Multiplayer Cloud Gaming Research Articles

A distributed auction-based algorithm for virtual machine placement in multiplayer cloud gaming infrastructures

A distributed auction-based algorithm for virtual machine placement in multiplayer cloud gaming infrastructures

A distributed auction-based algorithm for virtual machine placement in multiplayer cloud gaming infrastructures

A distributed auction-based algorithm for virtual machine placement in multiplayer cloud gaming infrastructures

Cost-Efficient and Quality-of-Experience-Aware Player Request Scheduling and Rendering Server Allocation for Edge-Computing-Assisted Multiplayer Cloud Gaming

Prompted by the remarkable progress in both cloud computing and GPU virtualization, cloud gaming has been attracting more and more attention in the gaming industry. With the cloud gaming model, players do not need to download or install the game on local devices, and constantly upgrade their devices. Despite these advantages, cloud gaming faces several challenges for its success, including long response delay, poor game fairness, and high operational cost. To this end, this article proposes an edge computing-assisted multiplayer cloud gaming system named ECACG to improve multiplayer cloud gaming experiences and operating costs by offloading the game rendering task to the nearby edge server. Based on the ECACG, two decision processes are completed. One is player request scheduling and the other is rendering server allocation. The decision problem is formulated into a constrained multiobjective optimization model. A novel hybrid algorithm based on deep reinforcement learning and heuristic strategy is developed to solve the optimization problem. The effectiveness of the proposed ECACG is evaluated by simulation experiments based on the real-world parameters. The simulation results show that compared with the existing schemes, the proposed ECACG can achieve lower rental costs and better fairness, while providing the good-enough response delay for players.

Boosted Metaheuristic Algorithms for QoE-Aware Server Selection in Multiplayer Cloud Gaming

Cloud Gaming (CG) provides a high performance and cost-effective solution where players with low-end devices can play high-end games without the need for advanced hardware. A cloud-based video game system offloads all the computational tasks to the cloud. Considering the dynamic nature of game workloads and resource capacity, resource management is still a significant challenge. Since CG is a real-time gaming service, graphics processing units (GPUs) are necessary to accelerate game scene rendering. GPUs are one of the most expensive resources in a CG platform. Therefore, service providers have a strong incentive to utilize GPUs efficiently to maximize their economic profit. In addition, players' quality of game experience (QoE) is a crucial parameter that can directly affect a service provider's profit and must be taken into account in any resource scheduling optimization. To satisfy both parties, in this paper we propose two efficient methods for GPU based server selection in CG. The proposed methods are an improved version of two well-known metaheuristic algorithms called Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), which we refer to as Boosted-PSO and Boosted-GA, respectively. The proposed methods consider service providers' profits and players' experience simultaneously. Our objective is to maximize GPU utilization, which will not only lead to the service provider's economic benefit, but also increase the player's QoE. Our simulation results show that compared to the existing methods to solve such an NP-Hard optimization problem, our Boosted-PSO method, followed by Boosted-GA, achieves the highest efficiency in terms of GPU utilization, capacity wastage, and player's QoE.

Cost-Efficient and Quality of Experience-Aware Provisioning of Virtual Machines for Multiplayer Cloud Gaming in Geographically Distributed Data Centers

Prompted by the remarkable progress in both cloud computing and GPU virtualization, cloud gaming has become increasingly more popular in the gaming industry. In cloud gaming, games are stored and run on cloud servers and the gamers interact with games through thin clients. Cloud gaming service providers generally employ multiple geographically distributed data centers to deliver their services. The main challenge for cloud gaming service providers is to find the best tradeoff between two contradicting objectives: reducing the infrastructure operating costs and increasing the quality of player's experience. In this paper, we address a virtual machine provisioning problem for multiplayer cloud gaming with the objective of minimizing both the inter-player delay among interacting players and the electricity costs of cloud gaming service providers, while providing the good-enough response delay to gamers. We formulate the problem into a constrained multiobjective optimization problem and propose an improved grey wolf algorithm to solve the problem. The performance of our proposed algorithm are assessed by simulation experiments based on the real-world parameters. The results show the superior performance of the proposed approach in comparison with the state-of-the-art approaches applied to similar problems.

The Server Allocation Problem for Session-Based Multiplayer Cloud Gaming

Advances in cloud computing and GPU virtualization are allowing the game industry to move into a cloud gaming era. In this paper, we consider multiplayer cloud gaming (MCG), which is the natural integration of multiplayer online gaming and cloud gaming paradigms. With MCG, a game server and a set of rendering servers for the players need to be located and launched in the clouds for each game session. We formulate an MCG server allocation problem with the objective of minimizing the total server rental and bandwidth cost charged by the cloud to support an MCG session. The MCG server allocation problem is hard to solve optimally. We propose several efficient heuristics to address the problem and carry out theoretical analysis for the proposed hill-climbing algorithm. We conduct extensive experiments using real Internet latency and cloud pricing datasets to evaluate the effectiveness of our proposed algorithms as well as several alternatives. Experimental results show that our best algorithm can achieve near-optimal cost under real-time latency constraints.

Delay-Aware Quality Optimization in Cloud-Assisted Video Streaming System

Cloud-assisted video streaming has emerged as a new paradigm to optimize multimedia content distribution over the Internet. This article investigates the problem of streaming cloud-assisted real-time video to multiple destinations (e.g., cloud video conferencing, multi-player cloud gaming, etc.) over lossy communication networks. The user diversity and network dynamics result in the delay differences among multiple destinations. This research proposes <underline>D</underline>ifferentiated cloud-<underline>A</underline>ssisted <underline>VI</underline>deo <underline>S</underline>treaming (DAVIS) framework, which proactively leverages such delay differences in video coding and transmission optimization. First, we analytically formulate the optimization problem of joint coding and transmission to maximize received video quality. Second, we develop a quality optimization framework that integrates the video representation selection and FEC (Forward Error Correction) packet interleaving. The proposed DAVIS is able to effectively perform differentiated quality optimization for multiple destinations by taking advantage of the delay differences in cloud-assisted video streaming system. We conduct the performance evaluation through extensive experiments with the Amazon EC2 instances and Exata emulation platform. Evaluation results show that DAVIS outperforms the reference cloud-assisted streaming solutions in video quality and delay performance.

Quality-of-Experience Optimization for a Cloud Gaming System With <italic>Ad Hoc</italic> Cloudlet Assistance

Cloud gaming systems host the game in the cloud, while Gameplays and views are streamed to the players’ terminals in the form of encoded video frames. To address the high-bandwidth issue of real-time gaming video transmission, we have proposed a cloudlet-assisted multiplayer cloud gaming system to encourage cooperative video sharing, which exploits the similarities of video frames among multiple players in the same crowd playing the same game via a secondary ad hoc network. In this paper, we provide a detailed modeling of the proposed system, including the correlation between video frames, mobility of terminal devices, and diversity of network quality of service for distinct players. With necessary mathematical formulations, we study the players’ behaviors regarding the cooperative sharing patterns to optimize the system performance in terms of the quality of users’ experience. Also, heuristic algorithms are proposed to reduce the computational complexity. Empirical study and trace-driven simulation results illustrate the impact of mobility on the system performance and show that the proposed solution is able to provide better quality of experience compared with the existing platform.

Advances in Mobile Cloud Computing

Recently the cloud computing technology has emerged as a new information technology infrastructure for the fast developing IT industry. In cloud computing, information is permanently stored in large-scale data centers on the Internet all over the world and temporarily accessed and cached on clients including desktops and portable PCs, sensors, etc. With the “cloud” as a metaphor for the Internet, cloud computing promises to deliver massively scalable IT-enabled data, software, and hardware capabilities as a service to external clients with Internet accesses. And the highly scalable computation capability of the cloud data centers can further assist and accelerate most of our computation intensive services and works effectively. Therefore the cloud computing has been envisioned as the key technology to achieve economies of scale in the deployment and operation of IT solutions. Regarding recent advances in mobile communication technologies, a new wave of user demands for rich mobile service experience has been fueled. Mobile users always expect broadband Internet access wherever they go, interact with each other via social networks while moving; furthermore, they are seeking ubiquitous access to a wealth of media-based contents and services. Because mobile devices are resource limited inherently, it is essential for the cloud to provide computational support for many media-rich applications. The combination of mobile media and cloud computing highly arises many technical challenges, and the fundamental tension between resource-hungry multimedia streams and power-limitedmobile devices has to be resolved. The effort for providing a universal rich-media experience across any screen is typically hindered by the heterogeneity amongst ever-evolving mobile devices, as manifested in their different physical form factors, middleware platforms, and interactive functions. Furthermore the developments of innovative pervasive mobile services, e.g., mobile video streaming, rich media dissemination, surveillance, gaming, e-health care, etc., can be greatly facilitated by Mobile Cloud Computing (MCC) platform employing emerged and emerging technologies. This issue consists of five papers addressing the various aspects ofMCC, such as mobile cloud computing services and applications, mobile cloud gaming, integration of MCC with Cyber-Physical Systems (CPS), as well as the relationship between cloud computing and big data. The issue opens with a comprehensive survey on diverse ways of combining cloud computing and mobile platforms towards a new computing/ communications paradigm. In “Mobile Cloud Computing: A Survey, State of Art and Future Directions”, M. R. Rahimi et al. provided a complete survey on the field of MCC on its state of art applications, research challenges, opportunities and future research directions. The typical MCC applications are discussed, such as mobile learning, commerce, healthcare and social networks. The authors further identify research gaps from three critical aspects from enhancing the efficiency of task offloading to improve the business model, and the research challenges and opportunities are made clear within the context of the existing schemes. In the paper “ACloudlet-AssistedMultiplayer Cloud Gaming System” by W. Cai et al., the authors proposed a novel cloudlet-assisted multiplayer cloud gaming system, in which the mobile devices are connected to the cloud server for realtime interactive game videos, while sharing the received video M. Chen (*) Huazhong University of Science and Technology, Room 410, Building Southern No. 1, 1037 Luoyu Road, Wuhan 430074, China e-mail: minchen@ieee.org

A Cloudlet-Assisted Multiplayer Cloud Gaming System

The unstable network connectivity is the bottleneck of providing Gaming as a service (GaaS) for mobile devices. Therefore, the most critical technical challenge is to compress and transmit the real-time gaming video, so that during the gaming session, the expected server transmission rate over the bandwidth-limited mobile network can be minimized, while satisfying the quality of experience for the players. Inspired by the idea of peer-to-peer sharing between multiple players, we propose a cloudlet-assisted multiplayer cloud gaming system, in which the mobile devices are connected to the cloud server for real-time interactive game videos, while sharing the received video frames with their peers via an ad hoc cloudlet. Experimental results show that expected server transmission rate can be significantly reduced compared to the conventional video encoding schemes for cloud games.