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首页> 外文期刊>IEEE Journal on Selected Areas in Communications >Online Resource Allocation, Content Placement and Request Routing for Cost-Efficient Edge Caching in Cloud Radio Access Networks
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Online Resource Allocation, Content Placement and Request Routing for Cost-Efficient Edge Caching in Cloud Radio Access Networks

机译：在线资源分配，内容放置和请求路由，用于云无线电接入网络中具有成本效益的边缘缓存

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摘要

In this paper, we advocate edge caching in cloud radio access networks (C-RAN) to facilitate the ever-increasing mobile multimedia services. In our framework, central offices will cooperatively allocate cloud resources to cache popular contents and satisfy user requests for those contents, so as to minimize the system costs in terms of storage, VM reconfiguration, content access latency, and content migration. However, this joint resource allocation, content placement and request routing, is nontrivial, since it needs to be continuously adjusted to accommodate system dynamics, such as user movement and content slashdot effect, while taking into account the time-correlated adjustment costs for VM reconfiguration and content migration. To this end, we build a comprehensive model to capture the key components of edge caching in C-RAN and formulate a joint optimization problem, aiming at minimizing the system costs over time and meanwhile satisfying the time-varying user requests and respecting various practical constraints (e.g., storage and bandwidth). Then, we propose a novel online approximation algorithm by resorting to the regularization, rounding, and decomposition technique, which can be proved to have a parameterized competitive ratio with a polynomial running time. Extensive trace-driven simulations corroborate the efficiency, flexibility, and lightweight of our proposed online algorithm; for instance, it achieves an empirical competitive ratio around 2 - 4 and gains over 30% improvement compared with many state-of-the-art algorithms in various system settings.
机译：在本文中，我们提倡在云无线电接入网络（C-RAN）中进行边缘缓存，以促进不断增长的移动多媒体服务。在我们的框架中，中心办公室将合作分配云资源以缓存受欢迎的内容，并满足用户对这些内容的请求，从而在存储，VM重新配置，内容访问延迟和内容迁移方面将系统成本降至最低。但是，这种联合资源分配，内容放置和请求路由很重要，因为需要不断调整以适应系统动态，例如用户移动和内容斜杠效果，同时还要考虑与时间相关的VM重新配置调整成本和内容迁移。为此，我们建立了一个全面的模型来捕获C-RAN中边缘缓存的关键组成部分，并提出了一个联合优化问题，旨在最大程度地降低系统成本，同时满足时变的用户需求并尊重各种实际约束（例如，存储和带宽）。然后，借助正则化，舍入和分解技术，提出了一种新颖的在线逼近算法，可以证明该算法具有带多项式运行时间的参数化竞争比。广泛的跟踪驱动仿真证实了我们提出的在线算法的效率，灵活性和轻量级；例如，与各种系统设置中的许多最新算法相比，它的经验竞争率约为2-4，并且提高了30％以上。

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