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首页> 外文期刊>International journal of information technologies and systems approach >Fog Caching and a Trace-Based Analysis of its Offload Effect
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Fog Caching and a Trace-Based Analysis of its Offload Effect

机译：雾缓存及其卸载效果的基于迹线的分析

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Many years of research on Content Delivery Networks (CDNs) offers a number of effective methods for caching of content replicas or forwarding requests. However, recently CDNs have aggressively started migrating to clouds. Clouds present a new kind of distribution environment as each location can support multiple caching options varying in the level of persistence of stored content. A subclass of clouds located at network edge is referred to as fog clouds. Fog clouds help by allowing CDNs to offload popular content to network edge, closer to end users. However, due to the fact that fog clouds are extremely heterogeneous and vary wildly in network and caching performance, traditional caching technology is no longer applicable. This paper proposes a multi-level caching technology specific to fog clouds. To deal with the heterogeneity problem and, at the same time, avoid centralized control, this paper proposes a function that allows CDN services to discover local caching facilities dynamically, at runtime. Using a combination of synthetic models and real measurement dataset, this paper analyzes efficiency of offload both at the local level of individual fog locations and at the global level of the entire CDN infrastructure. Local analysis shows that the new method can reduce inter-cloud traffic by between 16 and 18 times while retaining less than 30% of total content in a local cache. Global analysis further shows that, based on existing measurement datasets, centralized optimization is preferred to distributed coordination among services.
机译：内容交付网络（CDN）的多年研究为缓存内容副本或转发请求提供了许多有效的方法。但是，最近CDN积极地迁移到云中。云提供了一种新型的分发环境，因为每个位置都可以支持多个缓存选项，这些缓存选项的存储内容的持久性级别有所不同。位于网络边缘的云的子类称为雾云。雾云通过允许CDN将流行的内容卸载到更靠近最终用户的网络边缘来提供帮助。但是，由于雾云非常异构，并且网络和缓存性能变化很大，因此传统的缓存技术不再适用。本文提出了一种针对雾云的多层缓存技术。为了解决异构性问题，同时避免集中控制，本文提出了一种功能，该功能允许CDN服务在运行时动态发现本地缓存设施。结合综合模型和实际测量数据集，本文分析了单个雾位置的本地级别和整个CDN基础架构的全局级别的卸载效率。本地分析表明，该新方法可以将云间流量减少16到18倍，同时在本地缓存中保留少于30％的总内容。全局分析进一步表明，基于现有的度量数据集，集中优化比服务之间的分布式协调更可取。

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