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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/M013936/2
负责人：
JIA HU
金额：
$ 11.24万
依托单位：
UNIVERSITY OF EXETER
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%2F2
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年互联网视频流媒体和下载将占所有消费者互联网流量的76%以上。多媒体业务量的巨大增长引发了对互联网上高度可扩展和高效的内容检索和传播的需求。然而，互联网最初的设计是为了实现主机到主机的通信，并且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新范式，其中网络独立于主机位置向用户解释、处理和交付名称识别的内容。ICN部署了网络内缓存，可以从多个位置检索内容，以实现低传播延迟和网络流量减少。作为其基本构建块，高效的网内缓存对ICN至关重要。网内缓存的透明性、无处不在、细粒度等特点使得传统的缓存理论、模型和优化方法不再适用于ICN缓存。因此，大量的研究工作致力于解决网络内缓存这一非常具有挑战性的问题。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对于理解网内缓存的内在行为和特征是必不可少的。目前文献中报道的ICN分析模型主要采用独立参考模型、未知块级对象流行度等不切实际的假设，且通常仅基于低效的LCE缓存决策策略。此外，由于能源成本和二氧化碳排放量的增加，网络和系统的能效成为一个日益增长的问题。因此，一些研究也对ICN的能量效率进行了研究，这些研究主要是基于不现实的拓扑和内容请求模型。针对基于透明、无处不在、细粒度缓存的高能效信息网络缓存资源分配的分析建模和优化问题，本课题将研究在信息网络环境下的网络缓存资源分配问题，以实现高效、及时的内容传播。为了逐步解决这一具有挑战性的问题，我们的工作将集中在三个主要任务上：1)为ICN设计一个低复杂度的智能缓存决策策略，以减少缓存冗余，增加缓存多样性，并利用内容请求之间的相关性；2)开发新的分析工具，从缓存命中率和请求响应时间两个方面来评估所提出的缓存决策策略的能效和性能；3)开发一个集中式优化算法来研究流量状况和网络环境对缓存分配效率的影响，以及一个分布式缓存分配方案，该方案为内容块分配适当的缓存位置，以最大限度地降低能耗。在上述任务1和2中获得的对节能缓存分配的见解将被反馈到任务3的分布式管理方案设计中。该项目提出的研究被认为是针对节能ICN的网络内缓存分配的分析和优化的首创。本文的研究成果对ICN网内缓存技术的研究具有重要的理论意义和实用价值，为未来绿色互联网的多媒体应用奠定了基础。