基于雾的内容分发网络的低成本高效率内容分发优化模型

Prateek Yadav, Subrat Kar
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摘要

海量数据需求要求内容分发网络(CDN)采用不断发展的技术,在保证服务质量(QoS)的前提下高效分发内容。为满足这一需求,研究人员建议采用基于分布式雾的 CDN 模型,并优化雾节点位置。虽然许多研究都侧重于通过优化雾节点位置来提高 QoS,但很少考虑到受位置、使用变化和传输速率影响的内容分发。因此,CDN 服务的雾节点布局实用方法必须考虑其对内容分发的影响。此外,目前对基于雾的 CDN 的研究缺乏应对关键挑战的正式方法:R1) 处理终端用户请求的雾节点战略布局;R2) 构建有 QoS 保证的内容分发路径;R3) 构建基于雾的 CDN 模型的成本最小化。为了实现 R1-R3 的研究目标,我们将其构建为一个联合优化问题,考虑了四个参数:地理区域、开放式公共 Wi-Fi 接入点(OPWAP)位置、QoS 和成本。作为解决方案,我们提出了一个双步骤框架。首先,我们提出了基于地理区域和 OPWAP 位置的最佳雾节点位置启发式。其次,我们提出了两种选择雾节点的算法,即基于性能的贪婪节点选择算法(GPDS)和贪婪雾节点选择算法(GFNSA),在实现最佳内容分发路径的同时,最大限度地降低构建基于雾的 CDN 的成本。结果表明,所提出的方法优于基线技术,并为问题提供了接近最优的解决方案。
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A cost-efficient content distribution optimization model for fog-based content delivery networks
The massive data demand requires content distribution networks (CDNs) to use evolving techniques for efficient content distribution with guaranteed quality of service (QoS). The distributed fog-based CDN model, with optimal fog node placements, is a suggested aproach by researchers to meet this demand. While many studies have focused on improving QoS by optimizing fog node placement, they have rarely considered the impact on content distribution, affected by placement, usage changes, and delivery rates. Therefore, the practical approach to fog node placement for CDN services must examine its impact on content distribution. Further, current research on fog-based CDN lacks formal methods to address key challenges: R1) strategic placement of fog nodes to process end-user requests; R2) construction of a content distribution path with guaranteed QoS; R3) cost minimization of building a fog-based CDN model. We construct this as a joint optimization problem by considering four parameters: geographical regions, open public Wi-Fi access points (OPWAPs) locations, QoS, and cost to achieve research objectives R1–R3. As a solution, we propose a dual-step framework. First, a heuristic for optimal fog node placement based on geographic regions and OPWAP locations is proposed. Second, we propose two algorithms, Greedy Performance-based Node Selection (GPDS) and Greedy Fog Node Selection algorithm (GFNSA), for selecting fog nodes, minimizing the cost of building a fog-based CDN while achieving optimal content distribution paths. The results demonstrate that the proposed methods outperform the baseline techniques and provide near-optimal solutions to the problem.
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