The Server Allocation Problem for Session-Based Multiplayer Cloud Gaming

Deng, Yunhua; Li, Yusen; Seet, Ronald; Tang, Xueyan; Cai, Wentong

doi:10.1109/tmm.2017.2760621

Cited by 48 publications

(24 citation statements)

References 47 publications

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“…In fact, the considered use cases relies on one VM (or container) per user, as it usually happens in mobile (cloud) gaming [46]. In the case of online multi-player gaming, a single virtualized instance (container or VM) is deployed for rendering per user at the edge [47]. Thus, the fundamental observations from our experiments still hold.…”

Section: B Experimental Resultsmentioning

confidence: 82%

Edge Computing for the Internet of Things: A Case Study

Premsankar

Francesco

Taleb

2018

IEEE Internet Things J.

535

238

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The amount of data generated by sensors, actuators and other devices in the Internet of Things (IoT) has substantially increased in the last few years. IoT data are currently processed in the cloud, mostly through computing resources located in distant data centers. As a consequence, network bandwidth and communication latency become serious bottlenecks. This article advocates edge computing for emerging IoT applications that leverage sensor streams to augment interactive applications. First, we classify and survey current edge computing architectures and platforms, then describe key IoT application scenarios that benefit from edge computing. Second, we carry out an experimental evaluation of edge computing and its enabling technologies in a selected use case represented by mobile gaming. To this end, we consider a resource-intensive 3D application as a paradigmatic example and evaluate the response delay in different deployment scenarios. Our experimental results show that edge computing is necessary to meet the latency requirements of applications involving virtual and augmented reality. We conclude by discussing what can be achieved with current edge computing platforms and how emerging technologies will impact on the deployment of future IoT applications.

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Section: B Experimental Resultsmentioning

confidence: 82%

Edge Computing for the Internet of Things: A Case Study

Premsankar

Francesco

Taleb

2018

IEEE Internet Things J.

535

238

View full text Add to dashboard Cite

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“…For example, WFA was used by Onlive for game placement [60]. Also, the concept of the BFA, FFA, and NFA employed in other cloud gaming studies [21], [38], [40]. In this part of the experiments, we employ a new frame rate set, i.e., F2 = {30, 45, 60, 90, 120}, which includes some higher and realistic frame rates for today's games.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The results show that compared to Random Assignment and Nearest Assignment, considering wastage is helpful to achieve cost-effectiveness. Authors of [38] formulate the MCG Problem by considering the latency between the client, game server, rendering server, and the datacenter and then present several heuristics to address the server allocation problem for MCG. To solve these NP-Hard problems, they introduce a combination of price and wastage-based assignment algorithms with a greedy hill-climbing approach, called Lowest-Amortized-Cost, to take into account three parameters: server cost, bandwidth cost, and capacity wastage.…”

Section: B Related Workmentioning

confidence: 99%

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

2020

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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.

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“…Extensively studies have been conducted to optimize cloud gaming services, including graphical rendering [9], edge allocation [8], bandwidth allocation [21], server resource management [11], and dynamic streaming [22]. In contrast, few researchers investigated novel cloud gaming pricing strategies, which adopt playing time as their pricing criteria.…”

Section: Introductionmentioning

confidence: 99%

CloudArcade: A Blockchain Empowered Cloud Gaming System

Zhao

Chi

Wang

et al. 2020

Proceedings of the 2nd ACM International Symposium on Blockchain and Secure Critical Infrastructure

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By rendering game scenes on the remote cloud and delivering real-time video to end devices via the Internet, cloud gaming enables players to access game services anytime anywhere despite the hardware capacity of their terminals. However, as a commercial service, the state-of-the-art payment models for cloud gaming are still in their preliminary stages. In this paper, we reveal the shortages of existing cloud gaming pricing models and propose CloudArcade, a token-based cloud gaming system that employs blockchain-empowered cryptocurrency as a payment method for the players using the cloud gaming services. By using cryptocurrency, CloudArcade provides a transparent and resource-aware pricing method, it also enables a time irrelevant silent payment on the floating price to protects users' payment. These features eliminate the quality of experience degradation caused by the spot price in the traditional dynamic pricing model on the QoE-aware service pricing. We also employ the payment channel in CloudArcade to improve the system performance. Discussions on service pricing criteria are put forward, open issues about token issuing and malicious resource speculation are also reviewed. We believe the design of CloudArcade can show a good generality on other QoE-aware and human-centered applications.

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The Server Allocation Problem for Session-Based Multiplayer Cloud Gaming

Cited by 48 publications

References 47 publications

Edge Computing for the Internet of Things: A Case Study

Edge Computing for the Internet of Things: A Case Study

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

CloudArcade: A Blockchain Empowered Cloud Gaming System

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