ICNRG K. Pentikousis, Ed.
Internet-Draft EICT
Intended Status: Informational B. Ohlman
Expires: October 14, 2016 Ericsson
E. Davies
Trinity College Dublin
S. Spirou
Intracom Telecom
G. Boggia
Politecnico di Bari
April 12, 2016
Information-centric Networking: Evaluation and Security Considerations
draft-irtf-icnrg-evaluation-methodology-05
Abstract
This document presents a number of considerations regarding
evaluating Information-centric Networking (ICN) and sheds some light
on the impact of ICN on network security. It also surveys the
evaluation tools currently available to researchers in the ICN area
and provides suggestions regarding methodology and metrics.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Evaluation Considerations . . . . . . . . . . . . . . . . . . 4
2.1. Topology Selection . . . . . . . . . . . . . . . . . . . . 4
2.2. Traffic Load . . . . . . . . . . . . . . . . . . . . . . . 5
2.3. Choosing Relevant Metrics . . . . . . . . . . . . . . . . 10
2.3.1. Traffic Metrics . . . . . . . . . . . . . . . . . . . 13
2.3.2. System Metrics . . . . . . . . . . . . . . . . . . . . 14
2.4. Resource Equivalence and Tradeoffs . . . . . . . . . . . . 15
3. ICN Security Aspects . . . . . . . . . . . . . . . . . . . . . 15
3.1. Authentication . . . . . . . . . . . . . . . . . . . . . . 16
3.2. Authorization, Access Control and Logging . . . . . . . . . 18
3.3. Privacy . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.4. Changes to the Network Security Threat Model . . . . . . . 19
4. Evaluation Tools . . . . . . . . . . . . . . . . . . . . . . . 20
4.1. Open-source Implementations . . . . . . . . . . . . . . . 20
4.2. Simulators and Emulators . . . . . . . . . . . . . . . . . 21
4.2.1. ndnSIM . . . . . . . . . . . . . . . . . . . . . . . . 22
4.2.2. ccnSIM . . . . . . . . . . . . . . . . . . . . . . . . 22
4.2.3. Icarus simulator . . . . . . . . . . . . . . . . . . . 22
4.3. Experimental Facilities . . . . . . . . . . . . . . . . . 23
4.3.1. Open Network Lab [ONL] . . . . . . . . . . . . . . . . 23
4.3.2. POINT testbed . . . . . . . . . . . . . . . . . . . . 24
4.3.3. CUTEi: Container-based ICN testbed . . . . . . . . . . 24
5. Security Considerations . . . . . . . . . . . . . . . . . . . 25
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 25
7. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 25
8. Informative References . . . . . . . . . . . . . . . . . . . . 25
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 34
1. Introduction
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Information-centric Networking (ICN) is a networking concept that
arose from the desire to align the operation model of a network with
the model of its typical use. For TCP/IP networks, this implies
changing 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.
Different ICN approaches are evaluated in the peer-reviewed
literature using a mixture of theoretical analysis, simulation and
emulation techniques, and empirical (testbed) measurements. The
specific methodology employed may depend on the experimentation goal,
e.g., whether one wants to evaluate scalability, quantify resource
utilization, or analyze economic incentives. In addition, though, we
observe that ease and convenience of setting up and running
experiments can sometimes be a factor in published evaluations. As
discussed in [RFC7476], the development phase that ICN is going
through and the plethora of approaches to tackle the hardest problems
make this a very active and growing research area but, on the
downside, it also makes it more difficult to compare different
proposals on an equal footing.
Performance evaluation using actual network deployments has the
advantage of realistic workloads and reflects the environment where
the service or protocol are to be deployed. In the case of ICN,
however, it is not currently clear what qualifies as a "realistic
workload". 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 and the
evaluation methodology per se are actively being researched for
different 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 and the diversity of devices used.
The goal of this document is to summarize evaluation guidelines and
tools alongside suggested data sets and high-level approaches. We
expect this to be of interest to the ICN community as a whole as it
can assist researchers and practitioners alike 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. We note that 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. Therefore, ICN evaluations should assess
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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 has been produced by the IRTF Information-centric
Networking Research Group (ICNRG). The main objective of the ICNRG is
to couple ongoing ICN research in the above areas with solutions that
are relevant for evolving the Internet at large. The ICNRG produce
documents that provides guidelines for experimental activities in the
area of ICN so that different, alternative solutions can be compared
consistently, and information sharing accomplished for experimental
deployments. This document incorporates input from ICNRG participants
and their corresponding text contributions; it has been reviewed by
several ICNRG active participants (see section 7), 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. Section 3 discusses the impact of ICN
on network security. Section 4 surveys the tools currently available
to ICN researchers.
2. Evaluation Considerations
It is clear that the way we evaluate IP networks will not be directly
applicable for evaluating ICN. In IP the focus is on the performance
and characteristics of end-to-end connections between a source and a
destination. In ICN the "source" responding to a request can be any
ICN node in the network and may change from request to request. This
makes it difficult to use concepts like delay and throughput in a
traditional way. In addition evaluating resource usage in ICN is a
more complicated task as memory used for caching affects delays and
use of transmission resources, see the discussion on resource
equivalents at the end of this section.
There are two major types of evaluations of ICN that we see a need to
make. One type is to compare ICN to traditional networking and one
type is to compare different ICN implementations and approaches
against each other.
In this section we details some of the functional components needed
when evaluating different ICN implementations and approaches.
2.1. Topology Selection
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There's a wealth of earlier work on topology selection for simulation
and performance evaluation of host-centric networks. While the
classic dumbbell topology is regarded as inappropriate for ICN, most
ICN studies so far have been based on that earlier work for host-
centric networks [RFC7476]. However, there is no single topology that
can be used to easily evaluate all aspects of ICN. Therefore, one
should choose from a range of topologies depending on the focus of
the evaluation.
For scalability and resilience studies, there is a wide range of
synthetic topologies, such as the Barabasi-Albert model [BA] and the
Watts-Strogatz small-world topology [WATTS]. These allow experiments
to be performed whilst controlling various key parameters (e.g., node
degree). These synthetic topologies are appropriate in the general
case, as there are no practical assurances that a future information-
centric network will share the same topology with today's networks.
When studies look at cost (e.g., transit cost) or migration to ICN,
realistic topologies should be used. These can be inferred from
Internet traces, such as the CAIDA Macroscopic Internet Topology Data
Kit (http://www.caida.org/data/active/internet-topology-data-kit) and
Rocketfuel
(http://www.cs.washington.edu/research/networking/rocketfuel). A
problem is the large size of the topology (approx. 45K ASes, close to
200K links), which may limit the scalability of the employed
evaluation tool. Katsaros et al. [ICNScale] address this problem by
using scaled down topologies created following the methodology
described in [COMPLEX].
Studies that focus on node or content mobility can benefit from
topologies and their dynamic aspects as used in the DTN community. As
mentioned in [RFC7476], DTN traces are available to be used in such
ICN evaluations.
As with host-centric topologies, defining just a node graph will not
be enough for most ICN studies. The experimenter 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 [Montage]. Real values
for such parameters can be taken from existing platforms such as
iPlane (http://iplane.cs.washington.edu). Synthetic values could be
produced with specific tools [DELAY].
2.2. Traffic Load
In this subsection we provide 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
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work, could be used to evaluate different ICN proposals, for
instance, on routing, congestion control, and performance, and can be
considered as other kinds of ICN contributions emerge. As we are
still lacking ICN-specific traffic workloads we can currently only
extrapolate from today's workloads. A significant challenge then
relates to the identification of the applications contributing to the
observed traffic (e.g., Web or peer-to-peer), as well as to the exact
amount of traffic they contribute to the overall traffic mixture.
Efforts in this direction can take heed from today's traffic mix
comprising web, peer-to-peer file sharing, 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://multiprobe.ewi.tudelft.nl/multiprobe.html,
http://an.kaist.ac.kr/traces/IMC2007.html, and
http://traces.cs.umass.edu/index.php/Network/Network.
Taking a more systematic approach, and with the purpose of modeling
the traffic load, we can resort to measurement studies that
investigate the composition of Internet traffic, such as [1][2]. In
[1] a large scale measurement study was performed, with the purpose
of studying the traffic crossing inter-domain links. The results
indicate the dominance of Web traffic, amounting to 52% over all
measured traffic. However, Deep Packet Inspection (DPI) techniques
reveal that 25-40% of all HTTP traffic actually carries video
traffic. Results from DPI techniques also reveal the difficulty in
correctly identifying the application type in the case of P2P
traffic: mapping observed port numbers to well-known applications
shows P2P traffic constituting only 0.85% of overall traffic, while
DPI raises this percentage to 18.32% [1]. Relevant studies on a
large ISP show the percentage of P2P traffic ranging from 17 to 19%
of overall traffic [2]. Table I provides an overview of these
figures. The "other" traffic type denotes traffic that cannot be
classified in any of the first three application categories, and
consists of unclassified traffic and traffic heavily fragmented into
several applications (e.g., 0.17% DNS traffic).
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Table I. Traffic type ratios of total traffic [1, 2]
Traffic Type | Ratio
=====================
Web | 31-39%
---------------------
P2P | 17-19%
---------------------
Video | 13-21%
---------------------
Other | 29-31%
=====================
The content catalog for each type of traffic can be characterized by
a specific set of parameters:
a) The cardinality of the estimated content catalog.
b) The size of the exchanged contents (either chunks or entire named
information objects).
c) The popularity of objects expressed in their request frequency.
In most application types, the popularity distribution follows some
power law, indicating that a small number of information items
trigger a large proportion of the entire set of requests. The exact
shape of the power law popularity distribution directly impacts the
performance of the underlying protocols. For instance, highly skewed
popularity distributions (e.g., a Zipf-like distribution with a high
slope value) favor the deployment of caching schemes, since caching a
very small set of information items can dramatically increase the
cache hit ratio.
Several studies in the past few 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 video
on demand (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
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where the sum is obtained considering all values of j, 1 <= j <= M.
A recent analysis of HTTP traffic showed that content popularity is
better reflected by a trimodal distribution model in which the head
and tail of a Zipf distribution (with slope value 0.84) are replaced
by two discrete Weibull distributions with shape parameter values 0.5
and 0.24 respectively [IMB2014].
A variation of the Zipf distribution, termed the Mandelbrot-Zipf
distribution was suggested [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.
Popularity can also be characterized in terms of:
a) The temporal dynamics of popularity, i.e., how requests are
distributed in time. The popularity distribution expresses the
number of requests submitted for each information item
participating into a certain workload. However, they do not
describe how these requests are distributed in time. This aspect
is of primary importance when considering the performance of
caching schemes since the ordering of the requests obviously
affects the contents of a cache. For example, with a Least
Frequently Used (LFU) cache replacement policy, if all requests
for a certain item are submitted close in time, the item is
unlikely to be evicted from the cache, even by a (globally) more
popular item whose requests are more evenly distributed in time.
The temporal ordering of requests gains even more importance when
considering workloads consisting of various applications, all
competing for the same cache space.
b) The spatial locality of popularity i.e., how requests are
distributed throughout a network. The importance of spatial
locality relates to the ability to avoid redundant traffic in the
network. If requests are highly localized in some area of the
entire network, then similar requests can be more efficiently
served with mechanisms such as caching and/or multicast i.e., the
concentration of similar requests in a limited area of the network
allows increasing the perceived cache hit ratios at caches in the
area and/or the traffic savings from the use of multicast. Table
II provides an overview of distributions that can be used to model
each of the identified traffic types i.e., Web, Video (based on
YouTube measurements) and P2P (based on BitTorrent measurements).
These distributions are the outcome of a series of modeling
efforts based on measurements of real traffic workloads
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[3][4][5][6][7][8][9][10][11][12][13]. A tool for the creation of
synthetic workloads following these models, and also allowing the
generation of different traffic mixes is described in [14].
Table II. Overview of traffic types models
| Object Size | Temporal Locality | Popularity Distribution
=====================================================================
Web | Concatenation | Ordering via LRU | Zipf: p(i)=K/i^a
| of Lognormal | stack model [5] | i: popularity rank
| (body) and | | N: total items
| Pareto (tail) | Exact timing via | K: 1/Sum(1/i^a)
| [7,8] | exponential | a: distribution slope
| | distribution [6] | values 0.64-0.84 [3][4]
---------------------------------------------------------------------
VoD | Duration/size: | No analytical models | Weibull: k=0.513,
| Concatenated | | lambda=6010
| normal, most | Random distribution |
| videos | across total | Gamma: k=0.372,
| ~330 kb/s [13] | duration | theta=23910 [12]
---------------------------------------------------------------------
P2P | Wide variation | Mean arrival rate of | Mandelbrot-Zipf [9]:
| on torrent | 0.9454 torrents/hour | p(i)=K/((i+q)/a)
| sizes [9]. | Peers in a swarm | q: plateau factor,
| No analytical | arrive as | 5 to 100.
| models exist: | l(t)= l0*e^(-t/tau) | Flatter head than in
| Sample a real | l0: initial arrival | Zipf-like distribution
| BitTorrent [11]| rate (87.74 average) | (where q=0)
| distribution | tau: object |
| or use fixed | popularity |
| value | (1.16 average)* [10] |
=====================================================================
* Random ordering of swarm births (first request). For each swarm
calculate a different tau. Based on average tau and object
popularity. Exponential decay rule for subsequent requests.
Table III 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.
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Table III. 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
(+HLS) | | ~1 kB (*) |
(+DASH) | | ~5.6 kB (*) |
==================================================================
UGC = User Generated Content VoD = Video on Demand
(*) Using adaptive video streaming (e.g., HLS and DASH), with an
optimal segment length (2 s for DASh and 10 s for HLS) and a bitrate
of 4500 kbps [W16][Led12]
2.3. Choosing Relevant Metrics
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
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metrics used in these approaches fall into two categories: metrics
for the approach as a whole, and metrics for individual components
(name resolution, routing, and so on). 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, we have tried to smooth over differences by
classifying similarly defined metrics under the same name. Also, due
to space constraints, we have chosen to report here only the most
common 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
IV. 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 do not 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 opted for higher-level metrics. Throughput
and download time seem to be the most popular metrics altogether.
Table IV. 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 | | |
==================================================================
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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 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
approach as a whole, all surveyed approaches also evaluate the
performance of individual components. Name resolution, request/data
routing, and data caching are the most typical components, as
summarized in Table V. 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 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 V. 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 would 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.
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2.3.1. Traffic 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 IP performance metrics (IPPM)
working group, 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 them for measuring ICN
performance. In addition, said metrics and methods work 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 its 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 (QoS) metrics, not at the output of the network
like with IPPM, but at the input to the application. For live video
streaming application 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
human perception under certain environments, but these methods are
typically intrusive. The most important drawback of measuring
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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 at this
stage.
2.3.2. 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 [RFC7476], 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 processing requirements, signaling
overhead, and memory allocation for caching procedures in addition to
power consumption and battery lifetime. For gateways, which
typically act as a point of service to a large number of nodes and
have to satisfy the information requests from remote entities we need
to consider scalability-related metrics, such as frequency and
processing of successfully satisfied information requests.
Finally, given the in-network caching functionality of ICNs,
efficiency and performance metrics 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 cache of this router.
The output of the model essentially reflects the probability that the
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CoI will be found in a given cache. An initial study [L9] is applied
to the CCN/NDN framework, where contents get cached at every node
they traverse. 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.
2.4. 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 data 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
and the corresponding communication paradigm results in changes to
many aspects of network security. These will affect all scenarios
described in [RFC7476]. Additional evaluation will be required to
ensure relevant security requirements are appropriately met by the
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implementation of the chosen architecture in the various scenarios.
The ICN security aspects described in this document is complementing
the ICN security challenges outlined in the "ICN Research Challenges"
document [ICNCHA]. [***EDITORS NOTE: The referenced document is
expected to have become an RFC by the time this document is
published, this reference should thus be updated before publication
of this document.***]
The ICN architectures 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.
For Example, a roadmap for improving the security model in NetInf can
be found in [NETINFSC]. As secure communications on the Internet are
becoming the norm, major gaps in ICN security aspects are bound to
undermine the adoption of ICN.
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.
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All 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 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 information-centric network 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 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).
DONA (Data Oriented Network Architecture) [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.
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
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application to decide on provenance validation if required. For
dynamic content NetInf can use e.g. signed manifests. For more
details on NetInf security see [SCNETINF].
3.2. Authorization, Access Control and Logging
A potentially major concern for all ICN architectures considered here
is that they do not provide any inbuilt support for an authorization
framework or for logging. 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]. This scheme allows
semi-trusted entities (such as caches, CDN nodes) to delegate access
control decisions to 3rd party access control providers that are
trusted by the content publisher. The former entities have no access
to subscriber related information and should respect the decisions of
the access control providers.
A recent proposal for an extra layer in the protocol stack [LIRA]
gives control of the name resolution infrastructure to the publisher.
This enables access logging as well some degree of active cache
management, e.g., purging of stale content.
One possible technique that could allow for providing access control
to heterogeneous groups and still allow for a single encrypted object
representation that remains cacheable is Attribute Based Encryption
(ABE). A first proposal for this is presented in [ABE]. To support
heterogeneous group and avoid having a single authority that has a
master key multi authority ABE can be used [LEWKO].
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
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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. However it should be noted that it is only the
first hop router/cache that can see who request what, this as
requests are aggregated and only the previous hop router is visible
when a request is forwarded.
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 [CCNSEC] and
the analysis is applicable to all 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 privacy in ICNs can be found in [CONPRV]. Finally, Fotiou et
al. define an ICN privacy evaluation framework in [PRIFRA].
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. [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 4.3.
o More powerful routers upgraded to handle persistent caching
increase the network's attack surface. This is particularly the
case in systems 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
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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.
Appropriate mitigation of these threats will need to be considered in
each scenario.
4. Evaluation Tools
Since ICN is an emerging area, the community is in the process of
developing effective evaluation environments, including releasing
open-source implementations, simulators, emulators, and testbeds. To
date, none of the available evaluation tools can be seen as the one
and only community reference evaluation tool. Furthermore, no single
environment supports all well-known ICN approaches, as we describe
below, hindering the direct comparison of the results obtained for
different ICN approaches. The rest of this subsection reviews the
publicly available ICN implementations, simulators and experimental
facilities currently available to the community.
An updated version of the information in this section will be
maintained at the ICNRG Wiki page:
https://trac.tools.ietf.org/group/irtf/trac/wiki/IcnEvaluationAndTestbeds
4.1. Open-source Implementations
The Named Data Networking (NDN) project has open-sourced a software
reference implementation of the architecture and protocol called NDN
(http://http://named-data.net). NDN is available for deployment on
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various operating systems and includes C and Java libraries that can
be used to build applications.
CCN-lite (http://www.ccn-lite.net) is a lightweight implementation of
the CCN protocol that supports most of the key features of CCNx and
is interoperable with CCNx. CCN-lite implements the core CCN logic
in about 1000 lines of code, so it is ideal for classroom work and
course projects as well as for quickly experimenting with CCN
extensions. For example, Baccelli et al. use CCN-lite on top of the
RIOT operating system to conduct experiments over an IoT (Internet of
Things) testbed [NDNIOT].
PARC is offering CCN source code under various licensing schemes,
please see http://www.ccnx.org for details.
The PURSUIT project (http://www.fp7-pursuit.eu) has open-sourced its
Blackhawk publish-subscribe (Pub/Sub) implementation for Linux and
Android; more details are available at https://github.com/fp7-
pursuit/blackadder. Blackadder uses the Click modular router for
ease of development. The code distribution features a set of tools,
test applications and scripts. The POINT project (http://www.point-
h2020.eu/) is currently maintaining Blackadder.
The 4WARD and SAIL projects have open-sourced software that
implements different aspects of NetInf, e.g., NetInf URI format, HTTP
and UDP convergence layer, using different programming languages.
The Java implementation provides a local caching proxy and client.
Further, an OpenNetInf prototype is available as well as a hybrid
host-centric and information-centric network architecture called the
Global Information Network (GIN), a browser plug-in and video
streaming software. See http://www.netinf.org/open-source for more
details.
4.2. Simulators and Emulators
Simulators and emulators should be able to capture faithfully all
features and operations of the respective ICN architecture(s) and any
limitations should be openly documented. It is 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 medium- 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.
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4.2.1. ndnSIM
The Named Data Networking (NDN) project (http://named-data.net/) has
developed ndnSIM [ndnSIM][ndnSIM2]; this is a module that can be
plugged into the ns-3 simulator (https://www.nsnam.org/) and supports
the core features of NDN. One can use ndnSIM to experiment with
various NDN applications and services as well as components developed
for NDN such as routing protocols, caching and forwarding strategies
among others. 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://ndnsim.net/2.0/.
4.2.2. ccnSIM
ccnSim [ccnSim] is CCN-specific simulator that was specially designed
to handle forwarding of a large number of CCN-chunks
(http://www.infres.enst.fr/~drossi/index.php?n=Software.ccnSim).
ccnSim is written in C++ for the OMNeT++ simulation framework
(https://omnetpp.org/). Other CCN-specific simulators include CCN
Packet Level Simulator [CCNPL] and CCN-Joker [CCNj]. 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. Finally, Cabral et al. [MiniCCNx] use container-
based emulation and resource isolation techniques to develop a
prototyping and emulation tool.
4.2.3. Icarus simulator
The Icarus simulator focuses on caching in ICN and is agnostic with
respect to any particular ICN implementation. The simulator is
implemented in Python, uses the Fast Network Simulator Setup tool
[FNSS], and is available at http://icarus-sim.github.io/. Icarus has
several caching strategies implemented, including among others
ProbCache [PROBCACH], node-centrality-based caching [CL4M] and hash-
route-based caching [HASHROUT].
ProbCache [PROBCACH] is taking a resource management view on caching
decisions and approximates the available cache capacity along the
path from source to destination. Based on this approximation and in
order to reduce caching redundancy across the path, it caches content
probabilistically. According to [CL4M] the node with the highest
betweenness centrality along the path from source to destination is
responsible for caching incoming content. Finally, [HASHROUT]
calculates the hash-function of a content's name and assigns contents
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to caches of a domain according to that. The hash-space is split
according to the number of caches of the network. Then upon
subsequent requests, and based again on the hash of the name included
in the request, edge routers redirect requests to the cache assigned
with the corresponding hash-space. [HASHROUT] is an off-path caching
strategy, in contrast to [PROBCACH] and [CL4M], which however,
requires minimum co-ordination and redirection overhead. In its
latest update, Icarus also includes implementation of the "Satisfied
Interest Table" (SIT) [SIT]. The SIT table is pointing towards the
direction where content has been sent recently. Among other benefits,
this enables information resilience in case of network fragmentation
(i.e., content can still be found in neighbour caches or in users'
devices) and inherently supports user-assisted caching (i.e., P2P-
like content distribution).
The simulator is constantly being updated and more strategies are
added in each update. Tortelli et al. [ICNSIMS] provide a comparison
of ndnSIM, ccnSim, and Icarus.
4.3. Experimental Facilities
An important consideration in the evaluation of any kind of future
Internet mechanism lies in the characteristics of that evaluation
itself. 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.
Different experimental facilities have different characteristics and
capabilities, e.g. low cost of use, reproducible configuration, easy-
to-use tools, sharable, available background traffic.
4.3.1. Open Network Lab [ONL]
An example of an experimental facility that supports CCN is the Open
Network Lab [ONL] that 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 setup as per the
experiment requirements, and also serves as a control mechanism,
allowing access to various control variables and traffic counters.
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Further, it is also possible to run and evaluate CCN over popular
testbeds [PLANET] [EMULAB] [DETERLAB] [OFELIA] by directly running,
for example, the CCNx open-source code [CCNOFELI] [ICNGRID] [CCNPL]
[CCNOSN]. Also, the Network Experimentation Programming Interface
[NEPI] is a tool developed for controlling and managing large-scale
network experiments. NEPI can be used to control and manage large-
scale CCNx experiments e.g., on PlanetLab [NEPIICN].
4.3.2. POINT testbed
The POINT project is maintaining a testbed with 40 machines across
Europe, North America (MIT) and Japan (NICT) interconnected in a
topology containing one Topology Manager and one Rendezvous node that
handle all publish/subscribe and topology formation requests [IEICE].
All machines run Blackadder. New nodes can join and experiments can
be run on request.
4.3.3. CUTEi: Container-based ICN testbed
NICT has also developed a testbed used for ICN experiments [AFI]
comprising multiple servers located in Asia and other locations.
Each testbed server (or VM) utilizes a Linux kernel-based container
(LXC) for node virtualization. This testbed enables users to run
applications and protocols for ICN in two experimentation modes using
two different container designs:
1. application-level experimentation using a "common container" and
2. network-level experimentation using a "user container."
A common container is shared by all testbed users, and a user
container is assigned to one testbed user. A common container has a
global IP address to connect with other containers or external
networks, whereas each user container uses a private IP address and a
user space providing a closed networking environment. A user can
login to his/her user containers using SSH with his/her certificate,
or access them from PCs connected to the Internet using SSH
tunnelling.
This testbed also implements an "on-filesystem cache" to allocate
caching data on a UNIX filesystem. The on-filesystem cache system
accommodates two kinds of caches: "individual cache" and "shared
cache." Individual cache is accessible for one dedicated router for
the individual user, while shared cache is accessible for a set of
routers in the same group to avoid duplicated caching in the
neighborhood for cooperative caching.
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5. Security Considerations
This document does not impact the security of the Internet.
6. IANA Considerations
This document presents no IANA considerations.
7. Acknowledgments
Konstantinos Katsaros contributed the updated text of Section 2.2
along with an extensive set of references.
Priya Mahadevan, 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, E. Baccelli, Claudia Campolo, Christian Esteve Rothenberg,
Suyong Eum, Nikos Fotiou, Dorothy Gellert, Luigi Alfredo Grieco,
Myeong-Wuk Jang, Ren Jing, Will Liu, Antonella Molinaro, Luca
Muscariello, Ioannis Psaras, Dario Rossi, Stefano Salsano, Damien
Saucez, Dirk Trossen, Jianping Wang, Yuanzhe Xuan, and Xinwen Zhang.
The IRSG review was provided by Aaron Falk.
8. Informative References
[RFC2330] Paxson, V., Almes, G., Mahdavi, J., and M. Mathis,
"Framework for IP Performance Metrics", RFC 2330, May
1998.
[RFC5743] Falk, A., "Definition of an Internet Research Task Force
(IRTF) Document Stream", RFC 5743, December 2009.
[RFC6920] Farrell, S., Kutscher, D., Dannewitz, C., Ohlman, B.,
Keranen, A., and P. Hallam-Baker, "Naming Things with
Hashes", RFC 6920, April 2013.
[RFC7476] Pentikousis, K., Ohlman, B., Corujo, D., Boggia, G.,
Tyson, G., Davies, E., Molinaro, A., and S. Eum,
"Information-Centric Networking: Baseline Scenarios ", RFC
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INTERNET DRAFT Evaluation and Security Considerations April 12, 2016
7476, March 2015.
[ICNCHA] Internet draft, "ICN Research Challenges" draft-irtf-
icnrg-challenges-04, [***EDITORS NOTE: This reference is
expected to have become an RFC by the time this document
is published, this reference should thus be updated before
publication of this document.***]
[ndnSIM] Afanasyev, A. et al., "ndnSIM: NDN simulator for NS-3",
NDN Technical Report NDN-0005, Revision 2, October 2012.
[ndnSIM2] Mastorakis, S. et al., "ndnSIM 2.0: A new version of the
NDN simulator for NS-3", NDN Technical Report NDN-0028,
Revision 1, January 2015.
[ccnSim] Rossini, G. and D. Rossi, "Large scale simulation of CCN
networks", Proc. Algotel 2012 , La Grande Motte, France,
May 2012.
[CCNPL] Muscariello, L., "Content centric networking packet level
simulator", available online at
http://perso.rd.francetelecom.fr/muscariello/sim.html
[CCNj] Cianci, I. et al. "CCN - Java Opensource Kit EmulatoR for
Wireless Ad Hoc Networks", Proc. 7th ACM Int. Conf. on
Future Internet Technologies, Seoul, Korea, Sept., 2012.
[IEICE] G. Parisis, D. Trossen, and H. Asaeda, "A Node Design and
a Framework for Development and Experimentation for an
Information-Centric Network", IEICE Trans. Commun., vol.
E96-B, no. 7, pp.1650-1660, July 2013.
[PROBCACH] I. Psaras, W. Chai, G. Pavlou, "Probabilistic In-Network
Caching for Information-Centric Networks", Proc. SIGCOMM
ICN Workshop. ACM, 2012.
[CL4M] Chai, W. K. et al., "Cache 'Less for More' in Information-
centric Networks", Proc. Networking. IFIP, 2012.
[SIT] Vasilis Sourlas, Leandros Tassiulas, Ioannis Psaras and
George Pavlou, "Information Resilience through User-
Assisted Caching in Disruptive Content-Centric Networks"
14th IFIP NETWORKING, Toulouse, France, May 2015.
[HASHROUT] L. Saino, I. Psaras, G. Pavlou, "Hash-routing Schemes for
Information-Centric Networking", Proc. SIGCOMM ICN
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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-telecom.com
Gennaro Boggia
Dep. of Electrical and Information Engineering
Politecnico di Bari
Via Orabona 4
Pentikousis, et al. Expires October 14, 2016 [Page 34]
INTERNET DRAFT Evaluation and Security Considerations April 12, 2016
70125 Bari
Italy
Email: g.boggia@poliba.it
Pentikousis, et al. Expires October 14, 2016 [Page 35]
