Dynamic in-network caching for energy efficient content delivery
Dynamic in-network caching for energy efficient content delivery
复制标题
DOI：
10.1109/infcom.2013.6566772
复制
发表时间：
2013-04
期刊：
影响因子：
--
通讯作者：
Jaime Llorca;A. Tulino;K. Guan;Jairo O. Esteban;Matteo Varvello;Nakjung Choi;D. Kilper
中科院分区：
文献类型：
--
作者：
Jaime Llorca;A. Tulino;K. Guan;Jairo O. Esteban;Matteo Varvello;Nakjung Choi;D. Kilper
考虑一个媒体内容的生产者网络，用户在其中动态创建和请求内容对象。请求过程受对象的受欢迎程度的控制，并且随着时间的流逝而变化。为了满足用户请求，可以通过网络存储和运输内容对象，其特征是存储和运输资源的容量和能源效率。节能动态网络内部缓存问题旨在在任何给定时间符合用户请求，满足网络资源能力并最大程度地使整体上的内容对象在每个网络元素上缓存和运输的内容对象，以找到网络配置的演变。能源使用。我们提供1）基于整数线性程序（ILP）的离线解决方案（ILP），为能源有效的动态网络内缓存问题提供一个以信息为中心的优化框架，该框架获得了可获得可以实现的最大效率提高的。凭借对用户请求和网络资源的全球知识，以及3）有效的完全分布的在线解决方案EEOND，该解决方案允许网络节点根据其当前的估计来做出本地缓存决策全球能源利益。我们的解决方案在能力，能源效率和内容流行方面考虑了网络异质性，并适应了不断变化的网络条件，最大程度地减少了整体能源使用。
Consider a network of prosumers of media content in which users dynamically create and request content objects. The request process is governed by the objects' popularity and varies across network regions and over time. In order to meet user requests, content objects can be stored and transported over the network, characterized by the capacity and energy efficiency of the storage and transport resources. The energy efficient dynamic in-network caching problem aims at finding the evolution of the network configuration, in terms of the content objects being cached and transported over each network element at any given time, that meets user requests, satisfies network resource capacities and minimizes overall energy use. We provide 1) an information-centric optimization framework for the energy efficient dynamic in-network caching problem, 2) an offline solution, EE-OFD, based on an integer linear program (ILP) that obtains the maximum efficiency gains that can be achieved with global knowledge of user requests and network resources, and 3) an efficient fully distributed online solution, EEOND, that allows network nodes to make local caching decisions based on their current estimate of the global energy benefit. Our solutions take into account the network heterogeneity, in terms of capacity, energy efficiency and content popularity, and adapt to changing network conditions minimizing overall energy use.