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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。随着多媒体通信量的迅猛增长，人们对互联网中具有高度可扩展性和高效性的内容检索和传播提出了更高的要求。然而，Internet最初的设计是为了实现主机对主机的通信，因此缺乏对内容分发的自然支持。在这种情况下，以信息为中心的网络（Information-Centric Networking， ICN）已经成为未来互联网的一种新范式，在这种范式中，网络独立于主机位置解释、处理和向用户交付名称标识的内容。ICN部署了网络内缓存，使内容能够从多个位置检索，从而实现低传播延迟和减少网络流量。作为ICN的基本组成部分，高效的网内缓存对ICN至关重要。网络内缓存的透明性、泛在性和细粒度性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，大量的研究工作一直致力于解决网络内缓存这个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络内缓存的内在行为和特征是必不可少的。目前文献报道的ICN分析模型主要采用了不现实的假设，如独立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，由于能源成本和二氧化碳排放的增加，网络和系统的能源效率日益受到关注。因此，一些研究也对ICN的能源效率进行了调查，这些研究主要基于不切实际的拓扑模型和内容请求。据我们所知，现有文献中尚未报道透明、无处不在和细粒度缓存的高能效信息中心网络的缓存资源分配的分析建模和优化。该项目将研究网络内缓存资源分配，以在信息中心网络的背景下实现节能和及时的内容传播。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，以评估在多媒体应用和异构网络条件下缓存命中率和请求响应时间方面所提出的缓存决策策略的能源效率和性能；3)开发集中优化算法，研究交通状况和网络环境对缓存分配效率的影响，并开发分布式缓存分配方案，为内容块分配合适的缓存位置，以最大限度地降低能耗。在上述任务1和2中获得的关于高效缓存分配的见解将提供给任务3中的分布式管理方案设计。该项目提出的研究被认为是同类研究中首次对节能ICN的网内缓存分配进行分析和优化。本研究结果将对ICN网内缓存的理论和实践有直接的贡献，并为未来绿色多媒体互联网的应用铺平道路。