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Analysis and Optimization of Cache Resource Allocation for Energy-Efficient Information-Centric Networking

节能信息中心网络的缓存资源分配分析与优化

基本信息

批准号：
EP/M013936/1
负责人：
JIA HU
金额：
$ 11.72万
依托单位：
Liverpool Hope University
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2015
资助国家：
英国
起止时间：
2015 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FM013936%2F1
关键词：
Analysis Optimization Cache Resource Allocation

项目摘要

It is predicted that Internet video streaming and downloads will account for more than 76 percent of all consumer Internet traffic in 2018. The tremendous growth of multimedia traffic has given rise to the demand for highly scalable and efficient content retrieval and dissemination in the Internet. However, the Internet was originally designed to enable host-to-host communication and lacks natural support for content distribution. In this context, Information-Centric Networking (ICN) has emerged as a new paradigm for future Internet, where the network interprets, processes, and delivers name-identified content to the users independently of the host location. ICN deploys in-network caching that enables content to be retrieved from multiple locations to achieve low dissemination latency and network traffic reduction. Serving as its fundamental building block, efficient in-network caching is vitally important for ICN. The distinct features of in-network caching such as transparency, ubiquity and fine-granularity have made traditional caching theory, models and optimization approaches inapplicable to ICN caches. Therefore, significant research efforts have been devoted to tackling the very challenging problem of in-network caching. The existing research works have been primarily focused on the simulation studies of ICN caching. However, analytical modelling of ICN cache networks is indispensable for the understanding of the intrinsic behaviors and features of in-network caching. The analytical models reported in the current literature for ICN mainly adopt unrealistic assumptions, such as independent reference model and unknown chunk-level object popularity, and are commonly based on the inefficient Leave Copy Everywhere (LCE) cache decision policy only. Furthermore, due to both increasing energy cost and CO2 emission, energy efficiency of networks and systems becomes a dramatically growing concern. Consequently, energy-efficiency of ICN has also been investigated by some studies, which are mainly based on unrealistic models of topology and content requests. To the best of our knowledge, analytical modelling and optimization of cache resource allocation for energy-efficient information-centric networking with transparent, ubiquitous and fine-granular caches has not been reported in the existing literature.This project will investigate in-network cache resource allocation to achieve energy-efficient and timely content dissemination in the context of Information-Centric Networks. To tackle this challenging problem progressively, our work will be focused on three major tasks: 1) design of an intelligent cache decision policy with low complexity for ICN to reduce cache redundancy, increase the cache diversity and leverage the correlation between content requests; 2) development of novel analytical tools for evaluating the energy efficiency and performance of the proposed cache decision policy in terms of cache hit ratio and request response time with multimedia applications and heterogeneous network conditions; 3) development of a centralized optimization algorithm to investigate the impact of traffic conditions and network environments on the efficiency of cache allocation and a distributed cache allocation scheme that allocates appropriate cache locations of content chunks to minimize the energy consumption. The insights into energy-efficient cache allocation obtained in the aforementioned Tasks 1 and 2 will be feed into the distributed management scheme design in Task 3. The research proposed in the project is believed to among the first of its kind on the analysis and optimization of in-network cache allocation for energy-efficient ICN. The implications of this research will contribute directly to ICN in-network caching in both theoretical and practical sides and pave the way for future green Internet with multimedia applications.

据预测，2018年互联网视频流和下载将占所有消费者互联网流量的76%以上。多媒体流量的巨大增长已经引起了对互联网中高度可扩展和高效的内容检索和分发的需求。然而，互联网最初被设计为支持主机到主机的通信，缺乏对内容分发的自然支持。在这种背景下，以信息为中心的网络（ICN）已经成为未来互联网的一个新的范例，其中网络独立于主机位置解释，处理并向用户提供名称标识的内容。ICN部署了网内缓存，使内容能够从多个位置检索，以实现低传播延迟和减少网络流量。作为其基本构建块，高效的网络内缓存对ICN至关重要。网内缓存的透明性、普适性和细粒度等特点使得传统的缓存理论、模型和优化方法不适用于ICN缓存。因此，大量的研究工作已经致力于解决非常具有挑战性的问题，在网络缓存。现有的研究工作主要集中在ICN缓存的仿真研究上。然而，ICN缓存网络的分析建模是必不可少的内在行为和网络缓存的功能的理解。目前ICN的分析模型主要采用不切实际的假设，如独立的参考模型和未知的块级对象流行度，并且通常仅基于低效的Leave Copy Everywhere（LCE）缓存决策策略。此外，由于增加的能源成本和CO2排放，网络和系统的能源效率成为一个显着增长的关注。因此，ICN的能源效率也被一些研究，这主要是基于不切实际的模型的拓扑结构和内容请求。据我们所知，分析建模和优化的高速缓存资源分配的节能信息为中心的网络与透明的，无处不在的和细粒度的高速缓存还没有在现有的literation.This项目将调查在网络高速缓存资源分配，以实现节能和及时的内容传播的信息为中心的网络的背景下。为了逐步解决这个具有挑战性的问题，我们的工作将集中在三个主要任务上：1）设计一个低复杂度的ICN智能缓存决策策略，以减少缓存冗余，增加该高速缓存的多样性和利用内容请求之间的相关性;（二）开发新的分析工具，用于根据缓存命中率评估拟议缓存决策策略的能效和性能以及多媒体应用和异构网络条件下的请求响应时间; 3）开发集中式优化算法以研究流量条件和网络环境对缓存分配效率的影响，以及分布式缓存分配方案，其分配内容块的适当缓存位置以最小化能量消耗。在前面提到的任务1和2中获得的对节能缓存分配的见解将被馈送到任务3中的分布式管理方案设计中。该项目中提出的研究被认为是同类研究中的第一个分析和优化节能ICN的网络缓存分配的研究。本研究的意义将直接有助于ICN在网络缓存的理论和实践方面，为未来的绿色互联网与多媒体应用铺平道路。