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Towards simulation and optimization of cache placement on large virtual content distribution networks

机译:面向大型虚拟内容分发网络上的缓存放置的仿真和优化

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IP video traffic is forecast to be 82% of all IP traffic by 2022. Traditionally, content distribution networks (CDN) were used extensively to meet quality of service levels for IP video services. To handle the dramatic growth in video traffic, CDN operators are migrating their infrastructure to the cloud and fog in order to leverage its greater availability and flexibility. For hyperscale deployments, energy consumption, cache placement, and resource availability can be analyzed using simulation in order to improve resource utilization and performance. Recently, a discrete-time simulator for modelling hierarchical virtual CDNs (vCDNs) was proposed with reduced memory requirements and increased performance using multi-core systems to cater for the scale and complexity of these networks. The first iteration of this discrete-time simulator featured a number of limitations impacting accuracy and applicability: it supports only tree based topology structures, the results are computed per level, and requests of the same content differ only in time duration. In this paper, we present an improved simulation framework that (a) supports graph based network topologies, (b) requests have been reconstituted for differentiation of requirements, and (c) statistics are now computed per site and network metrics per link, improving granularity and parallel performance. Moreover, we also propose a two phase optimization scheme that makes use of simulation outputs to guide the search for optimal cache placements. In order to evaluate our proposal, we simulate a vCDN network based on real traces obtained from the BT vCDN infrastructure, and analyze performance and scalability aspects. (C) 2019 Elsevier B.V. All rights reserved.
机译:到2022年,IP视频流量预计将占所有IP流量的82%。传统上,内容分发网络(CDN)被广泛用于满足IP视频服务的服务质量水平。为了应对视频流量的急剧增长,CDN运营商正在将其基础架构迁移到云和雾中,以利用其更大的可用性和灵活性。对于超大规模部署,可以使用仿真来分析能耗,缓存放置和资源可用性,以提高资源利用率和性能。最近,提出了一种用于建模分层虚拟CDN(vCDN)的离散时间模拟器,该方法使用多核系统来降低内存需求并提高性能,以适应这些网络的规模和复杂性。此离散时间模拟器的第一次迭代具有许多限制,影响准确性和适用性:它仅支持基于树的拓扑结构,结果是按级别计算的,并且相同内容的请求仅在持续时间内有所不同。在本文中,我们提出了一种改进的仿真框架,该框架(a)支持基于图的网络拓扑,(b)重构了请求以区分需求,并且(c)现在对每个站点和每个链路的网络指标计算统计信息,从而提高了粒度和并行性能。此外,我们还提出了一个两阶段优化方案,该方案利用模拟输出来指导搜索最佳缓存位置。为了评估我们的建议,我们基于从BT vCDN基础架构获得的真实跟踪来模拟vCDN网络,并分析性能和可伸缩性方面。 (C)2019 Elsevier B.V.保留所有权利。

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