ICNRG K. Pentikousis, Ed.
Internet-Draft EICT
Intended Status: Informational B. Ohlman
Expires: April 24, 2014 Ericsson
E. Davies
Trinity College Dublin
S. Spirou
Intracom Telecom
G. Boggia
Politecnico di Bari
P. Mahadevan
PARC
October 21, 2013
Information-centric Networking: Evaluation Methodology
draft-irtf-icnrg-evaluation-methodology-00
Abstract
This document surveys the evaluation tools currently available to
researchers in the information-centric networking (ICN) area and
provides suggestions regarding methodology and metrics. Finally,
this document sheds some light on the impact of ICN on network
security.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Evaluation Methodology . . . . . . . . . . . . . . . . . . . . 4
2.1. ICN Simulators and Testbeds . . . . . . . . . . . . . . . 4
2.1.1. CCN and NDN . . . . . . . . . . . . . . . . . . . . . 4
2.1.2. Publish/Subscribe Internet Architecture . . . . . . . 6
2.1.3. NetInf . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1.4. COMET . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.5. Large-scale Testing . . . . . . . . . . . . . . . . . 7
2.2. Topology Selection . . . . . . . . . . . . . . . . . . . . 8
2.3. Traffic Load . . . . . . . . . . . . . . . . . . . . . . . 9
2.4. Choosing Relevant Metrics . . . . . . . . . . . . . . . . 11
2.4.1. Traffic Metrics . . . . . . . . . . . . . . . . . . . 13
2.4.2. System Metrics . . . . . . . . . . . . . . . . . . . . 15
2.5. Resource Equivalence and Tradeoffs . . . . . . . . . . . . 16
3. ICN Security Aspects . . . . . . . . . . . . . . . . . . . . . 16
3.1. Authentication . . . . . . . . . . . . . . . . . . . . . . 17
3.2. Authorization, Access Control and Statistics . . . . . . . 19
3.3. Privacy . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.4. Changes to the Network Security Threat Model . . . . . . . 20
4. Security Considerations . . . . . . . . . . . . . . . . . . . 21
5. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 21
6. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 21
7. Informative References . . . . . . . . . . . . . . . . . . . . 21
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 26
1. Introduction
Information-centric networking (ICN) marks a fundamental shift in
communications and networking. As discussed in [draft-irtf-icnrg-
scenarios], the development phase that ICN is going through, and the
plethora of approaches to tackle the hardest problems, make this a
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very active and growing research area but, on the downside, it also
makes it more difficult to compare different proposals on an equal
footing. Different ICN approaches have been evaluated in the peer-
reviewed literature using a mixture of theoretical analysis,
simulation and emulation techniques, and empirical (testbed)
measurements. These are all popular methods for evaluating network
protocols, architectures, and services in the networking community.
Typically, researchers follow a specific methodology based on the
goal of their experiment, e.g., whether they want to evaluate
scalability, quantify resource utilization, analyze economic
incentives, and so on, as we have discussed earlier. In addition,
though, we observe that ease and convenience of setting up and
running experiments can sometimes be a factor in published
evaluations.
It is worth pointing out that for well-established protocols, such as
TCP, performance evaluation using actual network deployments has the
benefit of realistic workloads and reflects the environment where the
service or protocol will be deployed. However, results obtained in
this environment are often difficult to replicate independently.
Beyond this, the difficulty of deploying future Internet
architectures and then engaging sufficient users to make such
evaluation realistic is often prohibitive.
Moreover, for ICN in particular, it is not yet clear what qualifies
as a "realistic workload". As such, trace-based analysis of ICN is
in its infancy, and more work is needed towards defining
characteristic workloads for ICN evaluation studies. Accordingly,
the experimental process itself as well as the evaluation methodology
are being actively researched for ICN architectures. Numerous
factors affect the experimental results, including the topology
selected, the background traffic that an application is being
subjected to, network conditions such as available link capacities,
link delays, and loss-rate characteristics throughout the selected
topology; failure and disruption patterns; node mobility; as well as
other aspects such as the diversity of devices used, and so on, as we
explain in the remainder of this section.
Apart from the technical evaluation of the functionality of an ICN
architecture, its future success will be largely driven by its
deployability and economic viability. Thus any evaluation will also
have to include an assessment of its incremental deployability in the
existing network environment together with a view of how the
technical functions will incentivize deployers to invest in the
capabilities that allow the architecture to spread across the
network.
This document incorporates input from ICNRG participants and their
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corresponding text contributions, has been reviewed by several ICNRG
active participants (see section 6), and represents the consensus of
the research group. That said, note that this document does not
constitute an IETF standard; see also [RFC5743].
The remainder of this document is organized as follows. Section 2
presents various techniques and considerations for evaluating
different ICN architectures. Then, Section 3 discusses the impact of
ICN on network security.
2. Evaluation Methodology
At this stage, we do not intend to develop a complete methodology or
a benchmarking tool. Instead, this document proposes key guidelines
alongside suggested data sets and high-level approaches that we
expect to be of interest to the ICN community as a whole. Through
this, researchers and practitioners alike would be able to compare
and contrast different ICN designs against each other, as well as
against the state of the art in host-centric solutions, and identify
the respective strengths and weaknesses.
2.1. ICN Simulators and Testbeds
Since ICN is still an emerging area, the community is still in the
process of developing effective evaluation environments, including
simulators, emulators, and testbeds. To date, none of the available
evaluation methodologies can be seen as the one and only community
reference evaluation tool. Furthermore, no single environment
supports all well-known ICN approaches. Simulators and emulators
should be able to capture, faithfully, all features and operations of
the respective ICN architecture(s). It is also essential that these
tools and environments come with adequate logging facilities so that
one can use them for in-depth analysis as well as debugging.
Additional requirements include the ability to support mid- to large-
scale experiments, the ability to quickly and correctly set various
configurations and parameters, as well as to support the playback of
traffic traces captured on a real testbed or network. Obviously,
this does not even begin to touch upon the need for strong validation
of any evaluated implementations.
The rest of this subsection summarizes the ICN simulators and
testbeds currently available to the community.
2.1.1. CCN and NDN
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The CCN project has open-sourced a software reference implementation
of the architecture and protocol called CCNx (www.ccnx.org). CCNx is
available for deployment on various operating systems and includes C
and Java libraries that can be used to build CCN applications. CCN-
lite (www.ccn-lite.net) is a lightweight implementation of the CCN
protocol, supports most of the key features of CCNx, and is
interoperable with CCNx. The core CCNx logic has been implemented in
about 1000 lines of code and is ideal for classroom work and course
projects as well as for quickly experimenting with CCNx extensions.
ndnSIM [ndnSIM] is a module that can be plugged into the ns-3
simulator and supports the core features of CCN. One can use ndnSIM
to experiment with various CCN applications and services as well as
components developed for CCN such as routing protocols, caching and
forwarding strategies. The code for ns-3 and ndnSIM is openly
available to the community and can be used as the basis for
implementing ICN protocols or applications. For more details see
http://www.nsnam.org and http://www.ndnsim.net.
ccnSim [ccnSim] is another CCN-specific simulator that was specially
designed to handle forwarding of a large number of CCN-chunks.
ccnSim is written in C++ for the OMNeT++ simulation framework
(www.omnetpp.org). Interested readers could consider also the
Content Centric Networking Packet Level Simulator [CCNPL]. Finally,
CCN-Joker [CCNj] is an application-layer platform that can be used to
build a CCN overlay. CCN-Joker emulates in user-space all basic
aspects of a CCN node (e.g., handling of Interest and Data packets,
cache sizing, replacement policies), including both flow and
congestion control. The code is open source and is suitable for both
emulation-based analyses and real experiments.
An example of a testbed that supports CCN is the Open Network Lab
(see https://onl.wustl.edu/). The ONL testbed currently comprises 18
extensible gigabit routers and over a 100 computers representing
clients and is freely available to the public for running CCN
experiments. Nodes in ONL are preloaded with CCNx software. ONL
provides a graphical user interface for easy configuration and
testbed set up as per the experiment requirements, and also serves as
a control mechanism, allowing access to various control variables and
traffic counters. It is also possible to run and evaluate CCN over
popular testbeds such as PlanetLab (www.planet-lab.org), Emulab
(www.emulab.net), and Deter (www.isi.deterlab.net) by directly
running the CCNx open-source code on PlanetLab and Deter nodes,
respectively.
NEPI, the Network Experimentation Programming Interface,
(http://nepi.inria.fr) is a tool developed for controlling and
managing large-scale network experiments. NEPI provides an experiment
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description language to design network experiments, describing
topology, applications, and a controller to automatically deploy
those experiments on target experimentation environments, such as
PlanetLab. The controller is also capable of collecting result and
log files during the experiment execution. NEPI also allows to
specify node selection filters while designing the experiment,
thereby supporting automatic discovery and provisioning of testbed
nodes during experiment deployment, without the user having to hand-
pick them. It is simple and efficient to use NEPI to evaluate CCNx on
large-scale testbeds such as PlanetLab.
2.1.2. Publish/Subscribe Internet Architecture
The PSIRP project has open-sourced its Blackhawk publish-subscribe
(Pub/Sub) implementation for FreeBSD; more details are available
online at http://www.psirp.org/downloads.html. Despite being limited
to one operating system, the code base also provides a virtual image
to allow its deployment on other environments through virtualization.
The code distribution features a kernel module, a file system and
scope daemon, as well as a set of tools, test applications and
scripts. This work was extended as part of the PURSUIT project,
resulting in the development of the Blackadder prototype for Linux
and FreeBSD. It currently runs on a testbed across Europe, America
(MIT) and Japan (NICT). All sites are connected via OpenVPN, which
exports a virtual Ethernet device to all machines in the testbed. In
total, 40 machines in a graph topology containing one Topology
Manager and one Rendezvous node that handle all publish/subscribe and
topology formation requests are interconnected [IEICE].
Moreover, the ICN simulation environment [ICN-Sim] allows the
simulation of new techniques for topology management following the
Publish-Subscribe paradigm and the PSIRP approach. The simulator is
based on the OMNET++ simulator and the INET/MANET frameworks. It is
currently publicly available at
http://sourceforge.net/projects/icnsim. A design characteristic of
this platform is the separation between the network and topology
management policies. An interface is used to provide this
functionality and policies can be imported and applied in the network
as topology manager applications running on top of this interface.
2.1.3. NetInf
The EU FP7 4WARD and SAIL projects have made a set of open-source
implementations available; see http://www.netinf.org/open-source for
more details. Of note, two software packages are available. The
first one is a set of tools for NetInf implementing different aspects
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of the protocol (e.g., NetInf URI format, HTTP and UDP convergence
layer) using different programming languages. The Java
implementation provides a local caching proxy and client. The second
one, is a OpenNetInf prototype from the 4WARD project. Besides a
rich set of NetInf mechanisms implemented, it also provides a browser
plug-in and video streaming software. The SAIL project developed a
hybrid host-centric and information-centric network architecture
called the Global Information Network (GIN). The prototype for this
can be downloaded from http://gin.ngnet.it.
2.1.4. COMET
The EU FP7 COMET project developed a simulator, called Icarus, which
implements ProbCache [PROBCACHE], centrality-based in-network caching
[CL4M] and the hash-route-based algorithms detailed in [HASHROUTE].
The simulator is built in Python and makes use of the Fast Network
Simulator Setup tool [FNSS] to configure the related parameters of
the simulation. The simulator is available from:
https://github.com/lorenzosaino/icarus/
2.1.5. Large-scale Testing
An important consideration in the evaluation of any kind of future
Internet mechanism, lies in the characteristics of that evaluation
itself. Often, central to the assessment of the features provided by
a novel mechanism, lies the consideration of how it improves over
already existing technologies, and by "how much." With the
disruptive nature of clean-slate approaches generating new and
different technological requirements, it is complex to provide
meaningful results for a network layer framework, in comparison with
what is deployed in the current Internet. Thus, despite the
availability of ICN implementations and simulators, the need for
large-scale environments supporting experimental evaluation of novel
research is of prime importance to the advancement of ICN deployment.
In this regard, initiatives such as the Future Internet Research and
Experimentation Initiative (www.ict-fire.eu), enable researchers to
test new protocols and architectures in real conditions over
production networks (e.g., through virtualization and software-
defined networking mechanisms), simplifying the validation of future
evolutions and reducing the gap between research and deployment.
Similarly, Future Internet Design (www.nets-find.net) is a long-term
initiative along the same direction in the US. GENI (www.geni.net)
also offers experimentation infrastructure as does PlanetLab
(www.planet-lab.org), which likely offers the largest testbed
available today. Those wishing to perform smaller, more controlled
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experiments can also consider the Emulab testbed (www.emulab.net),
which allows various topologies to be configured.
The Asia Future Internet Forum (www.asiafi.net) has also designed a
testbed mainly used for ICN experiments. This testbed consists of
multiple servers located in Asia and will be (presumably) expanded
with servers in other locations. Each testbed server includes
multiple Linux kernel-based LXCs. One container is called "bridge
container" and the other container is called "user container". A
bridge container has a global IP address used to connect to the
physical network through bridge mode, and a local (private) IP
address. A user container, which is assigned to each researcher,
connects to the bridge container in the same server using their local
IP addresses. A user container connects to the researcher's remote
containers located in different servers via tunnels established
between its local bridge container and the remote bridge containers.
Finally, the National Institute of Information and Communications
Technology (NICT) builds and operates the high-performance testbed
JGN-X (see http://www.jgn.nict.go.jp/english/index.html), which has
cutting-edge network functions and technologies including those
currently in development. JGN-X aims to establish new-generation
network technology and accelerate the R&D in areas such as network
virtualization and advanced operations of virtualized layers. JGN-X
is used for collaboration among developers in order to foster the
establishment and expansion of new-generation network technology.
2.2. Topology Selection
[draft-irtf-icnrg-scenarios] introduced several topologies that have
been used in ICN studies so far but, to date and to the best of our
understanding, there is no single topology that can be used to easily
evaluate all aspects of the ICN paradigm. There is rough consensus
that the classic dumbbell topology cannot serve well future
evaluations of ICN approaches. Therefore, one should consider a
range of topologies, each of which would stress different aspects, as
outlined earlier in this document. Current Internet traces are also
available to assist in this, e.g. see
http://www.caida.org/data/active/internet-topology-data-kit and
http://www.cs.washington.edu/research/networking/rocketfuel.
Depending on what is the focus of the evaluation, intra-domain
topologies alone may be appropriate. However, those interested, for
example, in quantifying transit costs will require inter-domain
traces (note that the above CAIDA traces offer this). Scalability is
an important consideration in this choice of this with CAIDA's ITDK
traces recording millions of routers across thousands of domains.
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Beyond these traces there is a wide range of synthetic topologies,
such as the Barabasi-Albert model [BA] and the Watts-Strogatz small-
world topology [WATTS]. These synthetic traces allow experiments to
be performed whilst controlling various key parameters (e.g. degree).
Through this, different aspects can be investigated, such as
inspecting resilience properties. For some research, this may be
more appropriate as, practically speaking, there are no assurances
that a future ICN will share the same topology with today's networks.
Besides defining the evaluation topology as a graph G = (V,E), where
V is the set of vertices (nodes) and E is the set of edges (links),
one should also clearly define and list the respective matrices that
correspond to the network, storage and computation capacities
available at each node as well as the delay characteristics of each
link, so that the results obtained can be easily replicated in other
studies. Recent work by Hussain and Chen [Montage], although
currently addressing host-centric networks, could also be leveraged
and be extended by the ICN community. Measurement information can
also be taken from existing platforms such as iPlane
(http://iplane.cs.washington.edu), which can be used to provide
configuration parameters such as access link capacity and delay.
Alternatively, synthetic models such as [DELAY] can be used to
configure such topologies.
Finally, the dynamic aspects of a topology, such as node and content
mobility, disruption patterns, packet loss rates as well as link and
node failure rates, to name a few, should also be carefully
considered. As mentioned in [draft-irtf-icnrg-scenarios], for
example, contact traces from the DTN community could also be used in
ICN evaluations.
2.3. Traffic Load
As we are still lacking ICN-specific traffic workloads we can
currently only extrapolate from today's workloads. In this
subsection we provide a first draft of a set of common guidelines, in
the form of what we will refer to as a content catalog for different
scenarios. This catalog, which is based on previously published
work, could be used to evaluate different ICN proposals, for example,
on routing, congestion control, and performance, and can be
considered as other kinds of ICN contributions emerge.
We take scenarios from today's Web, file sharing (BitTorrent-like)
and User Generated Content (UGC) platforms (e.g., YouTube), as well
as Video on Demand (VoD) services. Publicly available traces for
these include those available from web sites such as
http://mikel.tlm.unavarra.es/~mikel/bt_pam2004,
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http://multiprobe.ewi.tudelft.nl/multiprobe.html,
http://an.kaist.ac.kr/traces/IMC2007.html, and
http://traces.cs.umass.edu/index.php/Network/Network.
The content catalog for each type of traffic can be characterized by
a specific set of parameters: the cardinality of the estimated
content catalog, the average size of the exchanged contents (either
chunks or entire named information objects), and the statistical
distribution that best reflect the popularity of objects and their
request frequency. Table I summarizes the content catalog. With
this shared point of reference, the use of the same set of parameters
(depending on the scenario of interest) among researchers will be
eased, and different proposals could be compared on a common base.
Table I. Content Catalog
Traffic | Catalog | Mean Object Size | Popularity Distribution
Load | Size | [L4][L5][L7][L8] | [L3][L5][L6][L11][L12]
| [L1][L2]| [L9][L10] |
| [L3][L5]| |
==================================================================
Web | 10^12 | Chunk: 1-10 kB | Zipf with
| | | 0.64 <= alpha <= 0.83
------------------------------------------------------------------
File | 5x10^6 | Chunk: 250-4096 kB | Zipf with
sharing | | Object: ~800 MB | 0.75 <= alpha<= 0.82
------------------------------------------------------------------
UGC | 10^8 | Object: ~10 MB | Zipf, alpha >= 2
------------------------------------------------------------------
VoD | 10^4 | Object: ~100 MB | Zipf, 0.65 <= alpha <= 1
==================================================================
* UGC = User Generated Content ** VoD = Video on Demand
Several studies in the past years have stated that Zipf's law is the
discrete distribution that best represents the request frequency in a
number of application scenarios, ranging from the Web to VoD
services. The key aspect of this distribution is that the frequency
of a content request is inversely proportional to the rank of the
content itself, i.e., the smaller the rank, the higher the request
frequency. If we denote with M the content catalog cardinality and
with 1 <= i <= M the rank of the i-th most popular content, we can
express the probability of requesting the content with rank "i" as:
P(X=i) = ( 1/i^(alpha) ) / C, with C = SUM(1 / j^(alpha)), alpha > 0
where the sum is obtained considering all values of j, 1 <= j <= M.
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Further, a variation of the Zipf distribution, termed the Mandelbrot-
Zipf distribution, has been suggested by [P2PMod] to better model
environments where nodes can locally store previously requested
content. For example, it was observed that peer-to-peer file sharing
applications typically exhibited a 'fetch-at-most-once' style of
behavior. This is because peers tend to persistently store the files
they download, a behavior that may also be prevalent in ICN.
2.4. Choosing Relevant Metrics
ICN is a networking concept that spun out of the desire to align the
operation model of a network with the model of its typical use. For
TCP/IP networks, this means to change the mechanisms of data access
and transport from a host-to-host model to a user-to-information
model. The premise is that the effort invested in changing models
will be offset, or even surpassed, by the potential of a "better"
network. However, such a claim can be validated only if it is
quantified.
Quantification of network performance requires a set of standard
metrics. These metrics should be broad enough so they can be applied
equally to host-centric and information-centric (or other) networks.
This will allow reasoning about a certain ICN approach in relation to
an earlier version of the same approach, to another ICN approach or
to the incumbent host-centric approach. It will therefore be less
difficult to gauge optimization and research direction. On the other
hand, the metrics should be targeted to network performance only and
should avoid unnecessary expansion into the physical and application
layers. Similarly, at this point, it is more important to capture as
metrics only the main figures of merit and to leave more esoteric and
less frequent cases for the future.
To arrive at a set of relevant metrics, it would be beneficial to
look at the metrics used in existing ICN approaches, such as CCN
[CCN] [VoCCN] [NDNP], NetInf [4WARD6.1] [4WARD6.3] [SAIL-B2] [SAIL-
B3], PURSUIT [PRST4.5], COMET [CMT-D5.2] [CMT-D6.2], Connect [SHARE]
[RealCCN], and CONVERGENCE [ICN-Web] [ICN-Scal] [ICN-Tran]. The
metrics used in these approaches fall into two categories: metrics
for the approach as a whole, and metrics for individual components
(resolution, routing, etc.). Metrics for the entire approach are
further subdivided into traffic and system metrics. It is important
to note that the various approaches do not name or define metrics
consistently. This is a major problem when trying to find metrics
that allow comparison between approaches. For the purposes of
exposition, in what follows we have tried to smooth differences by
pitting similarly defined metrics under the same name. Also, due to
space constraints, we have chosen to report here only the most common
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metrics between approaches. For more details the reader should
consult the references for each approach.
Traffic metrics in existing ICN approaches are summarized in Table
II. These are metrics for evaluating an approach mainly from the
perspective of the end user, i.e., the consumer, provider, or owner
of the content or service. Depending on the level where these
metrics are measured, we have made the distinction into user,
application and network-level traffic metrics. So for example,
network-level metrics are mostly focused on packet characteristics,
whereas user-level metrics can cover elements of human perception.
The approaches don't make this distinction explicitly, but we can see
from the table that CCN and NetInf have used metrics from all levels,
PURSUIT and COMET have focused on lower-level metrics, and Connect
and CONVERGENCE prefer higher-level metrics. Throughput and download
time seem to be the most popular metrics altogether.
Table II. Traffic metrics used in ICN evaluations
User | Application | Network
======================================================
Download | Goodput | Startup | Throughput | Packet
time | | latency | | delay
==================================================================
CCN | x | x | | x | x
------------------------------------------------------------------
NetInf | x | | x | x | x
------------------------------------------------------------------
PURSUIT | | | x | x | x
------------------------------------------------------------------
COMET | | | x | x |
------------------------------------------------------------------
Connect | x | | | |
------------------------------------------------------------------
CONVERGENCE | x | x | | |
==================================================================
While traffic metrics are more important for the end user, the owner
or operator of the networking infrastructure is normally more
interested in system metrics, which can reveal the efficiency of an
approach. The various ICN approaches have used system metrics, but
unfortunately the situation is not as coherent as with the traffic
metrics. The most common system metrics used are: protocol overhead,
total traffic, transit traffic, cost savings, router cost, and router
energy consumption.
Besides the traffic and systems metrics that aim to evaluate an
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approach as a whole, all of the surveyed approaches also evaluate the
performance of individual components. The name resolution,
request/data routing, and data caching are the most typical
components, so Table III presents the popular metrics for each of
those components. FIB size and path length, i.e., the routing
component metrics, are almost ubiquitous among approaches, perhaps
due to the networking background of the involved researchers. That
might be also the reason for the sometimes decreased focus on traffic
and system metrics, in favor of component metrics. It can certainly
be argued that traffic and system metrics are affected by component
metrics, however no approach has made the relationship clear. With
this in mind, and also taking into account that traffic and system
metrics are readily useful to end users and network operators, we
will restrict ourselves to those in the following sections.
Table III. Component metrics in existing ICN approaches
Resolution | Routing | Cache
======================================================
Resolution | Request | FIB | Path | Size | Hit
time | rate | size | length | | ratio
==================================================================
CCN | x | | x | x | x | x
------------------------------------------------------------------
NetInf | x | x | | x | | x
------------------------------------------------------------------
PURSUIT | | | x | x | |
------------------------------------------------------------------
COMET | x | x | x | x | | x
------------------------------------------------------------------
CONVERGENCE | | x | x | | x |
==================================================================
Before proceeding, we should note that we'd like our metrics to be
applicable to host-centric networks as well. Standard metrics
already exist for IP networks and it would certainly be beneficial to
take them into account. It is encouraging that many of the metrics
used by existing ICN approaches can also be used on IP networks and
that all of the approaches have tried on occasion to draw the
parallels.
2.4.1. Traffic Metrics
At their core, host-centric and information-centric networking
function as data transport services. Information of interest to a
user resides in one or more storage points connected to the network
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and, on the user's request, the network transports this information
to the user for consumption. We could therefore do worse than to
quantify the data transport performance of the network in terms of
Quality of Service (QoS) metrics.
The IETF has been working for more than a decade on devising metrics
and methods for measuring the performance of IP networks. The work
has been carried out largely within the IPPM WG, guided by a relevant
framework [RFC2330]. IPPM metrics include delay, delay variation,
loss, reordering, and duplication. While the IPPM work is certainly
based on packet-switched IP networks, it is conceivable that it can
be modified and extended to cover ICN networks as well. However, more
study is necessary to turn this claim into a certainty. Many experts
have toiled for a long time on devising and refining the IPPM metrics
and methods, so it would be an advantage to use IPPM on measuring ICN
performance. In addition, IPPM works already for host-centric
networks, so comparison with information-centric networks would
entail only the ICN extension of the IPPM framework. Finally, an
important benefit of measuring the transport performance of a network
at it's output, using QoS metrics such as IPPM, is that it can be
done mostly without any dependence to applications.
Another option for measuring transport performance would be to use
Quality of Service metrics, not at the output of the network like
with IPPM, but at the input to the application. So for an
application like live video streaming the relevant metrics would be
startup latency, playout lag and playout continuity. The benefit of
this approach is that it abstracts away all details of the underlying
transport network, so it can be readily applied to compare between
networks of different concepts (host-centric, information-centric, or
other). As implied earlier, the drawback of the approach is its
dependence on the application, so it is likely that different (types
of) applications will require different metrics. It might be
possible to identify standard metrics for each type of application,
but the situation is not as clear as with IPPM metrics and further
investigation is necessary.
At a higher level of abstraction, we could measure the network's
transport performance at the application output. This entails
measuring the quality of the transported and reconstructed
information as perceived by the user during consumption. In such an
instance we would use Quality of Experience (QoE) metrics, which are
by definition dependent on the application. For example, the
standardized methods for obtaining a Mean Opinion Score (MOS) for
VoIP (e.g., ITU-T P.800) is quite different from those for IPTV
(e.g., PEVQ). These methods are notoriously hard to implement, as
they involve real users in a controlled environment. Such
constraints can be relaxed or dropped by using methods that model
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human perception under certain environments, but these methods are
typically intrusive. The most important drawback of measuring
network performance at the output of the application is that only one
part of each measurement is related to network performance. The rest
is related to application performance, e.g., video coding, or even
device capabilities, both of which are irrelevant to our purposes
here and are generally hard to separate. We therefore see the use of
QoE metrics in measuring ICN performance as a poor choice.
2.4.2. System Metrics
Overall system metrics that need to be considered include
reliability, scalability, energy efficiency, and delay/disconnection
tolerance. In deployments where ICN is addressing specific
scenarios, relevant system metrics could be derived from current
experience. For example, in IoT scenarios, which were discussed
earlier in [draft-irtf-icnrg-scenarios], it is reasonable to consider
the current generation of sensor nodes, sources of information, and
even measurement gateways (e.g., for smart metering at homes) or
smartphones. In this case, ICN operation ought to be evaluated with
respect not only to overall scalability and network efficiency, but
also the impact on the nodes themselves. Karnouskos et al.
[SensReqs] provide a comprehensive set of sensor and IoT-related
requirements, for example, which include aspects such as resource
utilization, service life-cycle management and device management.
Additionally, various specific metrics are also critical in
constrained environments, such as CPU processing requirements,
signaling overhead, and memory allocation for caching procedures in
addition to power consumption and battery lifetime. Also, in nodes
acting as gateways, which typically not only act as a point of
service to a large number of nodes, but also have to satisfy the
information requests from remote entities; they need to consider
scalability-related metrics, such as frequency and processing of
successfully satisfied information requests.
Finally, given the in-network caching functionality of Information-
Centric Networks, metrics for the efficiency and performance of in-
network caching have to be defined. Such metrics will need to guide
researchers and operators regarding the performance of in-network
caching algorithms. A first step on this direction has been made in
[L9]. The paper proposes a formula that approximates the proportion
of time that a content stays in a network cache. The model takes as
input the rate of requests for a given content (the Content of
Interest) and the rate of requests for all other contents that go
through the given network element (router) and move the CoI down in
the (LRU) cache. The formula takes also into account the size of the
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cache of this router.
The output of the model essentially reflects the probability that the
CoI will be found in a given cache. The initial study [L9] is
applied to the CCN/NDN framework, where contents get cached at every
node they traverse, while efforts are underway to assess the accuracy
of the model for other caching strategies. The formula according to
which the probability or proportion is calculated is given by:
pi = [mu/(mu+lambda)]^N,
where lambda is the request rate for CoI, mu is the request rate for
contents that move CoI down the cache and N is the size of the cache
(in slots).
The formula can be used to assess the caching performance of the
system and can also potentially be used to identify the gain of the
system due to caching. This can then be used to compare against gains
by other factors, e.g., addition of extra bandwidth in the network.
2.5. Resource Equivalence and Tradeoffs
As we have seen above, every ICN network is built from a set of
resources, which include link capacities, different types of memory
structures and repositories used for storing named information
objects and chunks temporarily (i.e. caching) or persistently, as
well as name resolution and other lookup services. Complexity and
processing needs in terms of forwarding decisions, management (e.g.
need for manual configuration, explicit garbage collection, and so
on), and routing (i.e. amount of state needed, need for manual
configuration of routing tables, support for mobility, etc.) set the
stage for a range of engineering tradeoffs.
In order to be able to compare different ICN approaches it would be
beneficial to be able to define equivalence in terms of different
resources which today are considered incomparable. For example,
would provisioning an additional 5 Mb/s link capacity lead to better
performance than adding 100 GB of in-network storage? Within this
context one would consider resource equivalence (and the associated
tradeoffs) for example for cache hit ratios per GB of cache,
forwarding decision times, CPU cycles per forwarding decision, and so
on.
3. ICN Security Aspects
The introduction of an information-centric networking architecture
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and the corresponding communication paradigm changes many aspects of
network security. These will affect all the scenarios described in
[draft-irtf-icnrg-scenarios]. Additional evaluation will be required
to ensure relevant security requirements are appropriately met by the
implementation of the chosen architecture in the various scenarios.
The various ICN architectures that are currently proposed have
concentrated on authentication of delivered content to ensure the
integrity of the content. However the approaches are primarily
applicable to freely accessible content that does not require access
authorization, although they will generally support delivery of
encrypted content.
The introduction of widespread caching mechanisms may also provide
additional attack surfaces. The caching architecture to be used also
needs to be evaluated to ensure that it meets the requirements of the
usage scenarios.
In practice, the work on security in the various ICN research
projects has been heavily concentrated on authentication of content.
Work on authorization, access control, privacy and security threats
due to the expanded role of in-network caches has been quite limited.
A roadmap for improving the security model in NetInf can be found in
[NETINFSC]. In the rest of this section we briefly consider the
issues and provide pointers to the work that has been done on the
security aspects of the architectures proposed.
3.1. Authentication
For fully secure content distribution, content access requires that
the receiver needs to be able to reliably assess:
validity: is it a complete, uncorrupted copy of what was
originally published;
provenance: can the receiver identify the publisher, and, if so,
whether it and the source of any cached version of the
document can be adequately trusted; and
relevance: is the content an answer to the question that the
receiver asked.
All the ICN architectures considered in this document primarily
target the validity requirement using strong cryptographic means to
tie the content request name to the content. Provenance and
relevance are directly targeted to varying extents: There is a
tussle or trade-off between simplicity and efficiency of access and
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level of assurance of all these traits. For example, maintaining
provenance information can become extremely costly, particularly when
considering (historic) relationships between multiple objects.
Architectural decisions have therefore been taken in each case as to
whether the assessment is carried out by the ICN or left to the
application.
An additional consideration for authentication is whether a name
should be irrevocably and immutably tied to a static piece of
preexisting content or whether the name can be used to refer to
dynamically or subsequently generated content. Schemes that only
target immutable content can be less resource hungry as they can use
digest functions rather than public key cryptography for generating
and checking signatures. However, this can increase the load on
applications. This is because they are required to manage many names,
rather than using a single name for an item of evolving content that
changes over time (e.g. a piece of data containing an age reference).
NetInf uses the Named Information (ni) URI scheme [RFC6920] to
identify content. This allows NetInf to assure validity without any
additional information but gives no assurance on provenance or
relevance. A "search" request allows an application to identify
relevant content and applications may choose to structure content to
allow provenance assurance but this will typically require additional
network access. NetInf validity authentication is consequently
efficient in a network environment with intermittent connectivity as
it does not force additional network accesses and allows the
application to decide on provenance validation if required. NetInf
primarily targets static content, but an extension would allow
dynamic content to be handled. The immutable case only uses digest
functions.
DONA [DONA] and CCN [CCN], [SECCONT] integrate most of the data
needed to verify provenance into all content retrievals but need to
be able to retrieve additional information (typically a security
certificate) in order to complete the provenance authentication.
Whether the application has any control of this extra retrieval will
depend on the implementation. CCN is explicitly designed to handle
dynamic content allowing names to be pre-allocated and attached to
subsequently generated content. DONA offers variants for dynamic and
immutable content.
PURSUIT [PSTSEC] appears to allow implementers to choose the
authentication mechanism so that it can, in theory, emulate the
authentication strategy of any of the other architectures. It is not
clear whether different choices would lead to lack of
interoperability.
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3.2. Authorization, Access Control and Statistics
A potentially major concern for all the ICN architectures considered
here is that they do not provide any inbuilt support for an
authorization framework or for statistics monitoring. Once content
has been published and cached in servers, routers or end points not
controlled by the publisher, the publisher has no way to enforce
access control, determine which users have accessed the content or
revoke its publication. In fact, in some cases, it is even difficult
for the publishers themselves to perform access control, where
requests do not necessarily contain host/user identifier information.
Access could be limited by encrypting the content but the necessity
of distributing keys out-of-band appears to negate the advantages of
in-network caching. This also creates significant challenges when
attempting to manage and restrict key access. An authorization
delegation scheme has been proposed [ACDICN] but this requires access
to a server controlled by the publisher to obtain an access token
making it essentially just an out-of-band key distribution system.
Evaluating the impact of the absence of these features will be
essential for any scenario where an ICN architecture might be
deployed. It may have a seriously negative impact on the
applicability of ICN in commercial environments unless a solution can
be found.
3.3. Privacy
Another area where the architectures have not been significantly
analyzed is privacy. Caching implies a trade-off between network
efficiency and privacy. The activity of users is significantly more
exposed to the scrutiny of cache owners with whom they may not have
any relationship.
Although in many ICN architectures, the source of a request is not
explicitly identified, an attacker may be able to obtain considerable
information if s/he can monitor transactions on the cache and obtain
details of the objects accessed, the topological direction of
requests and information about the timing of transactions. The
persistence of data in the cache can make life easier for an attacker
by giving a longer timescale for analysis.
The impact of CCN on privacy has been investigated in a useful
master's thesis [CCNSEC]. The analysis in this thesis is mostly
applicable to all of the ICN architectures because it is mostly
focused on the common caching aspect. The privacy risks of named
data networking are also highlighted in [CCNPRIV]. Further work on
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privacy in ICNs can be found in [CONPRV].
3.4. Changes to the Network Security Threat Model
The architectural differences of the various ICN models as compared
to TCP/IP have consequences for network security. There is limited
consideration of the threat models and potential mitigation in the
various documents describing the architectures. The references
[CCNSEC] and [CONPRV] also consider the changed threat model. Some
of the key aspects are:
o Caching implies a tradeoff between network efficiency and user
privacy as discussed in Section 3.3.
o More powerful routers upgraded to handle persistent caching
increase the network's attack surface. This is particularly the
case in systems (e.g., CCN) that may need to perform cryptographic
checks on content that is being cached. For example, not doing
this could lead routers to disseminate invalid content.
o ICNs makes it difficult to identify the origin of a request as
mentioned in Section 4.3 slowing down the process of blocking
requests and requiring alternative mechanisms to differentiate
legitimate requests from inappropriate ones as access control
lists (ACLs) will probably be of little value for ICN requests.
o Denial-of-service (DoS) attacks may require more effort on ICN
than on TCP/IP but they are still feasible. One reason for this
is that it is difficult for the attacker to force repeated
requests for the same content onto a single node; ICNs naturally
spread content so that after the initial few requests, subsequent
requests will generally be satisfied by alternative sources,
blunting the impact of a DoS attack. That said, there are many
ways around this, e.g., generating random suffix identifiers that
always result in cache misses.
o Per-request state in routers can be abused for DoS attacks.
o Caches can be misused in the following ways:
+ Attackers can use caches as storage to make their own content
available.
+ The efficiency of caches can be decreased by attackers with the
goal of DoS attacks.
+ Content can be extracted by any attacker connected to the
cache, putting users' privacy at risk.
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Appropriate mitigation of these threats will need to be considered in
each scenario.
4. Security Considerations
This document does not impact the security of the Internet.
5. IANA Considerations
This document presents no IANA considerations.
6. Acknowledgments
Daniel Corujo and Gareth Tyson contributed to an earlier version of
this document.
This document has benefited from reviews, pointers to the growing ICN
literature, suggestions, comments and proposed text provided by the
following members of the IRTF Information-Centric Networking Research
Group (ICNRG), listed in alphabetical order: Marica Amadeo, Hitoshi
Asaeda, Claudia Campolo, Suyong Eum, Dorothy Gellert, Luigi Alfredo
Grieco, Myeong-Wuk Jang, Ren Jing, Will Liu, Antonella Molinaro,
Ioannis Psaras, Dirk Trossen, Jianping Wang, Yuanzhe Xuan, and Xinwen
Zhang.
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1998.
[RFC6920] Farrell, S., Kutscher, D., Dannewitz, C., Ohlman, B.,
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[ndnSIM] Afanasyev, A. et al., ndnSIM: NDN simulator for NS-3 NDN
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Architecture", Proc. SIGCOMM. ACM, 2007.
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Technical Report TR-2009-01, PARC, 2009.
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on Security Analysis", Deliverable 2.4, PURSUIT EU FP7
project, April 2012.
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[CCNSEC] Lauinger, T., "Security and Scalability of Content-Centric
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[CCNPRIV] Lauinger, Y., et al, "Privacy Risks in Named Data
Networking: What is the Cost of Performance?", ACM SIGCOMM
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[CONPRV] Chaabane, A et al, "Privacy in Content-Oriented
Networking: Threats and Countermeasures", arXiv:1211.5183,
2012.
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Authors' Addresses
Kostas Pentikousis (editor)
EICT GmbH
Torgauer Strasse 12-15
10829 Berlin
Germany
Email: k.pentikousis@eict.de
Borje Ohlman
Ericsson Research
S-16480 Stockholm
Sweden
Email: Borje.Ohlman@ericsson.com
Elwyn Davies
Trinity College Dublin/Folly Consulting Ltd
Dublin, 2
Ireland
Email: davieseb@scss.tcd.ie
Spiros Spirou
Intracom Telecom
19.7 km Markopoulou Avenue
19002 Peania, Athens
Greece
Email: spis@intracom.com
Gennaro Boggia
Dep. of Electrical and Information Engineering
Politecnico di Bari
Via Orabona 4
70125 Bari
Italy
Email: g.boggia@poliba.it
Priya Mahadevan
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INTERNET DRAFT ICN Evaluation Methodology October 21, 2013
Palo Alto Research Center
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Pentikousis, et al. Expires April 24, 2014 [Page 27]