Research on smart caching and content sharing for mobile networks

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学校代码

学号

I201222051

密级

10487

博士学位论文
面向移动互联网的智能缓存和
资源共享关键技术研究

学位申请人：翁母勇
学 科 专 业：计算机系统结构
指 导 教 师：陈敏

教授

答 辩 日 期：2015 年 5 月 12 日


A Dissertation Submitted in Partial Fulfillment of the Requirements
for the Degree of Doctor of Philosophy in Engineering

Research on Smart Caching and Content Sharing for
Mobile Networks

Ph.D. Candidate : Ong Mau Dung

Major

: Computer System Architecture

Supervisor

: Prof. Chen Min

Huazhong University of Science and Technology
Wuhan 430074, P. R. China
May, 2015


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摘

要

目前的互联网架构设计和建立于上世纪 60 年代末，70 年代初，其主要目的是为
了实现资源共享。它被设计为一个能够使两个终端主机之间进行会话的通讯架构。
因此，如今的互联网已经不仅仅作为一个传统的包传输网络，而且作为一个分发网
络来使用。由于上述原因，需要调整网络架构以便有效地将内容分发给众多用户。
内容中心网络（CCN）提供了满足一种支持上述要求的网络体系结构。CCN 是
一种面向内容名字的方法，它被用来向边缘网关/路由器传播内容，CCN 是一种在不
断发展的分布网络构架。在保证最小的上行带宽需求和最短的下行延迟的前提下，
为了使内容共享的概率达到最大化，CCN 路由器/网关应该尽可能长地缓存交换的内
容。为此，首先提出了两种类型的为 CCN 所设计的智能缓存策略。所提出的新缓存
策略具有较高命中率，能够显著的提高 CCN 的性能。然后介绍了在无线体域网
（WBAN），车辆自组织网络（VANET），数据中心网络（DCN）和多源移动流（MS2）
中的 CCN 的应用和网络架构解决方案，并阐明了 CCN 的有效性和灵活性。
在 WBAN 中，为了在医疗保健系统中提供一个高质量的远程数据传输，提出了
一种新型的混合系统和 WBAN 网络传输结构，保证了系统的服务质量（QoS）。在

医疗保健系统中，长期演进（LTE)可以被作为一个最佳选择，对于动态移动的病人
和医生，CCN 和自适应流是一个合适的选择。
在 VANET 中，对于有效和可靠信息传播和检索需求越来越多，因此，提出一
种解决方案，Vehicular Named Data Networking （VENDNET），将 CCN 的基本原
理转化到 VANET。结果表明，CCN 机制可以显著改善 VANET 的性能。
在云计算和移动社交计算的时代，移动站（MS）的移动特性可能导致 MS 和数
据中心（DC）之间的距离太远而导致包延迟的增加，因此需要将服务随时迁移到不
同的 DC。在当前的网络体系架构中，由于虚拟机的迁移导致的 MS 或者虚拟机 IP
地址的变化会导致两个人之间的 IP 会话释放，服务被中断。基于 CCN 的单一的内
容命名标识，替代 IP 地址，保证了服务的连续性。
最后，本文综合 CCN 提出了移动多媒体流的 MS2 框架用于移动多媒体流。该合
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成体系进一步改善了 MS2 的性能，因为它将附近的无线接入网络的可用流行内容缓
存提供给移动用户，有助于避免从远程的服务器访问流行内容而导致的拥塞。

关键词：内容中心网络

智能缓存

缓存命中率

无缝服务迁移

II

网络流量卸载


 

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Abstract
The current Internet’s architecture was designed and created in the late 1960s and
early 1970s primarily to enable resource sharing. It was designed as a communication
architecture to enable a conversation between two end-hosts. Thus, the usage of Internet
nowadays is more than a network for traditional packet deliveries, we are now using it as a
distribution network. For this reason, the network architecture needs to be operated in
order to effectively support content distribution to the large number of users.
Content Centric Networking (CCN) provides a network architecture to support the
above requirements. CCN is an evolving distribution network architecture since CCN is a
content name-oriented approach to disseminate content to edge gateways/routers. To
maximize the probability of content sharing while ensuring minimal upstream bandwidth
demand and lowest downstream latency, CCN routers/gateways should cache exchanged
content as long as possible. For this reason, novel smart caching policies are firstly
proposed and especially designed for CCN. The obtained simulation results demonstrated
the good performance of the proposed policies in achieving higher hitting rates. Then,
many CCN applications and network architecture solutions are presented in Wireless
Body Area Network (WBAN), Vehicle Ad-hoc Network (VANET), Data Center Network
(DCN) and Multi-Source Mobile Streaming (MS2) to illustrate for the efficient and
flexible CCN implementation.
In WBAN, a new type of hybrid system and WBAN transmission network
architecture are proposed to provide high Quality of Service (QoS) for remote monitoring
in the context of the healthcare system. Long-Term Evolution (LTE) has engaged the best
choice for healthcare system and CCN with adaptive streaming is a suitable solution for

dynamic patients and physicians.
In VANET, motivated by the increasing demand for efficient and reliable
information dissemination and retrieval, Vehicular Named Data Networking (VENDNET)
solution is proposed by inheriting the basic principle of the CCN into VANET. The
obtained results show that CCN mechanism can improve the performance of the VANET
significantly.
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In the era of cloud and mobile social computing, the mobility characteristic may
cause a service on mobile station (MS) to be migrated between different Data Centers
(DC); otherwise, a packet delay is increased due to the fact that a considerable
geographical distance between MS and serving DC. With a current networking
architecture, IP address of either MS or Virtual Machine (VM) is changed because of the
VM migration, and an IP session between two peers is released and the service is
disrupted as a result. Based on unique content name identification in CCN, instead of IP
address, service migration can be continuous.
Finally, CCN is integrated in recently proposed MS2 architecture for mobile
multimedia streaming. The resultant architecture improves further the performance of MS2
as it makes popular content available to mobile users at caches placed nearby Radio
Access Networks. This helps in avoiding the access to popular content from far away
servers along paths that could otherwise get congested.
Key words: Content centric networking
Network traffic offloading

Smart caching

Cache hit rate

Seamless service migration

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Table of Contents
摘

要............................................................................................................... I

Abstract ..........................................................................................................III
1

Introduction

1.1 Overview and motivation...................................................................... (1)
1.2

Content Centric Networking (CCN): the Next Generation Network
(NGN) for the Future Internet (FI) ...................................................... (3)

1.3

Research on smart caching, content sharing, network architecture and

smart services provisioning in mobile network ................................... (9)

1.4 Summary ............................................................................................. (12)
2
2.1

CCN performance improvement with smart caching
Related works and their drawback ...................................................... (14)

2.2 Popularity Prediction and Cooperative Caching (PPCC) ................... (15)
2.3 PPCC algorithm simulation and results .............................................. (21)
2.4 Fine-Grained Popularity-based Caching (FGPC) ............................... (26)
2.5 FGPC algorithm simulation and results.............................................. (30)
2.6 Summary ............................................................................................. (35)
3
3.1

Efficient content sharing over CCN network architecture
Current challenges in content sharing for Wireless Body
Area Network (WBAN) ..................................................................... (37)

3.2

The virtue of sharing: efficient content delivery in WBAN for
ubiquitous healthcare ......................................................................... (43)

3.3 Performance evaluation for a new hybrid system in WBAN ............. (46)
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3.4 Vehicular Ad Hoc Network (VANET) overview, wireless access
Standards and existing issues............................................................. (52)
3.5 Vehicular Named Data Network (VENDNET): efficient content
distribution in VANET ...................................................................... (62)
3.6 VENDNET performance evaluation................................................... (63)
3.7 Summary ............................................................................................. (66)
4

Seamless service migration in mobile network

4.1 Virtualization technology and Data Center (DC) overview ............... (68)
4.2 Motivation and problem statement ..................................................... (69)
4.3 Seamless service migration framework .............................................. (71)
4.4 Simulation and results ......................................................................... (73)
4.5 Summary ............................................................................................. (76)
5
5.1

Supporting rich media services via Named Data Networking
Related works and problem statement ................................................ (78)

5.2 MS2 : Traditional solution to supporting rich media services
in mobile networks............................................................................. (80)
5.3 MM3C: Multi-Source Mobile Streaming in Cache-enabled CCN ..... (85)
5.4 MM3C performance evaluation .......................................................... (88)
5.5 Summary ............................................................................................. (89)
6

6.1

Mobile Data Offloading: A Named Data Networking Approach
Challenges in mobile traffic................................................................ (90)

6.2 Integrating NDN with LTE ................................................................. (92)
6.3 Network architecture........................................................................... (93)
6.4

Results Analysis .................................................................................. (95)

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6.5 Summary ............................................................................................. (97)
7

Conclusions and future works

7.1

Conclusions ......................................................................................... (98)

7.2

Recommendation for future works ..................................................... (99)

Acknowledgements .................................................................................. (101)
References ................................................................................................. (102)
Appendix 1

Academic papers published during the period
of PhD degree directory ................................................... (111)

Appendix 2

OPNET Modeler simulation tool .................................... (113)

 
 

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1

Introduction

This chapter introduces history of Information Centric Networking (ICN), focus on
the Content Centric Networking (CCN) and its technology. From the motivation behind
the CCN architecture project, we introduce our research on smart caching, content sharing,
network architecture, and smart services provisioning in mobile networks.

1.1

Overview and motivation
The network technology for today’s Internet was created in the 1960s. And it still

speaks only of connection between hosts, e.g. Clients-Server, and Peer-to-Peer (P2P)
connection. Therefore, the existing network architecture meets challenges. That is to say,
host-to-host connection strongly ineffective distributes content to a large number of users
such as YouTube in US, Youku in China or Daum in Korea, etc. Internet users and mobile
subscribers feel unsatisfied with the performance in terms of delay, jitter and throughput,
etc[1]. The existing P2P network architecture meets challenges, the increasing demand for
mass distribution and replication of large amounts of resources has led to develop existing
P2P networking. The other challenge in P2P networking is how to optimal peer selection.
If suboptimal P2P peer is selected that leads to expensive inter-provider traffic[2-3].
Content Delivery Network (CDN) overlay tries to solve a fundamental challenge for
the Internet. That is to say, how to distribute and retrieve content effectively, meanwhile
reduce the delay time of terminal host. CDNs such as Akamai and Limelight mitigated this
problem by placing caches at strategic locations in the network. They are essentially a
large overlay infrastructure comprised of a large number of caches, and serve contracted
data. The basic approach to address the performance problem is to move the content from
the places of origin servers to the places at the edge of the Internet. Serving content from a
local replica server typically has better performance (lower access latency, higher transfer
rate) than from the origin server, and using multiple replica servers for servicing requests
costs less than using only the data communications network does. CDN takes precisely
this approach. On the technical level, CDN provides content caching and services
distributed on the Internet. On the physical level, CDN includes some center servers and
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the number of edge servers placed in wide geographic locations. Using CDN, the request
content come from users will be redirected to the nearest edge server to solve the
backbone network bottleneck and provide better quality of service [4].
However, with the exponential growth of Internet traffic, especially video traffic, the
CDN requires very high cost for large storage in the edge servers

[5-6]

. Moreover,

deployment considerations force them to place these content caches at the peering point
edges. Carriers are averse to having third parties such as Akamai and Limelight install
their content caches in the carriers’ Point of Presence (PoPs). Thus, these content caches
are not integrated into the network - rather they connect to the network like an external
application. Another disadvantage of CDNs are that these services are specific to
contracted applications that are specifically modified to use them.
The proliferation of video in the past few years and its demanding Constant Bit Rate
(CBR) communication patterns have further limited the benefits of using CDNs. Content
caches at peering edges do not reduce traffic on the network backbones; they only reduce
peering traffic. Traffic demands on the network backbone are growing due to growing
video traffic and carriers cannot easily upgrade their backbones to handle this traffic surge.
Beside CDN, cloud computing provides elastic infrastructure and pay-as-you-go. These
characteristics make cloud computing become a suitable solution for the drawback of
CDN about large storage.
Nowadays, Internet subscribers care about the data content they wish to get much
more than where the content comes from. Information-Centric Networking (ICN) has
emerged as a promising candidate for the architecture of the future Internet

[7-8]

. Inspired

by the fact that the Internet is increasingly used for information dissemination, rather than
for pair-wise communication between end hosts, ICN aims to reflect current and future
needs better than the existing Internet architecture. By naming information at the network
layer, ICN favors the deployment of in-network caching and multicast mechanisms, thus
facilitating the efficient and timely delivery of information to the users. However, there is
more to ICN than information distribution, with related research initiatives employing
information-awareness as the means for addressing a series of additional limitations in the
current Internet architecture, for example, mobility management and security enforcement,
so as to fulfill the entire spectrum of future Internet requirements and objectives.
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The ICN architectures leverage in-network storage for caching, multiparty
communication through replication, and interaction models that decouple senders and
receivers. The common goal is to achieve efficient and reliable distribution of content by
providing a general platform for communication services that are today only available in
dedicated systems such as P2P overlays and proprietary content distribution networks. The
following more recent projects representing four approaches being actively developed:
Data-Oriented Network Architecture (DONA) [9], Content-Centric Networking (CCN) [10],
currently in the Named Data Networking (NDN) project
Routing Paradigm (PSIRP)
(PURSUIT) project

[11]

, Publish-Subscribe Internet

[12]

, now in the Publish-Subscribe Internet Technology

[13]

, Network of Information (NetInf)

Future Internet (4WARD) project

[14]

from the Design for the

[15]

, currently in the Scalable and Adaptive Internet

Solutions (SAIL) project [16].

1.2

Content Centric Networking (CCN): the Next Generation Network
(NGN) for the Future Internet (FI)
In order to alleviate the bandwidth problem while considering the feature of Internet

subscribers, CCN is proposed to effectively distribute popular data content to a huge
number of users. CCN was first proposed by V. Jacobson in 2009 [10]. At this time, CCN is
not only a theory proposal but also capability for the real world implementation. Thus,
many researches, projects and prototypes have been applied CCN

[17-18]

. Similar with

NDN, CCN is a network architecture build on Internet Protocol (IP) engineering principle,
but it treats content as a primitive. IP infrastructure services that have taken decades to
evolve, such as Domain Name Service (DNS) naming conventions and namespace
administration or inter-domain routing policies and conventions, can be readily used by
CCN. Indeed, because CCN hierarchically structured names are semantically compatible
with IP’s hierarchically structured addresses, the core IP routing protocols, Border
Gateway Protocol (BGP), Intermediate System To Intermediate System (IS-IS) and Open
Shortest Path First (OSPF), can be used as-is to deploy CCN co-existing or overlaying
with IP.
It should be noted that CCN and NDN can be exchangeable used in this thesis. CCN
refers to the architecture project V. Jacobson started at PARC, which included leading the
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development of a software codebase that represents a baseline implementation of this
architecture. NDN refers to the National Science Foundation (NSF)-funded Future Internet
Architecture project, a 12-campus collaboration that began in 2010 and included PARC.
The NDN project originally used CCN as its codebase, but as of 2013 has forked a version

to support the needs specifically related to the NSF-funded architecture research and
development.
CCN is built around secure name-based packet forwarding. While CCN is a new
architecture for efficient, secure distribution of content, it is compatible with today’s
Internet. A CCN network is built around CCN nodes that perform name-based forwarding
of packets between content consumers and content providers. CCN nodes also
transparently cache content as it is being transferred, and respond with cached content
when possible. Similar to IP, CCN may be deployed over any layer-2 technology. CCN
can also be layered over IP. CCN can be layered over a higher protocol such as HTTP,
should it be an expedient means to avoid firewalls. IP can also be deployed over CCN.
Deploying CCN in the existing Internet will initially layer all CCN packets on IP. Explicit
tunneling between CCN nodes will be used to bridge segments of the network where no
CCN nodes are placed. This feature allows incremental deployment of CCN in the Internet.
Eventually, we envision regions in the Internet that primarily use CCN, with residual IP
traffic possibly layered on native CCN nodes.
1.2.1

CCN architecture and operation workflow

In the CCN architecture, the basic operation of a CCN node is similar to an IP node.
CCN nodes receive and send packets over faces. A face is a connection point to an
application, or another CCN node, or some other kind of channel. A face may have
attributes that indicate expected latency and bandwidth, broadcast or multicast capability,
or other useful features. In CCN, two types of packets are envisioned to identify a content,
which is typical hierarchical and human readable. They are namely interest packets (IntPk)
and data packets (DataPk). A CCN node accepts IntPk and either sends them out on an
outgoing face, or replies directly with a matching content packet. If the node has
forwarded an IntPk then it should normally receive a responding content packet which it
will send to the original requesting face.

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Figure 1.1

Example of Client sends IntPk and receives DataPk.

It is important to note that CCN provides a naming framework, CCN names need not
be human readable. For names that are intended to be human readable, the current
convention is to use UTF-8 encoding. A CCN name is similar to an URI, where segment
has a label and a value. The label identifies the purpose of the name component, such as a
general name component used for routing, or a specialized component used to sequence
numbers or timestamps. There are also application specific labels. An example CCN name
for content might look like: /hust.edu.com/epiclab/documents/CCN-Introduction.pdf
In the above example, the first segment can be a globally routable name under the
control of some organization and can be derived from a Domain Name Service (DNS)
name assigned to that organization. Other conventions can also be used. The remaining
segments in the name are application specific. Protocol specific segments such as version
number or chunk numbers can also be included in the name. CCN name matching is based
on exact equality for segment. Exact matching for two names requires a match for all
corresponding segments. Prefix matching requires that all segments in the prefix be equal
to corresponding segments in the name. Since name matching examines entire segments,
simple hash techniques can provide efficient name matching.
A typical CCN framework is illustrated in Fig. 1.2. It presents a simple and effective
communication model. Typically, CCN nodes includes two data tables for name-based
routing, and a cache. They are Forwarding Information Base (FIB), Pending Interest Table

(PIT) and Content Store (CS).
Once a CCN node receives an IntPk, it looks up its CS. If an appropriate content is
found, the DataPk will be sent for a request, otherwise the IntPk will be checked in PIT.
PIT keeps track of unsatisfied IntPks. After PIT creates a new entry for an unsatisfied
IntPk, the IntPk is forwarded to upstream towards a potential content source based on
FIB’s information.
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A returned DataPk will be sent to downstream and stored on CS. In general, a content
is cached at routers for a certain time. When the caching deadline expires, the content is
removed to cope with the limited size of content storage. When CS is about to get full or
receive a new content, it stores the new content according to the underlying replacement
policy to leave space for the new content. Least Recently Used (LRU), Least Frequently
Used (LFU) and First In First Out (FIFO) are few notable examples of replacement
policies for CCN.

Figure 1.2

1.2.2

A typical CCN framework.

Transport and congestion control
In the transport layer of CCN, it can run over any layer-2 technology or above. The

CCN protocol requires very little functionality from the underlying packet transport. CCN

simply assumes stateless, unreliable, unordered, best-effort delivery of packets. Thus,
IntPk and/or DataPk might be lost or corrupted in transit, or the requested content might
be unavailable. In CCN, the return path for the DataPk is the reverse of the path for the
IntPk. Intermediate CCN nodes might fail as well, resulting in DataPk not reaching the
consumer (the application that sent the initial IntPk). Reliability in CCN is achieved by
retransmitting IntPk that have not yet been satisfied. The onus is on the content consumer
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to retransmit unsatisfied IntPk. The consumer can incorporate a timeout for every IntPk it
sends. Upon a timeout, an unsatisfied IntPk can be retransmitted.
CCN enforces flow balance, so that one IntPk results in at most one DataPk. As in
TCP, it is not necessary, or even desirable, to wait for a reply before sending out the next
IntPk, so many IntPk may be in flight simultaneously. The IntPk in CCN are analogous to
window advertisements in TCP. Unlike TCP, however, lost packets do not stall the
pipeline, as CCN packets are independently named and are not part of a specific
conversation. CCN maintains flow balance to enable efficient communication over links
with varying latencies and bandwidths and nodes with varying capabilities. Flow balance
at every node allows for simple and effective techniques to avoid congestion. The
underlying transport may receive duplicate IntPk and DataPk. All duplicate DataPk are
dropped by CCN nodes. Duplicate IntPk received over different paths are also detected
and dropped through the use of a hop limit. CCN transport can forward an IntPk on
multiple faces. This feature is especially useful in dynamically determining the best face
to use based on varying network and traffic conditions.
1.2.3

Security

CCN supports content-based security, rather than connection based security. In CCN,
every DataPk is authenticated with digital signatures and private content is encrypted. This
key attribute is what enables CCN to retrieve content from any CCN node without
contacting the content’s source. Since the name and the content are cryptographically
bound together, it is not necessary to trust the nodes or the connection.
Signature verification provides a means for securely associating a name, a publisher,
and content together. With appropriate verification only valid content will pass to the
client. As a side benefit, verification provides error detection for DataPk with a high
degree of reliability. Every DataPk has the following fields:
ƒ

Signature

ƒ

Name: the full CCN name for the content

ƒ

SignedInfo: key hash, key locator, and other details

ƒ

Data: the binary representation of the content

Every DataPk is signed with a signing key such as a public key. The signature is

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computed over the entire packet content exclusive of the Signature field, and so includes
the Name, the SignedInfo, and the Data. The exact method for computing the signature
depends on the cryptographic algorithm associated with the signing key.
The SignedInfo includes a hash of the key, a key locator, a timestamp, and some
additional details not within the scope of this document. A key locator may either include
the public key directly, or it make contain a name used to find the key. Signature
verification proves that the content can be trusted, provided that the key can be trusted.
CCN does not mandate policy decisions about trust. The level of trust for the key depends
on a trust model that is chosen by the client.
The information included in the DataPk must support a reasonable set of models, but
is not dependent on them. In practice, we expect several trust models, including a choice
of public key infrastructure. By providing a mechanism for transporting authenticated data,
CCN makes key distribution itself more reliable as keys can be distributed as CCN
Content Objects. Privacy can be incorporated in CCN and can be layered on top of CCN.
1.2.4 Publishing
A publisher can publish its content under a given name prefix. For example, all
content published by EPIC lab can be under the "/epiclab" name prefix. When an IntPk is
delivered to the publisher, a DataPk can be generated in response. The publisher signs
each DataPk with the publisher’s private key so it can be verified using the publisher’s
known public key.
In CCN, content that changes over time is represented as having multiple versions. It
is possible to represent new versions that are minimally changed from old versions as a set
of changes along with a reference to the older version, and this approach can provide
significant bandwidth savings. However, such a convention would be layered on top of the
core CCN protocol. CCN just provides a naming scheme that supports different versions.
Since CCN is a form of packet switching, large content must be represented as

multiple chunks. This feature allows for partial caching and delivery of partial results
before the entire content can be generated. There is no mechanism in the core CCN
protocol for deleting or revoking access to content. It is not always possible to find all of
the copies in an extended network. Rather, when publishing content, one gives it a

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time-to-live, so that cached copies expire. Thus, only the originating sources for the
content need to have their copies deleted to eventually ensure that the content is no longer
in the network (although clients cannot be guaranteed to not have copies).

1.3

Research on smart caching, content sharing, network architecture
and smart services provisioning in mobile network
From the background of CCN protocol introduction, caching decision, replacement

policies, and content sharing play crucial roles in CCN’s overall performance. We
research on smart caching to achieve higher hitting rate and faster convergence speed.
Moreover, we research on integrated CCN to existing network architecture and service to
improve Quality of Service (QoS) and Quality of Experiment (QoE).
1.3.1 Research on smart caching
(1) Prefix-based Prediction-oriented Cooperative Caching (PPCC)
We propose two novel replacement policies, named Prefix-based Prediction-oriented
(PP) and Prefix-based Prediction-oriented Cooperative Caching (PPCC). PP maintains
prefix tree of all contents in CS, then PP can determine popularity levels and give suitable

lifetime for each content, included new coming content. PPCC is PP with periodic
exchange prefix information among close by CCN nodes.
(2) Fine-Grained Popularity-based Caching (FGPC)
FGPC maintains a large table to generate three kinds of statistic information, namely
i) content names, ii) popularity levels of content by counting the frequency of appearances
of a content name, and iii) time stamp of used contents located in a cache. By filtering
popular content based on the large table, FGPC achieves effective caching with high
hitting ratio.
1.3.2

Research on content sharing and network architecture

(1) Wireless Body Area Network (WBAN)
WBAN includes a set of body sensor nodes which are placed around human body,
collecting data while sending them to medical center. In order to deliver the body signal to
remote terminals in timely fashion, an extended communication architecture dubbed

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"beyond-BAN communication" was proposed

[19-20]

. However, existing architectures are

not suitable for the scenarios with high mobility of both patients and physicians due to the

fluctuation of wireless links. Furthermore, when the amount of healthcare content is large,
the quality of delivery is hard to be guaranteed.
To address these challenging issues, we propose a novel network architecture, which
integrates WBAN with the Long Term Evolution (LTE) networking and CCN

[21-22]

. The

integration with LTE is to enlarge the radio coverage and guarantee the quality of wireless
transmissions, while the integration with CCN is to leverage edge router caching
technique to enhance the capacity of the WBAN coordinator, and to avoid the packets loss
by adapting to dynamic wireless link conditions with the adaptive streaming technique.
The experimental results conducted by OPNET Modeler prove that our solution improves
the Quality of Service (QoS) performance of WBAN transmission significantly.
(2) Vehicular Ad-hoc Network (VANET)
VANET is a technique that uses moving vehicles as wireless nodes in a mobile
network, in which each wireless node takes a role as an end-user and wireless router to
support wide range communications. Motivated by the increasing demand for efficient and
reliable information dissemination and retrieval, the Named Data Networking (NDN)[1]
presents a simple and effective communication model

[10, 23]

. We propose our solution,

Vehicular Named Data Networking (VENDNET), by inheriting the basic principle of the
NDN. However, extending the NDN model to the VANET is not straightforward due to a
lot of challenges in the vehicle environment such as the limited and intermittent
connectivity, and node mobility.

We first introduce some meeting challenges in different types of vehicle
communication mechanism

[24-25]

. Motivation from the NDN model simulation, the

VENDNET performance is taken into account by clearly comparing the VANET under
two scenarios: with typical clients–server connection and with NDN connection.
1.3.3

Research on smart services provisioning in mobile network

(1) Seamless service migration in mobile network
In the era of cloud and mobile social computing, a plethora of mobile applications
[1]

CCN and NDN can be exchangeable used with the same meaning.
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demand Mobile Stations (MSs) seamlessly interact with the cloud anyplace in a real-time
fashion. The mobility characteristic may cause a service on MS to be migrated between
different Data Centers (DC); otherwise, a packet delay is increased due to the fact that a
considerable geographical distance between MS and serving DC. With a current
networking architecture, IP address of either MS or Virtual Machine (VM) is changed
because of the VM migration, and an IP session between two peers is released and the

service is disrupted as a result.
We leverage the emerging CCN as a straightforward solution to these issues. Based
on unique content name identification, instead of IP address, service migration can be
continuous. Furthermore, a seamless service migration framework is proposed to conduct
the user's service request to the optimal DC, which satisfies user requirements, minimizes
the network usage and ensures application Quality of Experience (QoE).
(2) MM3C: Multi-Source Mobile Streaming in Cache-enabled CCN
Along with an ever-growing demand for rich video applications by an
ever-increasing population of mobile users, it is becoming difficult for the Internet
backbone to cope with a constantly increasing mobile traffic. Some previous research
work aim at solving the bottleneck issue of the Internet backbone considering
simultaneous multiple low streaming rate transmissions to mobile users. However, this
multi-source streaming approach does not consider redundant transmission of popular
contents.
A novel scheme integrating CCN with Multi-Source Mobile Streaming (MS2),
dubbed MM3C, is presented as a better solution to alleviate the problem. If the content is
popular, the previously queried content can be reused for multiple times to save bandwidth
capacity, reduce overall energy consumption, and improve users’ Quality of Experience
(QoE). Using OPNET Modeler for the simulation, the performance of MM3C is evaluated.
Compared to MS2 under the same network configuration, the simulation results show that
MM3C exhibits less number of bottleneck links, smaller average round trip time, while
achieving better performance in terms of traffic offloading.
(3) Mobile Data Offloading: A Named Data Networking Approach
Nowadays, mobile Internet becomes increasingly popular and the number of mobile
users is growing exponentially. Internet mobile subscribers often access to multimedia
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services, which consume a large amount of bandwidth in backbone transmissions as well
as server resource. With a traditional clients-server connections, servers are usually in
overload state by a huge number of users accessing the service at the same time. Moreover,
in a century of "green" Internet, it should be more effective in content distribution for a
huge number of users.
Name Data Networking (NDN) had been proposed as the promising solution for
above problem, which is a content name-oriented approach to disseminate content to edge
gateways/routers. In NDN, a content is cached at routers for a certain time. When deadline
is reached, the content will be removed to yield space due to the limited size of content
storage. If some content is popular, the previously queried one can be reused for multiple
times to save bandwidth.
In this section, we propose a solution for Long Term Evolution (LTE) mobile
network based on the concept of NDN. Furthermore, a novel caching policy dubbed by
Fine-Grained Popularity-based Caching (FGPC) is proposed. By OPNET Modeler
simulation, we carry out the evaluation in realistic mobile network with a huge number of
mobility LTE mobile users access to a single serve, where content names and content sizes
are obtained from real trace of Internet traffic. The obtained results show that Evolved
Packet Core (EPC) caching scheme can helps to satisfied most of the mobile users request,
which further increases the quality of service as well as offloads server traffic
significantly.

1.4

Summary
While IP has exceeded all expectations for facilitating ubiquitous interconnectivity, it

was designed for conversations between communications endpoints but is overwhelmingly
used for content distribution. Furthermore, today’s applications are typically written in
terms of what information they want rather than where it is located. The tremendous

growth of the Internet and the introduction of new applications to fulfill emerging needs,
has given rise to new requirements from the architecture, such as support for scalable
content distribution, mobility, security, trust, and so on. From above motivation, CCN was
successful designed for the Future Internet with a lot of key functionalities via
named-routing, on-path caching, mobility support and security.
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It should be noted that I use OPNET Modeler simulation tool for my research

[26-27]

.

OPNET Modeler is a very good tool for network designing and simulation. OPNET
Modeler was selected because most of the wired and wireless network components are
available in the OPNET 16.0 Modeler. In this version, a number of different models can
be created to simulate, analyze and compare their results. OPNET software can offer
students a broader insight in networking technologies, simulation techniques and the
impact of applications on network performance, and makes them feel as if they are real
network engineers.
In this section, I have presented about CCN architecture, operation workflow, and
applications. I also give the brief introduction for my research on smart caching, content
sharing, network architecture and smart services provisioning in mobile network. In the
next sections, we are going to describe my research areas one by one in detail.

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2

CCN performance improvement with smart caching

Having described the CCN architecture network, operation workflow, and having
given brief introduction for my research,

I am going to present novel smart caching in

order to improve CCN performance significantly.

2.1

Related works and their drawback
Recently, CCN has become a hot research area, and several projects and prototypes

have applied CCN

[28-30]

. Least Recently Used (LRU) and Least Frequently Used (LFU)

replacement policies are proposed in original CCN
are marked with time stamp

[31]

. At CCN node, all contents in CS

[31]

. When content is responded to satisfy IntPk, time stamp of

used content will be up-to-date. In LRU(LFU) policy, CS maintains the same lifetime for
all contents. And CS periodic refreshes a memory by checking lifetime of all contents. At
the refresh time, content is deleted if the different between a current time and its time
stamp equals or great than lifetime. In the other words, content recently used will be kept
and otherwise, content is deleted.
The drawback of LRU(LFU) policy only happens when buffer memory was full.
Generally, the LRU(LFU) policy makes a replacement decision (keep or delete) based on
time stamp in case of LRU (count number of sent in case of LFU) on each content. And
LRU(LFU) cannot know the popular level of coming content. Let us consider a situation
that a cache was in full state and a new unpopular content arrived. Then, one popular
content in CS has to be deleted and replaced by arrival unpopular content. For this reason,
hitting ratio of LRU(LFU) cannot be achieved maximum value. From above observation,
LRU and LFU ignore the advantage of CCN:
ƒ

LRU and LFU only base on object name to make replacement decision. Since,

LRU uses a time stamp on object name while LFU uses a frequencies of object name.
Because LRU and LFU do not consider prefixes popularity of the object, they cannot
recognize the popular level of new coming content.
ƒ

All contents have the same lifetime while their popularity levels are very skew.

In order to improve the precision of content replacement decision, a novel caching
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strategy, named Most Popular Content (MPC), was proposed in

[32]

. In MPC, every

router/gateway counts the local number of requests for each content name, and stores the
pair (content name; popularity count) into a content popularity table. Once the popularity
of a content object reaches a predetermined threshold in a caching node, it is tagged as a
popular content and is stored in the cache. By storing only popular content, MPC caches
less content, saves resources and reduces the number of cache operations, which makes it
achieve a higher hitting ratio in comparison to LRU and LFU.
Although some memory space is saved in MPC, the utilization of the cache is
typically not high. Based on our observation, higher hitting rates can be achieved by
intelligently utilizing vacant cache memory to contribute to a certain amount of hitting
ratio. For example, when there is room in available memory, unpopular content can be
stored. When the cache becomes full of content, unpopular contents are removed,
yielding space for popular ones. Furthermore, such strategy of caching less content,
adopted by MPC, results in a slow convergence of MPC in terms of hitting rate
performance.

From above observation, we propose two novel replacement policies, named
Prefix-based Prediction oriented Cooperative Caching (PPCC) and Fine-Grained
Popularity-based Caching (FGPC). In the next two sections, we are going to present them
in detail.

2.2

Popularity Prediction and Cooperative Caching (PPCC)
In our proposed Prefix-based Prediction-oriented (PP) policy, prefix tree (PT) is

created to maintain all contents in CS, then PP can determine popularity levels and give
suitable lifetime for each content, included new coming content. PPCC is PP with periodic
exchange prefix information among close by CCN nodes. Due to the important as
compared to the existing LRU or LFU solutions, the PP and PPCC schemes have the
following unique features:
ƒ

We carefully investigate the characteristics of CCN where "name" of data content

includes prefixes. We add a table to handle prefix name and prefix counter.
ƒ

The way Internet users request content is fine turned with Pareto principle, that is

20% popular content is requested by 80% number of users [33-37].
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