A novel multi-path traffic control mechanism in named data networking

Named Data Networking (NDN) as a promising future network architecture has been attracted a lot of attention. In NDN, to keep fairness between flows and avoid congestion, one of the most important issues is the traffic control. Although some works have been done to solve the traffic control problem by controlling the sending/forwarding of Interest, to the best of our knowledge, the cooperation between traffic control and forwarding strategy is largely ignored.

Therefore, to fill this gap, we propose an effective traffic control mechanism named Multi-path Flow Control(MFC) in this paper. Our proposal is the first to combine traffic control with multi-path forwarding strategy. In contrast with other traffic control mechanisms, MFC performs better in supplying different flows with fair service rates, enhancingnetwork throughput and avoiding “Waiting Interest”, which is a unique problem in NDN. Finally, numerical simulation results based on ndnSIM platform are illustrated to show the performance of the proposed scheme.

A home cloud-based home network auto-configuration using SDN

In recent years, the prevalence of network devices such as smartphones, smart appliances, and various sensors has increased. As a result, interest in and demand for home networks has steadily increased. So, auto-configuration problems in home networks are very important and must be addressed, because some household members will not be familiar with the Information Technology (IT) devices and thus find it difficult to use home networks. Many studies have been undertaken in a variety research fields and standards for the home network auto-configuration problem. However, the conventional research and standards have a significant problem in that each home device requires middleware to be installed and must follow a standard configuration for home gateways.

In this paper, we propose a new method for the auto-configuration of home networks in home cloud environments using the Software Defined Networking (SDN) controller. The SDN controller has two key roles for home network auto-configuration: auto-recognition and registration of home devices, and management of home devices according to the home network connection state. It allows the home network to be automatically configured without middleware and home gateways, which are required in the existing standards. In addition, home networks can be configured to utilize SDNs that satisfy the variousnetwork requirements and functions required by each of the home services. The experimental results verify the home network auto-configuration, the usability of the proposed method, and the SDN use case satisfy the requirements of each home service.

Software defined networking-based vehicular Adhoc Network with Fog Computing

Vehicular Adhoc Networks (VANETs) have been attracted a lot of research recent years. Although VANETs are deployed in reality offering several services, the current architecture has been facing many difficulties in deployment and management because of poor connectivity, less scalability, less flexibility and less intelligence. We propose a new VANET architecture called FSDN which combines two emergent computing and network paradigm Software Defined Networking (SDN) and Fog Computing as a prospective solution. SDN-based architecture provides flexibility, scalability, programmability and global knowledge while Fog Computing offers delay-sensitive and location-awareness services which could be satisfy the demands of future VANETs scenarios.

We figure out all the SDN-based VANET components as well as their functionality in the system. We also consider the system basic operations in which Fog Computing are leveraged to support surveillance services by taking into account resource manager and Fog orchestration models. The proposed architecture could resolve the main challenges in VANETs by augmenting Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Base Station communications and SDN centralized control while optimizing resources utility and reducing latency by integrating Fog Computing. Two use-cases for non-safety service (data streaming) and safety service (Lane-change assistance) are also presented to illustrate the benefits of our proposed architecture.

Realization of congestion in software defined networks

Software defined networking[1][9] has brought a rapid change in networking industry by providing programmability and abstraction. it also enables centralization of control in infrastructure. In this paper, we describe the results of our experiment to determine the performance of SDN by simulating Open vSwitch under conditions of congestion. We did experiments on MININET by creating four compute nodes and observing their behaviour under congestion. The connections were made by using open vSwitch (OVS).

We generated a lot of traffic between VMs using iperf[5] and determined at what level of traffic Open vSwitch[7] saturates leading to a drastic drop in throughput. We found that packet loss and throughput reduction can be observed when we increase number of parallel connection between nodes. Moreover the change is always sudden and drastic when it reaches the threshold value. Several experiments were performed to analyze the throughput variations caused due to traffic increase and the reason behind those variations.

A Partial Cache for Multimedia Content in Named Data Networking

Named Data Networking (NDN) is a novel transmission framework in future Internet. It is a content-centric network. The content is routed based on its unique name, not IP address in NDN. A NDN node broadcasts an interest packet with the requesting named content to NDN. A NDN node has the requested named content. It will encapsulate requested content into data packets and deliver them back to the requesting NDN node. If intermediate nodes are located on the routing path of requesting/replying named content.

They will also cache the named content for serving the next same requesting from different NDN nodes. However, multimedia content usually are used the streaming fashion to deliver over the Internet. It is an important issue to design a proper caching mechanism of named multimedia to satisfy the features of multimedia streaming. In this paper, we proposed a partial caching mechanism to temporally cache the named streaming content on the intermediate nodes in NDN. It can provide a better streaming service than the originated caching mechanism in NDN.

Energy consumption analysis of cloud-based video games streaming to mobile devices

Within the growing of the gaming industry and mobile technologies, cloud-based video games streaming to mobile devices gains popularity fast. However, several issues and challenges such as user responsiveness, video quality, service quality, operating cost and energy consumption have to be addressed. Previous research studies did not thoroughly investigate the energy consumption of mobile devices used for cloud gaming.

This paper focuses on how mobile device energy consumption is impacted by video game content characteristics, transmission protocol and wireless network type. The results show that game content characteristics impact energy consumption (i.e., up to 47% between games at maximum brightness for OLED screens), UDP is more energy efficient than TCP (i.e., up to 13.6%), while WiFi is more energy efficient than 3G (i.e., up to 40%). The outcome of this study can provide beneficial input for adaptive energy efficient cloud-based video games streaming mechanisms.

Anticipatory quality-resource allocation for multi-user mobile video streaming

Mobile video delivery forms the largest part of the traffic in cellular networks. Thus optimizing the resource allocation to satisfy a user’s quality of experience is becoming paramount in modern communications. This paper belongs to the line of research known as anticipatory networking that makes use of prediction of wireless capacity to improve communication performance.

In particular, we focus on the problem of optimal resource allocation for steady video delivery under maximum average quality constraints for multiple users. We formulate the problem as a piecewise linear program and provide a heuristic algorithm, which solution is close to optimal. Based on our formulation we are now able to trade off minimum video quality, average quality and offered network capacity.

Transmission distortion modeling for view synthesis prediction based 3-D video streaming

View synthesis prediction (VSP) is an important tool for improving the coding efficiency in the next generation three-dimensional (3-D) video systems. However, VSP will result in a new type of inter-view error propagation when the multi-view video plus depth (MVD) data are transmitted over the lossy networks. In this paper, this new type of error propagation is characterized and modeled.

Firstly, a new analytic model is formulated to estimate the expected transmission distortion caused by error propagation from the synthesized reference view. Then, the compound impact of the transmission distortions of both the texture video and the depth map on the quality of the synthetic reference view is mathematically analysed. Our extensive simulation results demonstrate that the proposed transmission distortion model is very accurate.

Adaptive Layer Switching Algorithm Based on Buffer Underflow Probability for Scalable Video Streaming Over Wireless Networks

Scalable Video Coding (SVC) has been raised as a promising technique to enable flexible videotransmission for mobile users with heterogeneous terminals and varying channel capacities. In this paper, we design an adaptive layer switching algorithm for on-demand scalable video service based on receiver’s buffer underflow probability (BUP). Since the low quality of channel may lead to a low buffer fullness, the buffer fullness is an indicator for reflecting the channel condition, and we define BUP for characterizing the mismatch between the video bitrate and the channel throughput. Accordingly, the adaptive SVC transmission problem is formulated as the adaptive adjustment of video layers based on BUP. This allows us to optimize the attainable video quality, while keeping BUP below a desired level.

In order to estimate BUP, we derive an analytical model based on the large deviation principles. Then, an online layer switching algorithm is proposed using this estimation model, which is capable of accommodating different channel qualities without any prior knowledge of the channel variations and of the video characteristics. We further introduce a perturbation-based layer switching approach for reducing the quality fluctuating issue caused by frequent layer switches, thus improving the viewer’s QoE. A system prototype is implemented to evaluate the success of the proposed method.We also conduct simulations in multiuser scenarios with real video traces and the results demonstrate that the proposed algorithm is capable of improving the playback experience, while keeping a low playback interruption rate and quality variation.

Optimal Rate Allocation for Video Streaming in Wireless Networks With User Dynamics

We consider the problem of optimal rate allocation and admission control for adaptive video streamingsessions in wireless networks with user dynamics. The central aim is to achieve an optimal tradeoff between several key objectives: maximizing the average rate utility per user, minimizing the temporal rate variability, and maximizing the number of users supported. We derive sample path upper bounds for the long-term net utility rate in terms of either a linear program or a concave optimization problem, depending on whether the admissible rate set is discrete or continuous.

We then show that the upper bounds are asymptotically achievable in large-scale systems by policies which either deny access to a user or assign it a fixed rate for its entire session, without relying on any advance knowledge of the duration. Moreover, the asymptotically optimal policies exhibit a specific structure, which allow them to be characterized through just a single variable, and have the further property that the induced offered load is unity. We exploit the latter insights to devise parsimonious online algorithms for learning and tracking the optimal rate assignments and establish the convergence of these algorithms. Extensive simulation experiments demonstrate that the proposed algorithms perform well, even in relatively small-scale systems.