Cluster-based cooperative fog caching for scalable coded videos of multiple content providers

IF 6 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Internet of Things Pub Date : 2024-09-25 DOI:10.1016/j.iot.2024.101380
Ferdous Sharifi , Shaahin Hessabi , Young Choon Lee
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Abstract

Efficient content caching in video streaming services is important for improving user experience as well as reducing network bandwidth consumption. Fog caching combined with scalable video coding (SVC) has the potential to significantly improve caching efficiency. However, challenges such as the limited storage capacity of fog nodes and determining the optimal number of SVC layers must be overcome for their effective adoption. This becomes more complicated with multiple content providers requiring shared cache resources. To the best of our knowledge, no existing research has simultaneously tackled all these aspects. In this paper, we present Cluster-based Cooperative Fog Caching (CCo-Fog), a holistic caching strategy that enables multiple content providers to share the scarce storage of fog nodes in a multi-tier fog network to judiciously cache SVC videos in a cooperative manner. In particular, CCo-Fog consists of a cluster-based storage partitioning method and tier-wise cooperative content placement policies. The partitioning method distributes the storage of each fog node to multiple content providers for users clustered based on their population density and their proximity to fog nodes. The content placement policies determine the optimal number of SVC layers of each video for different tiers of the fog network by solving a latency-aware content placement optimization problem. Our evaluations on a real-world dataset and various configurations demonstrate the efficacy of CCo-Fog, showing a reduction in latency by about 60% and an increase in fog hit ratio by about 20% on average, compared to state-of-the-art caching strategies.
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基于集群的合作雾缓存,用于多个内容提供商的可扩展编码视频
视频流服务中的高效内容缓存对于改善用户体验和减少网络带宽消耗非常重要。雾缓存与可扩展视频编码(SVC)相结合,有望显著提高缓存效率。然而,要有效地采用这种方法,必须克服一些挑战,如雾节点的存储容量有限,以及确定最佳的 SVC 层数。如果多个内容提供商需要共享缓存资源,情况就会变得更加复杂。据我们所知,目前还没有研究能同时解决所有这些问题。在本文中,我们提出了基于集群的合作式雾缓存(CCo-Fog),这是一种整体缓存策略,能让多个内容提供商共享多层雾网络中雾节点的稀缺存储,以合作的方式明智地缓存 SVC 视频。具体而言,CCo-Fog 由基于集群的存储分区方法和分层合作内容放置策略组成。分区方法将每个雾节点的存储空间分配给多个内容提供商,这些内容提供商根据用户的人口密度和与雾节点的距离为用户提供服务。内容放置策略通过解决延迟感知内容放置优化问题,为不同层级的雾网络确定每个视频的最佳 SVC 层数。我们在真实数据集和各种配置上进行的评估证明了 CCo-Fog 的功效,与最先进的缓存策略相比,延迟降低了约 60%,雾命中率平均提高了约 20%。
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来源期刊
Internet of Things
Internet of Things Multiple-
CiteScore
3.60
自引率
5.10%
发文量
115
审稿时长
37 days
期刊介绍: Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT. The journal will place a high priority on timely publication, and provide a home for high quality. Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.
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