Clouds + Games: A multifaceted approach

The computer game landscape is changing: people play games on multiple computing devices with heterogeneous form-factors, capability, and connectivity. Providing high playability on such devices concurrently is difficult. To enhance the gaming experience, designers could leverage abundant and elastic cloud resources, but current cloud platforms aren't optimized for highly interactive games. Existing studies focus on streaming-based cloud gaming, which is a special case for the more general cloud game architecture. The authors explain how to integrate techniques from the cloud and game research communities into a complete architecture for enhanced online gaming quality. They examine several open issues that appear only when clouds and games are put together. © 2014 IEEE

Use of Cloud Gaming in Education

The use of digital games in education has been the subject of research for many years and their usefulness has been confirmed by many studies and research projects. Standardized tests, such as PISA test, show that respondents achieved better reading, math and physics results if they used the computer more for gaming-related activities. It has been proven that the application of video games in education increases student motivation, improves several types of key skills—social and intellectual skills, reflexes and concentration. Nevertheless, there are several challenges associated with the application of video games in schools and they can be categorized as technical (network and end device limitations), competency (teachers’ knowledge in the area), qualitative (lack of educational games of high quality), and financial (high cost of purchasing games and equipment). The novel architecture for delivery of gaming content commonly referred to as “cloud gaming” has the potential to solve most of the present challenges of using games in education. A well-designed cloud gaming platform would enable seamless and simple usage for both students and teachers. While solving most of the present problems, cloud gaming introduces a set of new research challenges which will be discussed in this section

QoE-Centric Control and Management of Multimedia Services in Software Defined and Virtualized Networks

Multimedia services consumption has increased tremendously since the deployment of 4G/LTE networks. Mobile video services (e.g., YouTube and Mobile TV) on smart devices are expected to continue to grow with the emergence and evolution of future networks such as 5G. The end user’s demand for services with better quality from service providers has triggered a trend towards Quality of Experience (QoE) - centric network management through efficient utilization of network resources. However, existing network technologies are either unable to adapt to diverse changing network conditions or limited in available resources. This has posed challenges to service providers for provisioning of QoE-centric multimedia services. New networking solutions such as Software Defined Networking (SDN) and Network Function Virtualization (NFV) can provide better solutions in terms of QoE control and management of multimedia services in emerging and future networks. The features of SDN, such as adaptability, programmability and cost-effectiveness make it suitable for bandwidth-intensive multimedia applications such as live video streaming, 3D/HD video and video gaming. However, the delivery of multimedia services over SDN/NFV networks to achieve optimized QoE, and the overall QoE-centric network resource management remain an open question especially in the advent development of future softwarized networks. The work in this thesis intends to investigate, design and develop novel approaches for QoE-centric control and management of multimedia services (with a focus on video streaming services) over software defined and virtualized networks. First, a video quality management scheme based on the traffic intensity under Dynamic Adaptive Video Streaming over HTTP (DASH) using SDN is developed. The proposed scheme can mitigate virtual port queue congestion which may cause buffering or stalling events during video streaming, thus, reducing the video quality. A QoE-driven resource allocation mechanism is designed and developed for improving the end user’s QoE for video streaming services. The aim of this approach is to find the best combination of network node functions that can provide an optimized QoE level to end-users through network node cooperation. Furthermore, a novel QoE-centric management scheme is proposed and developed, which utilizes Multipath TCP (MPTCP) and Segment Routing (SR) to enhance QoE for video streaming services over SDN/NFV-based networks. The goal of this strategy is to enable service providers to route network traffic through multiple disjointed bandwidth-satisfying paths and meet specific service QoE guarantees to the end-users. Extensive experiments demonstrated that the proposed schemes in this work improve the video quality significantly compared with the state-of-the- art approaches. The thesis further proposes the path protections and link failure-free MPTCP/SR-based architecture that increases survivability, resilience, availability and robustness of future networks. The proposed path protection and dynamic link recovery scheme achieves a minimum time to recover from a failed link and avoids link congestion in softwarized networks

Leveraging Intelligent Computation Offloading with Fog/Edge Computing for Tactile Internet: Advantages and Limitations

[EN] With the recent advancement in wireless communication and networks, we are at the doorstep of the Tactile Internet. The Tactile Internet aims to enable the skill delivery and thereafter democratize the specialized skills for many emerging applications (e.g., remote medical, industrial machinery, remote robotics, autonomous driving). In this article, we start with the motivation of applying intelligent edge computing for computation offloading in the Tactile Internet. Afterward, we outline the main research challenges to leverage edge intelligence at the master, network, and controlled domain of the Tactile Internet. The key research challenges in the Tactile Internet lie in its stringent requirements such as ultra-low latency, ultra-high reliability, and almost zero service outage. We also discuss major entities in intelligent edge computing and their role in the Tactile Internet. Finally, several potential research challenges in edge intelligence for the Tactile Internet are highlighted.This work was supported in part by the National Natural Science Foundation of China under Grant 61901128, and Agile Edge Intelligence for Delay Sensitive IoT (AgilE-IoT) project (Grant No. 9131-00119B) of Independent Research Fund Denmark (DFF).Mukherjee, M.; Guo, M.; Lloret, J.; Zhang, Q. (2020). Leveraging Intelligent Computation Offloading with Fog/Edge Computing for Tactile Internet: Advantages and Limitations. IEEE Network. 34(5):322-329. https://doi.org/10.1109/MNET.001.200000432232934

Exploring traffic and QoS management mechanisms to support mobile cloud computing using service localisation in heterogeneous environments

In recent years, mobile devices have evolved to support an amalgam of multimedia applications and content. However, the small size of these devices poses a limit the amount of local computing resources. The emergence of Cloud technology has set the ground for an era of task offloading for mobile devices and we are now seeing the deployment of applications that make more extensive use of Cloud processing as a means of augmenting the capabilities of mobiles. Mobile Cloud Computing is the term used to describe the convergence of these technologies towards applications and mechanisms that offload tasks from mobile devices to the Cloud. In order for mobile devices to access Cloud resources and successfully offload tasks there, a solution for constant and reliable connectivity is required. The proliferation of wireless technology ensures that networks are available almost everywhere in an urban environment and mobile devices can stay connected to a network at all times. However, user mobility is often the cause of intermittent connectivity that affects the performance of applications and ultimately degrades the user experience. 5th Generation Networks are introducing mechanisms that enable constant and reliable connectivity through seamless handovers between networks and provide the foundation for a tighter coupling between Cloud resources and mobiles. This convergence of technologies creates new challenges in the areas of traffic management and QoS provisioning. The constant connectivity to and reliance of mobile devices on Cloud resources have the potential of creating large traffic flows between networks. Furthermore, depending on the type of application generating the traffic flow, very strict QoS may be required from the networks as suboptimal performance may severely degrade an application’s functionality. In this thesis, I propose a new service delivery framework, centred on the convergence of Mobile Cloud Computing and 5G networks for the purpose of optimising service delivery in a mobile environment. The framework is used as a guideline for identifying different aspects of service delivery in a mobile environment and for providing a path for future research in this field. The focus of the thesis is placed on the service delivery mechanisms that are responsible for optimising the QoS and managing network traffic. I present a solution for managing traffic through dynamic service localisation according to user mobility and device connectivity. I implement a prototype of the solution in a virtualised environment as a proof of concept and demonstrate the functionality and results gathered from experimentation. Finally, I present a new approach to modelling network performance by taking into account user mobility. The model considers the overall performance of a persistent connection as the mobile node switches between different networks. Results from the model can be used to determine which networks will negatively affect application performance and what impact they will have for the duration of the user's movement. The proposed model is evaluated using an analytical approac