Server Allocation for Multiplayer Cloud Gaming

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

doi:10.1145/2964284.2964301

Cited by 20 publications

(12 citation statements)

References 37 publications

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“…The work in [21] focuses on server acquisition between available datacenters based on the minimum price to support MCG. They consider four methods, three of them based on price: 1) Lowest Server Price Assignment, 2) Lowest Bandwidth Price Assignment, 3) Lowest Combined Price Assignment, and one based on wastage: Lowest Capacity Wastage Assignment.…”

Section: B Related Workmentioning

confidence: 99%

“…GPUs play the leading role in speeding up the rendering of the game's video and reducing the processing delay of the CG system and consequently have a considerable effect on end user's experience. Besides, they are the most expensive hardware rented by providers [21]. Furthermore, not all of the servers in datacenters are equipped with a GPU.…”

Section: Utilization Delay and User's Experience Models A Gpumentioning

confidence: 99%

“…Furthermore, CG has higher delay sensitivity compared to the other platforms, such as online games [16]. Since each game, depending on its genre and pace, has different resource requirements, assigning the cloud servers to game players in this distributed network is one of the remaining challenges of CG platforms [21].…”

Section: Introductionmentioning

confidence: 99%

“…GPUs play the leading role for fast video rendering in CG as a real-time service, therefore using GPU-equipped cloud servers in this platform is an essential requirement. Also, GPU is the most expensive infrastructure that CG providers should pay for [21]. Therefore, its maximum utilization is vitally important from their profitability perspective.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, its maximum utilization is vitally important from their profitability perspective. However, utilization should be efficient to satisfy more players as well, as they are the primary source of revenue for the CG provider [21]. Consequently, GPU is a critical parameter for server selection in the CG platform.…”

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: B Related Workmentioning

confidence: 99%

Section: Utilization Delay and User's Experience Models A Gpumentioning

confidence: 99%