Mobile cloud game in high performance computing environment

Mobile cloud game is a solution to play high-end games in indigent thin clients with a diversity of end-user devices, and as real-time gaming, mobile cloud game hosting game engines in the cloud. Moreover, frequent change in network quality is another issue that should be limited to run the real fast cloud game. Thus, reliable software components between cloud and user devices as clients, including using artificial intelligence (AI) algorithms such as machine learning, deep learning and so on will enhance the game performance, particularly in multiplayer and real-time conditions. In this paper, we list the mobile cloud game architecture in the high-performance computing (HPC) environment, where a load of the game will be distributed between servers as cloud and clients. The server node as clouds or clients will consist of more than one server with many processors (cores) or sometimes can be recognized as distributed computing. Using HPC for cloud games will boost the game performance where the execution times will be dispersed not only in some node in servers and clients but in many cores of each server or client. The involvement of the internet of things (IoT) and ubiquitous access from heterogeneous devices will give benefit to enjoyment in the game itself

Secure data sharing in cloud computing: a comprehensive review

Cloud Computing is an emerging technology, which relies on sharing computing resources. Sharing of data in the group is not secure as the cloud provider cannot be trusted. The fundamental difficulties in distributed computing of cloud suppliers is Data Security, Sharing, Resource scheduling and Energy consumption. Key-Aggregate cryptosystem used to secure private/public data in the cloud. This key is consistent size aggregate for adaptable decisions of ciphertext in cloud storage. Virtual Machines (VMs) provisioning is effectively empowered the cloud suppliers to effectively use their accessible resources and get higher benefits. The most effective method to share information resources among the individuals from the group in distributed storage is secure, flexible and efficient. Any data stored in different cloud data centers are corrupted, recovery using regenerative coding. Security is provided many techniques like Forward security, backward security, Key-Aggregate cryptosystem, Encryption and Re-encryption etc. The energy is reduced using Energy-Efficient Virtual Machines Scheduling in Multi-Tenant Data Centers

Neural Video Recovery for Cloud Gaming

Cloud gaming is a multi-billion dollar industry. A client in cloud gaming sends its movement to the game server on the Internet, which renders and transmits the resulting video back. In order to provide a good gaming experience, a latency below 80 ms is required. This means that video rendering, encoding, transmission, decoding, and display have to finish within that time frame, which is especially challenging to achieve due to server overload, network congestion, and losses. In this paper, we propose a new method for recovering lost or corrupted video frames in cloud gaming. Unlike traditional video frame recovery, our approach uses game states to significantly enhance recovery accuracy and utilizes partially decoded frames to recover lost portions. We develop a holistic system that consists of (i) efficiently extracting game states, (ii) modifying H.264 video decoder to generate a mask to indicate which portions of video frames need recovery, and (iii) designing a novel neural network to recover either complete or partial video frames. Our approach is extensively evaluated using iPhone 12 and laptop implementations, and we demonstrate the utility of game states in the game video recovery and the effectiveness of our overall design

Models and Methods for Network Selection and Balancing in Heterogeneous Scenarios

The outbreak of 5G technologies for wireless communications can be considered a response to the need for widespread coverage, in terms of connectivity and bandwidth, to guarantee broadband services, such as streaming or on-demand programs offered by the main television networks or new generation services based on augmented and virtual reality (AR / VR). The purpose of the study conducted for this thesis aims to solve two of the main problems that will occur with the outbreak of 5G, that is, the search for the best possible connectivity, in order to offer users the resources necessary to take advantage of the new generation services, and multicast as required by the eMBMS. The aim of the thesis is the search for innovative algorithms that will allow to obtain the best connectivity to offer users the resources necessary to use the 5G services in a heterogeneous scenario. Study UF that allows you to improve the search for the best candidate network and to achieve a balance that allows you to avoid congestion of the chosen networks. To achieve these two important focuses, I conducted a study on the main mathematical methods that made it possible to select the network based on QoS parameters based on the type of traffic made by users. A further goal was to improve the computational computation performance they present. Furthermore, I carried out a study in order to obtain an innovative algorithm that would allow the management of multicast. The algorithm that has been implemented responds to the needs present in the eMBMS, in realistic scenarios

Universal Mobile Service Execution Framework for Device-To-Device Collaborations

There are high demands of effective and high-performance of collaborations between mobile devices in the places where traditional Internet connections are unavailable, unreliable, or significantly overburdened, such as on a battlefield, disaster zones, isolated rural areas, or crowded public venues. To enable collaboration among the devices in opportunistic networks, code offloading and Remote Method Invocation are the two major mechanisms to ensure code portions of applications are successfully transmitted to and executed on the remote platforms. Although these domains are highly enjoyed in research for a decade, the limitations of multi-device connectivity, system error handling or cross platform compatibility prohibit these technologies from being broadly applied in the mobile industry. To address the above problems, we designed and developed UMSEF - an Universal Mobile Service Execution Framework, which is an innovative and radical approach for mobile computing in opportunistic networks. Our solution is built as a component-based mobile middleware architecture that is flexible and adaptive with multiple network topologies, tolerant for network errors and compatible for multiple platforms. We provided an effective algorithm to estimate the resource availability of a device for higher performance and energy consumption and a novel platform for mobile remote method invocation based on declarative annotations over multi-group device networks. The experiments in reality exposes our approach not only achieve the better performance and energy consumption, but can be extended to large-scaled ubiquitous or IoT systems

CoAP Infrastructure for IoT

The Internet of Things (IoT) can be seen as a large-scale network of billions of smart devices. Often IoT devices exchange data in small but numerous messages, which requires IoT services to be more scalable and reliable than ever. Traditional protocols that are known in the Web world does not fit well in the constrained environment that these devices operate in. Therefore many lightweight protocols specialized for the IoT have been studied, among which the Constrained Application Protocol (CoAP) stands out for its well-known REST paradigm and easy integration with existing Web. On the other hand, new paradigms such as Fog Computing emerges, attempting to avoid the centralized bottleneck in IoT services by moving computations to the edge of the network. Since a node of the Fog essentially belongs to relatively constrained environment, CoAP fits in well. Among the many attempts of building scalable and reliable systems, Erlang as a typical concurrency-oriented programming (COP) language has been battle tested in the telecom industry, which has similar requirements as the IoT. In order to explore the possibility of applying Erlang and COP in general to the IoT, this thesis presents an Erlang based CoAP server/client prototype ecoap with a flexible concurrency model that can scale up to an unconstrained environment like the Cloud and scale down to a constrained environment like an embedded platform. The flexibility of the presented server renders the same architecture applicable from Fog to Cloud. To evaluate its performance, the proposed server is compared with the mainstream CoAP implementation on an Amazon Web Service (AWS) Cloud instance and a Raspberry Pi 3, representing the unconstrained and constrained environment respectively. The ecoap server achieves comparable throughput, lower latency, and in general scales better than the other implementation in the Cloud and on the Raspberry Pi. The thesis yields positive results and demonstrates the value of the philosophy of Erlang in the IoT space

How to Place Your Apps in the Fog -- State of the Art and Open Challenges

Fog computing aims at extending the Cloud towards the IoT so to achieve improved QoS and to empower latency-sensitive and bandwidth-hungry applications. The Fog calls for novel models and algorithms to distribute multi-service applications in such a way that data processing occurs wherever it is best-placed, based on both functional and non-functional requirements. This survey reviews the existing methodologies to solve the application placement problem in the Fog, while pursuing three main objectives. First, it offers a comprehensive overview on the currently employed algorithms, on the availability of open-source prototypes, and on the size of test use cases. Second, it classifies the literature based on the application and Fog infrastructure characteristics that are captured by available models, with a focus on the considered constraints and the optimised metrics. Finally, it identifies some open challenges in application placement in the Fog