Improving Cloud Gaming Experience through Mobile Edge Computing

Zhang, Xu; Chen, Hao; Zhao, Yangchao; Ma, Zhan; Xu, Yiling; Huang, Haojun; Yin, Hao; Wu, Dapeng

doi:10.1109/mwc.2019.1800440

Cited by 108 publications

(46 citation statements)

References 13 publications

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“…Because each simulation is performed for the same amount of usage, the case in which the resource utilization rate is higher than that of other algorithms can be seen in the following example. Suppose that node A has (10,10,20) available resources, and node B has (20,20,20) available resources. When the VSF with resource requirements of (10, 10, 10) is deployed, the resource utilization rate is (1 + 1 + FIGURE 5.…”

Section: B Simulation Results and Discussionmentioning

confidence: 99%

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Joint Resource Allocation Based on Traffic Flow Virtualization for Edge Computing

2021

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Edge computing can provide network services with low latency and real-time processing by operating cloud services on network edges. Edge computing has numerous advantages such as low latency, local characteristics, and network congestion localization, however, associated resource management is becoming a significant challenge because of its features such as hierarchy, decentralization, and heterogeneity. Thus, a joint resource operation and management scheme for edge computing that can greatly reduce the traffic load in the network by considering the generated traffic load and computing resources is proposed in this paper. It is based on the concept of a virtual service flow (VSF) consisting of clients, data sources, a server instance, and external network entities for a specific service. The VSF consists of several drafts according to the expected server location, and each draft estimates a traffic load that can occur according to its characteristics. The location of the edge server of the VSF is determined for each VSF, and it is performed based on the VSF rejection and reconfiguration algorithm using the weighted vector bin packing algorithm that considers the client node coverage of the VSF. The proposed scheme is evaluated based on simulations that consider the actual characteristics of the network services and devices.

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Section: B Simulation Results and Discussionmentioning

confidence: 99%

“…In the existing environment based on cloud computing, even insensitive users may feel input delay, which soon results in reduced usability. Therefore, edge computing can be the core technology of cloud gaming [10].…”

Section: Introductionmentioning

confidence: 99%

Joint Resource Allocation Based on Traffic Flow Virtualization for Edge Computing

2021

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“…A QoE-aware resource allocation framework is presented in [39] for a mobile cloud gaming system. The proposed system, referred to as EdgeGame, offloads computationally intensive tasks into the edge instead of the remote rendering server.…”

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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“…To overcome the limitations of the client hardware, a cloud game collects user inputs from the client, processes the collected information, and renders the graphics accordingly in the cloud, and then streams only the rendered frames to the client. In the cloud computing model, cloud games were unfortunately difficult to realize because of the round-trip time, but in the edge computing environment, the problem of latency is solved, making it a potentially feasible service [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Progressive Traffic-Oriented Resource Management for Reducing Network Congestion in Edge Computing

Kim

2021

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Edge computing can deliver network services with low latency and real-time processing by providing cloud services at the network edge. Edge computing has a number of advantages such as low latency, locality, and network traffic distribution, but the associated resource management has become a significant challenge because of its inherent hierarchical, distributed, and heterogeneous nature. Various cloud-based network services such as crowd sensing, hierarchical deep learning systems, and cloud gaming each have their own traffic patterns and computing requirements. To provide a satisfactory user experience for these services, resource management that comprehensively considers service diversity, client usage patterns, and network performance indicators is required. In this study, an algorithm that simultaneously considers computing resources and network traffic load when deploying servers that provide edge services is proposed. The proposed algorithm generates candidate deployments based on factors that affect traffic load, such as the number of servers, server location, and client mapping according to service characteristics and usage. A final deployment plan is then established using a partial vector bin packing scheme that considers both the generated traffic and computing resources in the network. The proposed algorithm is evaluated using several simulations that consider actual network service and device characteristics.

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Improving Cloud Gaming Experience through Mobile Edge Computing

Cited by 108 publications

References 13 publications

Joint Resource Allocation Based on Traffic Flow Virtualization for Edge Computing

Joint Resource Allocation Based on Traffic Flow Virtualization for Edge Computing

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

Progressive Traffic-Oriented Resource Management for Reducing Network Congestion in Edge Computing

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