CloudNPlay: Resource Optimization for A Cloud-Native Gaming System

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 is promising to provide high-quality game services by outsourcing game execution to cloud so that users can access games via thin clients (e.g., smartphones or tablets). However, existing cloud gaming systems su er from low GPU utilization in the virtualized environment. Moreover, GPU resources are scheduled in units of virtual machines (VMs) and this kind of coarse-grained scheduling at the VM-level fails to fully exploit GPU processing capacity. In this paper, we present ShareRender, a cloud gaming sys- tem that o oads graphics workloads within VMs directly to GPUs, bypassing GPU virtualization. For each game running in a VM, ShareRender starts a graphics wrapper to intercept frame rendering requests and assign them to render agents responsible for frame rendering on GPUs. Thanks to the exible workload assignment among multiple render agents, ShareRender enables ne-grained resource sharing at the frame-level to signi cantly improve GPU utilization. Further more, we design an online algorithm to determine workload assignment and migration of render agents, which considers the tradeo between minimizing the number of active server and low agent migration cost. We conduct experiments on real deployment and trace-driven simulations to evaluate the performance of ShareRender under di erent system settings. The results show that ShareRender outperforms the existing video-streaming-based cloud gaming system by over 4 times.

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.

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.

In this paper, scalability and performance of AWS Lambda are discussed with emphasis on serverless technologies. It delves into best practices for efficient resource management, cost optimization, and code execution. Through detailed case studies, it provides real world examples and potential issues like cold start latency and restrictions in the amount of time versus CPU cores available for execution. It also divulges an analysis of solutions to some of the evident problems and enables developers and organizations intending to use AWS Lambda for developing versatile and high-performance applications. Altogether, this research provides directions for improving the serverless infrastructure [1].

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.

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.

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.

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.

This chapter provides a comprehensive examination of AWS's native cost control and optimization tools, offering insights into managing cloud expenditure effectively. The discussion begins with AWS Cost Explorer, detailing its capabilities for analyzing historical spending patterns and generating future cost projections through advanced forecasting features. The chapter explores AWS Compute Optimizer's role in analyzing resource utilization patterns and recommending optimal instance types, highlighting its machine learning-driven approach. AWS Trusted Advisor's cost optimization pillar is examined, focusing on its automated checks for underutilized resources and potential savings opportunities. The recently introduced AWS Cost Optimization Hub is presented as a centralized platform that aggregates recommendations from various AWS services, while the AWS Cost Optimization Intelligent Dashboard provides enhanced visibility into cost-saving opportunities through customizable visualizations. The integration of AWS Q for QuickSight is discussed, demonstrating how natural language processing can simplify cost analysis and reporting. The chapter also covers AWS Config's role in maintaining cost-effective resource configurations and AWS License Manager's capabilities in optimizing software license costs. Each tool's features, implementation strategies, and best practices are detailed, providing readers with actionable insights for building a comprehensive cost optimization strategy in their AWS environment.

Contrary to conventional gaming-on-demand services that stream gaming video from cloud to players' terminals, a decomposed cloud gaming platform supports flexible migrations of gaming components between the cloud server and the players' terminals. In this paper, we present the design and implementation of the proposed decomposed gaming system. The cognitive resource optimization of the system under distinct targets, including the minimization of cloud, network, and terminal resources and response delay, subject to quality of service (QoS) assurance, is formulated as a graph partitioning problem that is solved by exhaustive searches. Simulations and experimental results demonstrate the feasibility of cognitive resource management in a cloud gaming system to efficiently adapt to variations in the service environments, such as increasing the number of supported devices and reducing the network bandwidth consumption of user terminals, while satisfying different QoS requirements for gaming sessions. We also suggest two heuristic algorithms based on local greedy and genetic algorithm approaches, which can potentially provide scalable but suboptimal solutions in large-scale implementations.

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.

With the advent of 5G, supporting high-quality game streaming applications on edge devices has become a reality. This is evidenced by a recent surge in cloud gaming applications on mobile devices. In contrast to video streaming applications, interactive games require much more compute power for supporting improved rendering (such as 4K streaming) with the stipulated frames-per second (FPS) constraints. This in turn consumes more battery power in a power-constrained mobile device. Thus, the state-of-the-art gaming applications suffer from lower video quality (QoS) and/or energy efficiency. While there has been a plethora of recent works on optimizing game streaming applications, to our knowledge, there is no study that systematically investigates the <QoS, Energy> design pairs on the end-to-end game streaming pipeline across the cloud, network, and edge devices to understand the individual contributions of the different stages of the pipeline for improving the overall QoS and energy efficiency. In this context, this paper presents a comprehensive performance and power analysis of the entire game streaming pipeline consisting of the server/cloud side, network, and edge. Through extensive measurements with a high-end workstation mimicking the cloud end, an open-source platform (Moonlight-GameStreaming) emulating the edge device/mobile platform, and two network settings (WiFi and 5G) we conduct a detailed measurement-based study with seven representative games with different characteristics. We characterize the performance in terms of frame latency, QoS, bitrate, and energy consumption for different stages of the gaming pipeline. Our study shows that the rendering stage and the encoding stage at the cloud end are the bottlenecks to support 4K streaming. While 5G is certainly more suitable for supporting enhanced video quality with 4K streaming, it is more expensive in terms of power consumption compared to WiFi. Further, fluctuations in 5G network quality can lead to huge frame drops thus affecting QoS, which needs to be addressed by a coordinated design between the edge device and the server. Finally, the network interface and the decoder units in a mobile platform need more energy-efficient design to support high quality games at a lower cost. These observations should help in designing more cost-effective future cloud gaming platforms.

Good user experience with interactive cloud-based multimedia applications, such as cloud gaming and cloud-based VR, requires low end-to-end latency and large amounts of downstream network bandwidth at the same time. In this paper, we present a foveated video streaming system for cloud gaming. The system adapts video stream quality by adjusting the encoding parameters on the fly to match the player's gaze position. We conduct measurements with a prototype that we developed for a cloud gaming system in conjunction with eye tracker hardware. Evaluation results suggest that such foveated streaming can reduce bandwidth requirements by even more than 50% depending on parametrization of the foveated video coding and that it is feasible from the latency perspective.

Cloud gaming is an ideal solution to provide the desired GPU rendering for the Virtual Reality (VR) gaming while reducing the requirements for the weight, price, and capabilities of the head-mounted display. However, transmitting the high-resolution video requires a large amount of network bandwidth. In this paper, we present a cloud VR gaming system that significantly improves the coding efficiency by using a proposed Object-Aware Video Encoding (OAVE). Different from the video service, the game engine can offer information about the objects in the game scene. By combining this information and the eye-tracking data, the encoder compressed the rendered video in an object-dependent way that sets the quantization parameter according to the object of the user's gaze. Through the subjective experiment conducted on the prototype of the system, above 40% to 47% bandwidth saving can be achieved compared with the conventional VR gaming system without the proposed optimization.