GPU/QoE-Aware Server Selection Using Metaheuristic Algorithms in Multiplayer Cloud Gaming

Dinaki, Hossein Ebrahimi; Shirmohammadi, Shervin

doi:10.1109/netgames.2018.8463364

Cited by 8 publications

(18 citation statements)

References 14 publications

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“…In our previous work, we proposed a method that optimally assigns cloud servers to the game sessions requested by game players [29]. The method assesses the requested game and its required video quality in terms of frame rate and the load of the frames while considering the capacity of the eligible datacenters to allocate an appropriate cloud server to the player for the requested game session.…”

Section: Introductionmentioning

confidence: 99%

“…Our objective was the maximization of GPU utilization, which leads to the service provider's economic benefit, while increasing the player's QoE. To determine the priority of the two objectives, an end-to-end lag model weights them adaptively [29]. Then we proposed two efficient methods based on two metaheuristic algorithms, namely: Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).…”

Section: Introductionmentioning

confidence: 99%

“…However, the utilization percentage for small number of players in [29] is not sufficient enough and needs to be improved. For example, the results show almost 20 and 40 percent capacity wastage, for 20 players' case in PSO and GA-based methods respectively, however for 100 players' case, these wastages are half of these percentages.…”

Section: Introductionmentioning

confidence: 99%

“…To measure the improvements of the proposed algorithms, we have compared them with the conventional GA and PSO algorithms presented in [29], as well as four well-known and widely-used bin-packing algorithms called Best Fit Algorithm (BFA), First Fit Algorithm (FFA), Next Fit Algorithm (NFA) and Worst Fit Algorithm (WFA). The results indicate that our boosted algorithms achieve a remarkable improvement in terms of utilization and capacity wastage, and that the Boosted-PSO is the best one among them.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2020

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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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Minimizing Play Request Rejection through Workload Splitting in Edge-Cloud Gaming

Jaya

Cai

2021

2021 IEEE 27th International Conference on Parallel and Distributed Systems (ICPADS)

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GPU/QoE-Aware Server Selection Using Metaheuristic Algorithms in Multiplayer Cloud Gaming

Cited by 8 publications

References 14 publications

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

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

CloudArcade: A Blockchain Empowered Cloud Gaming System

Minimizing Play Request Rejection through Workload Splitting in Edge-Cloud Gaming

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