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

Lead Research Organisation: University of Exeter
Department Name: Engineering Computer Science and Maths

Abstract

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.

Planned Impact

Outcomes of this project have the potential to deliver scientific and technological advances that will strengthen the UK's competitiveness in future Internet, energy-efficiency, and many other related areas, thus contribute to the economy and wellbeing of the UK. Internet Service Providers (ISPs), content providers, networking equipment manufacturers, and Internet services and applications need to collaborate to advance ICN and its applications for planning and development of future products and services. Potential beneficiaries of this project include:

Internet Users
The Internet has been transformed from a host-to-host communications network to a global infrastructure for the massive distribution of information. According to recent predictions, global IP traffic has been and continues to be exponentially skyrocketing. Internet users are increasingly interested in receiving information wherever it is located and whenever it is needed, rather than in connecting a particular host or server. By decoupling information from its sources, the novel ICN approach is expected to realize highly scalable and efficient distribution and replication of large amounts of data to provide excellent Quality-of-Experience (QoE) for Internet users.
The proposed research on energy-efficient in-network caching is a vital and necessary component of ICN. The research outcomes can help bring ICN enabled effective and efficient content dissemination closer to practice. Successful deployment of content dissemination applications will offer Internet users best QoE in a cost-effective way, by minimizing the request response time and optimizing network resources. Various content dissemination applications can be tested and implemented in both near and medium time scales, such as medical devices and healthcare management systems, home media sharing, scalable and efficient video delivery, etc. Internet users will also benefit from the ICN on energy-efficient content distribution. Energy-efficiency is a growing concern due to both increasing energy costs and CO2 emissions. As more and more users access the Internet using mobile devices equipped with batteries, it will be desirable to adopt energy-efficient content distribution to prolong the battery life.

Internet industry
The exponential growth of data demand poses a significant challenge because the Internet was designed as a communications network rather than a data distribution network, which impacts every part of the Internet ecosystem and the whole industry needs novel and economical ways to solve these problems. A possible solution to provide scalable and efficient content distribution is to develop information-centric future Internet. In moving to energy-efficient ICN, ISPs, content providers, equipment manufacturers, and Internet services and applications will find themselves at an advantage in terms of networks and energy cost, QoE, and breath of services, which will undoubtedly enhance their competitiveness and profitability.

Academic Beneficiaries
Academic beneficiaries include the researchers carrying out related research. They will benefit from the theoretical and methodological advances to be achieved. Research outcomes will be disseminated for the network research community and other disciplines, to obtain a better understanding and keep track of cutting-edge research in this emerging area. The proposed project endeavors to enhance the knowledge and skill base of the UK through training and developing researchers.

Outcomes of this project may contribute to Internet industry and standardization activities, aid government agencies and other bodies to obtain a better understanding of the potential impact of ICN, thereby appropriate regulatory policies can be investigated. The contributions to the research community, internet industry, and standardization bodies will lead to a subsequent impact on the increase of quality of life and economic activity for the general public.
 
Description A new analytical model was developed to gain valuable insight into the caching performance of Information-Centric Networking (ICN) with arbitrary topology and bursty content requests. The analytical model can be used as a cost-efficient tool to investigate the impact of key network and content parameters on the performance of caching in ICN.

A cost-aware caching mechanism was proposed to study the Quality-of-Service (QoS) and cost of ICN, where a multi-objective evolution algorithm is adopted to find the optimal caching placement. We also propose a novel proactive content caching algorithm based on machine learning without centralized training data to improve the cache hits rate while preserving users' privacy.

A privacy-aware, secure, and energy-aware green routing protocol was designed to defend against internal attacks, achieve stronger privacy protection and reduce energy consumption in Software-Defined Wireless Mesh Networks.

An advantage actor-critic-based reinforcement learning (RL) framework was developed for resource allocation in cloud computing. The proposed method outperforms classic resource allocation algorithms in terms of job latency and achieves a faster convergence speed than the traditional policy gradient method.

A Federated learning-based Proactive Content Caching (FPCC) scheme was proposed, which is based on a hierarchical architecture where the server aggregates user-side updates to construct a global model and selects the most popular files. Experimental results based on real-world datasets verify that the FPCC outperforms other reference algorithms (Random, m-Greedy, and Thompson Sampling) in terms of cache efficiency. In the follow on work, we propose a mobility-aware federated learning scheme for edge caching in vehicular networks, to protect users' privacy, reduce communication costs, and support the high mobility of vehicles.

We propose a new Deep Reinforcement Learning (DRL)-based task offloading framework for multi-access edge computing, which can efficiently learn the offloading policy represented by a specially designed S2S neural network. The proposed DRL solution can automatically discover the common patterns behind various applications so as to infer a near-optimal offloading policy in different scenarios.
Exploitation Route Built upon our research findings, others can further develop more advanced caching models, resource allocation methods and security mechanisms and apply them to tackle important challenges in 5G networks, edge computing, and Cyber-Physical Systems (CPS). The analysis, optimization, and machine learning tools for resource allocation developed in the proposed research could enable the developments of various applications in the future Internet such as scalable video delivery, home media sharing, and etc.
Sectors Digital/Communication/Information Technologies (including Software)

 
Description Learning-Based Networking and Caching Technology for Mobile Crowd Sensing
Amount £73,000 (GBP)
Funding ID 2072878 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 10/2018 
End 07/2022
 
Description Guest Lecture 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Undergraduate students
Results and Impact H. W. (postdoc researcher) gave a guest lecture to the undergraduate cohorts who study mobile and ubiquitous computing. The lecture sparked questions and discussion afterward, and the students showed interest in related subject areas.
Year(s) Of Engagement Activity 2020