm asymptotically approaches the optimal caching strategy in the long run. When the noise structure is unknown, an $H_{\infty}$ filter based online algorithm is further proposed by taking a prescribed threshold as input, which guarantees prediction accuracy even under the worst-case noise process. Both online algorithms require no training phases, and hence are robust to the time-varying user demands. The underlying causes of estimation errors of both algorithms are numerically analyzed. Moreover, extensive experiments on real world dataset are conducted to validate the applicability of the proposed algorithms. It is demonstrated that those algorithms can be applied to scenarios with different noise features, and are able to make adaptive caching decisions, achieving content hit rate that is comparable to that via the hindsight optimal strategy.

Artificial Intelligence Enabled Software Defined Networking: A Comprehensive Overview

Abstract: Software-defined networking (SDN) represents a promising networking architecture that combines central management and network programmability. SDN separates the control plane from the data plane and moves the network management to a central point, called the controller that can be programmed and used as the brain of the network. Recently, the research community has shown an increased tendency to benefit from the recent advancements in the artificial intelligence (AI) field to provide learning abilities and better decision making in SDN. In this study, the authors provide a detailed overview of the recent efforts to include AI in SDN. The study showed that the research efforts focused on three main sub-fields of AI namely: machine learning, meta-heuristics and fuzzy inference systems. Accordingly, in this work, the authors investigate their different application areas and potential use, as well as the improvements achieved by including AI-based techniques in the SDN paradigm.

Building Tianjin driving cycle based on linear discriminant analysis

Abstract: Driving cycles are standardized measurement procedure for the certification of vehicles’ economy and emission, and could help evaluate driving distance and new vehicular technologies. Thus driving cycle is always a hot research topic in vehicle industry. Linear discriminant analysis is a typical multivariate statistical method which has been used in many fields such as geology and economics in recent years, but its application to driving cycles is scarce. In this paper, Tianjin driving cycle is developed by using linear discriminant analysis. The effectiveness of the developed driving cycle is confirmed by comparing the parameter of the driving cycle and real-world driving data and evaluating the economy of electric vehicle. The uniqueness of this methodology is also discussed compared with traditional methodology in cycle development. This research could offer a new methodology for building driving cycles and has reference value to related researches.