Let's depart together: Efficient play request dispatching in cloud gaming

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.

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-based systems often face the problem of dispatching a stream of jobs to run on cloud servers in an online manner. Each job has a size that defines the resource demand for running the job. Each job is assigned to run on a cloud server upon its arrival and the job departs after it completes. The departure time of a job, however, is not known at the time of its arrival. Each cloud server has a fixed resource capacity and the total resource demand of all the jobs running on a server cannot exceed its capacity at all times. The objective of job dispatching is to minimize the total cost of the servers used, where the cost of renting each cloud server is proportional to its running hours by “pay-as-you-go” billing. The above job dispatching problem can be modeled as a variant of the dynamic bin packing (DBP) problem known as MinUsageTime DBP. In this paper, we study the competitiveness bounds of MinUsageTime DBP. We establish an improved lower bound on the competitive ratio of Any Fit family of packing algorithms, and a new upper bound of $\mu +3$ on the competitive ratio of the commonly used First Fit packing algorithm, where $\mu $ is the max/min job duration ratio. Our result significantly reduces the gap between the upper and lower bounds for the MinUsageTime DBP problem to a constant value independent of $\mu $ , and shows that First Fit packing is near optimal for MinUsageTime DBP.

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 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.

The emerging Cloud Gaming Service provides a highly accessible video gaming experience. With Cloud Gaming, potential players without enough local resource can access high-quality gaming using low-spec devices. With advancing technology, we consider that if the processing power at low-spec devices can be well harvested, the quality delivered on Cloud Gaming can be further improved. Therefore, we propose a Hybrid-Streaming System that aimed at improving the graphic quality delivered by Cloud Gaming. By utilizing the available rendering power from both the Cloud Server and client PC, the system distributes rendering operations to both sides to achieve the desired improvement. Quantitative results show the proposed method improves graphics quality, as well as reducing the server’s workload while attaining acceptable network bandwidth consumption levels.

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 recent years, the concept of cloud game remains a hot spot in both academic and game industry, proved by a large number of related research papers and the emergence of various cloud gaming platforms. Cloud game has competitive advantages over other traditional games. However, cloud games are more susceptible to latency than traditional online games because the data of cloud games are processed in remote servers. This paper shows the researches and progress in cloud gaming, analyzes the main aspects that may influence cloud gaming latency and proposes feasible methods to improve these problems.

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.

Cloud gaming (CG) has gradually gained popularity. By leveling shared computing resources on the cloud, CG technology allows those without expensive hardware to enjoy AAA games using a low-end device. However, the bandwidth requirement for streaming game video is high, which can cause backbone network congestion for large-scale deployment and expensive bandwidth bills. To address this challenge, the authors proposed an innovative edge-assisted computing architecture that collaboratively uses AI-powered foveated rendering (FR) and super-resolution (SR). Using FR, the cloud server can stream gaming video in lower resolution, significantly reducing the transmitted data volume. The edge server will then upscale the video using a game-specific SR model, recovering the quality of the video, especially for the areas players pay the most attention. The authors built a prototype system called FRSR and did thorough, objective comparative experiments to demonstrate that this architecture can reduce bandwidth usage by 39.47% compared with classic CG implementation for similar perceived quality.

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.

Examines the future for cloud gaming services. These services have leveraged real-time video streaming technologies that have a long history of development. Cloud gaming refers to the technologies that offload parts of game software from traditional game consoles or personal computers (PCs) to powerful and elastic cloud infrastructures. Cloud gaming makes perfect sense to: 1) gamers, who otherwise have to constantly upgrade their consoles or PCs, which is certainly no fun and costly; 2) cloud service providers, who can sell the already-deployed and idling cloud resources to support the cutting-edge games that are extremely resource hungry; and 3) game developers, who no longer need to spend months to port their games to different platforms. As such, cloud gaming has attracted significant attention from both academia and industry.

Long network latency negatively impacts the performance of online games, and thus mechanisms are needed to mitigate its effects in order to provide a high-quality gaming experience. In this paper, we propose an indirect relay system (IRS) to forward game-state updates over detour paths in order to reduce the round-trip time (RTT) among players. We first collect extensive traces for RTTs among actual players in online games. We then analyze these traces to quantify the potential performance gain of the detour routing. Our analysis reveals that substantial reduction in the RTTs is possible. For example, our results indicate that more than 40% of players can observe at least 100 ms of RTT reduction by routing game-state updates through 1-hop detour paths. Because of the reduction in RTTs, players can join more gaming sessions that were not available to them due to long RTTs of the direct paths. Most importantly, we design and implement a complete IRS system for online games. To the best of our knowledge, this is the first system that directly reduces RTTs among players in online games, while previous works in the literature mitigate the long RTT issue by either hiding it from players or preventing players with high RTTs from being in the same game session. We implement the proposed IRS system and deploy it on 500 PlanetLab nodes. The results from real experiments show that the IRS system improves the online gaming quality from several aspects, while incurring negligible network and processing overheads. In particular, we observe that, with the proposed IRS system, more than 80% of game sessions achieve 100 ms or higher RTT reduction.

Trauma services: a profit center?

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.