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Cloud gaming systems host the game in the cloud, and stream players' gaming videos to the terminals in the form of encode video frames. To address the high bandwidth issue of real-time gaming video transmission, a cloudlet-assisted multiplayer cloud gaming system was proposed to encourage cooperative video sharing via a secondary ad-hoc network, on the purpose of exploiting the similarities of video frames among multiple players in a same game. However, the video cooperative sharing among players also introduces fairness problems. In this paper, we complete the ad hoc cloudlet-assisted cloud gaming system by further considering the mobility of terminal devices and the diversity of network quality for distinct players. With mathematical formulation, we study the players' behavior in cooperative sharing patterns and propose a reputation-based multiplayer fairness scheme in terms of frame encoding. Experimental results illustrate the impact of mobility on the system performance and evaluate that the proposed solution provides better fairness gaming ecosystem compared to the existing platform.

In this paper, we study the problem of how to dispatch the play requests to the cloud servers in a cloud gaming system. We show that the dispatching strategy of play requests may heavily affect the total service cost of the cloud gaming system. The play request dispatching problem can be considered as a variant of the dynamic bin packing problem. However, we show that the classical bin packing algorithms such as First Fit and Best Fit are not efficient in dispatching the play requests in cloud gaming due to the diurnal workload pattern of online games. To address this issue, we propose an efficient request dispatching algorithm that assigns the play requests according to the predicted ending times of the game sessions. Through extensive evaluations using real online gaming traces, we show that the proposed dispatching algorithm can significantly reduce the resource waste of the cloud servers and thus decrease the total service cost compared to the First Fit and Best Fit algorithms.

Cloud gaming has been a prospective paradigm through which users can realistically play high-end games on its low-end devices by outsourcing both computation and rendering jobs to remote cloud computers. The paper contains a thorough survey of cloud gaming systems, including its architecture, technology that allows the system, and performance issues. The paper describes the general architecture of cloud games, such as non-transparent and transparent ones, and outlines the major Quality of Service (QoS) and Quality of Experience (QoE) metrics, such as latency, bitrate, energy usage, and delay sensitivity of the users. Moreover, different optimization schemes embraced in the cloud gaming systems are discussed, such as distributed architecture, resources distribution schemes, sharing of GPUs, compression data schemes, adaptive transmission and edge computing contribution to latency minimization. The paper also evaluates available commercial cloud gaming services and business models to identify current trends in the market and challenges in deploying the services. According to the literature review, the paper highlights that efficient control of latency, efficient management of resources, and edge-assisted architectures are essential in enhancing scalability and user experience. Lastly, the paper provides the possible direction and technological solutions required to make cloud gaming a mass adoption technology.

Cloud gaming has been very popular in recent years, but issues relating to maintaining low interaction delay to guarantee satisfactory user experience are still prevalent. We observe that the server-side processing delay in cloud gaming system could be heavily influenced by how the resources are partitioned among processes. However, finding the optimal partitioning policy that minimizes the response delay faces several critical challenges. First, fine-grained resource partitioning is non-trivial due to the limitations of hardwre-based resource isolation techniques. Second, game wokload is highly dynamic and unpredictable, making the design of efficient resource partitioning policy more challenging. In this article, we propose an online resource partitioning framework for reducing response delay in cloud gaming, which has several promising properties. First, we divide the processes into disjoint groups and partition resources among process groups, which greatly simplifies the resource partitioning problem while ensuring high partitioning effectiveness. Second, to tackle dynamic workload changes, we classify game workloads into several clusters and maintain separate process grouping plan for each cluster. Third, we leverage reinforcement learning to adaptively choose the best actions for minimizing response delay in real time. We evaluate the proposed framework in a real cloud gaming environment using several real games. The experimental results show that our approach can reduce the response delay by 22 to 41 percent compared to a system without resource partitioning, and outperforms other resource partitioning policies significantly.

Cloud gaming enables people playing graphically intensive games from their less powerful, or even outdated computing devices. It is challenging to realize cloud gaming as it requires minimal latency in server-side processing, rendering and streaming, which are expensive in terms of resource requirements, e.g., powerful GPU servers. Commercial gaming providers, e.g., Google Stadia, Amazon Luna, etc., hardly disclose any information on how they optimize gaming performance and cloud cost. In this work, we aim to investigate resource cost optimization for such cloud gaming systems. In contrast to previous work which have been focusing more on theoretical approaches, we deliver a fully functional, cost-optimized cloud-native gaming system, called CloudNPlay, implemented entirely on AWS Lambda, EC2 and other AWS services. We have conducted extensive evaluations on the gaming performance including latency and frame rates; as well as cloud resource cost reduction. The results indicate that games hosted on CloudNPlay are highly playable. More importantly, CloudNPlay reduces the resource cost by around 24% compared to a non-optimized deployment. We plan to open-source CloudNPlay to facilitate further research in cloud gaming.

Massively Multiplayer Online Cloud Game (MMOCG) is a combination of cloud gaming and Massively Multiplayer Online Game (MMOG). Running MMOGs on the cloud can reduce the cost for the players, attract potential players, and meet the nowadays preference of mobile devices. However, the MMOCGs introduce new challenges that need to be addressed. MMOGs usually consume more computational resources because of their sophisticated graphics. Hosting MMOCGs can be more expensive for game operators than hosting other genres of games on the cloud. In order to earn a decent profit, the cost of hosting MMOCGs must be reduced to an acceptable cost. Moreover, MMOCG introduce extra cloud servers to MMOG, which increased the burden of network transmission. So, the MMOCGs must have a good quality of service (QoS) and quality of experience (QoE) to retain the players. In this thesis, we study the server allocation problem to improve MMOCGs in many aspects, including money cost, QoS, and QoE. We proposed three different server allocation strategies to address the challenges of MMOCGs in different situations. We first consider the MMOCG system with several cloud gaming platforms that lie in different locations in the world, each can provide a type of server that can host MMOG on them. The cloud gaming system only obtains the information necessary for gaming. With only the log-in information, a server allocation problem is defined to reduce the money cost of hosting MMOCGs and provide an acceptable network latency for the players. The problem is formulated into an optimization problem that aims to minimize the weighted combination of server rental cost, data transfer cost, and network latency. A genetic algorithm is developed to solve the minimization problem. Extensive experiments are conducted to evaluate the proposed method and compare it with the state-of-the-art as well. The experimental results show that the method gives a lower money cost and a shorter network latency most of the time. We then consider the MMOCG system that can collect the past logging in and out data of the players. We proposed a server allocation problem that aims to reduce the cost of cloud server rental and improve the QoE of the system. A genetic algorithm with gaming time prediction is proposed to solve the problem. The gaming time of the players logging into the system is predicted by the model trained by a neural network. We conduct experiments to evaluate our method and the proposed gaming time prediction model. The results show that our prediction model can achieve almost highly accurate prediction and the server allocation strategy we proposed outperforms the prior art in both server rental and QoE. At last, we consider the influence of QoE on the movement of players during gaming time and propose a server allocation problem aiming to improve the QoE of players during gaming time. We propose a genetic algorithm with trace prediction to address this problem. A trace prediction that predicts the future positions of players for a few time steps when they log into the MMOCG system. Experiments are conducted to evaluate our method by measuring the network latency, mean opinion score, and the time consuming computation. The results show that our method outperforms the genetic algorithm without trace prediction in over 50% of the cases.

SummaryVirtual desktop infrastructure (VDI) solutions seek to provide a satisfactory user experience at the client side when accessing remote desktop applications, even from mobile devices with limited capabilities. This paper presents a new approach, improving on previous work by the authors, in which a combination of Virtual Network Computing (VNC) and streaming protocols allowed efficient remote web access to virtualized applications within a cloud architecture. The new approach simplifies virtual machine templates, from which virtual machine instances are deployed, by centralizing software modules, greatly simplifying their management. Our new contribution consists of an integrated solution with specific WebM video encoding modules in charge of application visual output processing, an Hypertext Transfer Protocol (HTTP) streaming server, and a VNC server. The solution can be installed in the hypervisor of the host machines instead of replicating the servers and modules throughout the guest (virtual) machines that run the virtualized applications. Consequently, their implementations are unique and independent of the operating system of the virtual machines. In short, it is not necessary to provide different implementations for different operating systems, which reduces the complexity of virtual machine templates and greatly simplies platform management. To demonstrate our solution, we have modified the Quick Emulator (QEMU)‐Kernel‐based Virtual Machine (KVM) hypervisor source code accordingly. We also present qualitative and quantitative analyses that demonstrate that the new approach is advantageous in terms of software management and quality of experience, compared with our previous work and other well‐known thin clients, contributing to the enhancement of VDI systems. Copyright © 2015 John Wiley & Sons, Ltd.

Virtual reality (VR) based games are a type of game that provides immersive gaming experience, allowing players to dive into the virtual world of the game being played. VR-based games require a high minimum computer specification, so thin clients cannot play VR-based games properly. This research aims to see how to enable thin computers to play VR-based games by utilizing cloud gaming technology. Using a high specification computer as a server, an android device as a VR headset, this Final Project implements a VR headset device so that it can be used in conjunction with cloud gaming services to be able to play VR-based games on thin computers and see how well the implementation by seeing the result from computer resources used and the Quality of Services. With Parsec cloud gaming services, the application carried out in this Final Project can run well on computers with low specifications. CPU usage on the client computer when the service is running is high at 91% usage, with 2818 MB RAM usage. Quality of Service is obtained when setting the highest quality preset, with a throughput of 16MB with a delay of about 2 ms. VR games that are played can run well with a minimum bandwidth of 15 Mbps selected from the Frame per Second (FPS) results obtained to reach 56 FPS with medium quality settings.

Cloud gaming is a relatively new paradigm in which the game is rendered in the cloud and is streamed to an end-user device through a thin client. Latency is a key challenge for cloud gaming. In order to optimize the end-to-end latency, it is first necessary to understand how the end-to-end latency builds up from the mobile device to the cloud gaming server. In this paper we dissect the delays occurring in the mobile device and measure access delays in various networks and network conditions. We also perform a Europe-wide latency measurement study to find the optimal server locations and see how the number of server locations affects the network delay. The results are compared to limits found for perceivable delays in recent human-computer interaction studies. We show that the limits can be achieved only with the latest mobile devices with specific control methods. In addition, we study the expected latency reduction by near future technological development and show that its potential impact is bigger on the end-to-end latency than that of replication of the service and server placement optimization.

With the rapid development of cloud computing technologies, cloud gaming has gained wide attention from the industrial field. By executing the gaming software on the cloud and transiting the rendered game scenes back to the client, game players can play games anywhere on any device. However, the centralized architecture of existing cloud gaming platforms causes serious network latency, which deteriorates user experience significantly. To reduce network latency and relieve the workload on the mobile client, we propose and implement a cloudlet-based mobile cloud gaming system design. By deploying the cloudlets in the nearby region of mobile users, we can provide computational resources to the mobile clients and complete tasks such as gaming state maintenance, game scene rendering, etc. The system can also reduce the gaming latency and the workload on the client greatly. We use the open-source cloud computing software to build a cloudlet system and develop an Android-based cloud gaming client, which can receive and display gaming video streams, and map and upload the game operations. Finally, we conduct experimental evaluation of our mobile cloud gaming prototype. The results show that our proposed solution can significantly reduce the fraction of network latency.

The combination of cloud computing and advancements in GPU have made many real time services possible, including Cloud Gaming (CG). Doing all the process-intensive tasks in the cloud frees players from upgrading their heterogeneous devices and installing new software, and lets them play wherever and whenever. However, higher quality is always demanded by players. For instance, as frame rate has major impact on the player’s gaming performance, demand of higher frame rate is increasing. On the other hand, service providers aim to offer cost effective services. Management of the graphic-intensive CG service demand and maximizing the service providers' benefits is an issue that must be addressed properly. As remote GPU plays the main role of rendering and is the most expensive infrastructure rented by the service provider, its appropriate management is vital to address the above issue. To do so, we formulate the problem in an efficient manner and propose two methods to maximize both GPU utilization and the users’ quality of experience (QoE) at the same time, subject to the constraints of the servers. Our methods are based on two metaheuristic algorithms to solve an NP-Hard optimization problem for GPU-based server selection. Our simulation results shows that by increasing the number of players, both algorithms have increasing performance in terms of GPU utilization, reduced capacity wastage, and QoE.

Cloud gaming or gaming as a service, the newest entry in the online gaming world, leverages the well-known concept of cloud computing to provide real-time gaming services to players. This gaming paradigm provides affordable, flexible, and high performance solutions for end users with constrained computing resources and enables them to play high-end graphic games on low-end thin clients, because it renders everything in the cloud and simply streams the resulting high-quality video to the player. Despite its advantages, cloud gaming’s quality of experience suffers from high and unstable end-to-end delay. According to this fact of cloud gaming, datacenters are in charge of performing complex rendering and video encoding computations, delivering high-quality gaming experience to gamers which requires an efficient and smart resource allocation mechanism to allot resources (e.g., memory and network bandwidth) to gaming sessions consistent with their requirements. In this paper, we propose a bi-objective optimization method to find an optimum path for packet transmission within a data center by minimizing delay and maximizing bandwidth utilization. We use a metaheuristic model, called analytic hierarchy process, to solve the NP-complete optimization problem. The resulting method is an analytic hierarchy process-based game aware routing (AGAR) scheme that considers requested game type and requirements in terms of delay and bandwidth to select the best routing path for a game session in a cloud gaming network. The method executes within a software defined network controller, which affords it a global view of the data center with respect to communication delay and available bandwidth. Simulation results indicate that the AGAR can reduce the end-to-end delay by up to 9.5% compared with three other conventional representative methods: the delay-based Dijkstra, the equal cost multi-path routing, and the Hedera routing algorithms. In addition, we demonstrate that the proposed method assigns game flows to network paths and OpenFlow switches in a balanced manner that prevents potential network bottlenecks.

In cloud gaming systems, the game program runs at servers in the cloud, while clients access game services by sending input events to the servers and receiving game scenes via video streaming. In this paradigm, servers are responsible for all performance-intensive operations, and thus suffer from poor scalability. An alternative paradigm is called graphics streaming, in which graphics commands and data are offloaded to the clients for local rendering, thereby mitigating the server's burden and allowing more concurrent game sessions. Unfortunately, this approach is bandwidth consuming, due to large amounts of graphic commands and geometry data. In this paper, we present LiveRender, an open source gaming system that remedies the problem by implementing a suite of bandwidth optimization techniques including intra-frame compression, inter-frame compression, and caching, establishing what we call compressed graphics streaming. Experiments results show that the new approach is able to reduce bandwidth consumption by 52-73% compared to raw graphics streaming, with no perceptible difference in video quality and reduced response delay. Compared with the video streaming approach, LiveRender achieves a traffic reduction of 40-90% with even improved video quality and substantially smaller response delay, while enabling higher concurrency at the server.

Cloud gaming allows users to play high-quality games on low-end devices by offloading game processing to the cloud. However, network latency remains a significant issue affecting the gaming experience. Speculative execution is a promising approach to hide network latency by predicting and transmitting future frames early. However, existing methods generate excessive compute load and network traffic due to many potential input patterns. This paper introduces a pattern reduction method that uses a bit field representation of the input and facilitates efficient speculative execution in cloud games. There are two pattern reduction techniques: analyzing temporal patterns to detect frequent transitions and using LSTM-based predictions to estimate input probabilities. Experiments using actual gaming data show that the proposed methods significantly reduce rendered frames and network traffic versus prior speculative execution methods. The results demonstrate the method's effectiveness and scalability across diverse game genres.

Decentralized cloud datacenter reconsolidation through emergent and topology-aware behavior