4,910 research outputs found
Performance Evaluation of Caching Policies in NDN - an ICN Architecture
Information Centric Networking (ICN) advocates the philosophy of accessing
the content independent of its location. Owing to this location independence in
ICN, the routers en-route can be enabled to cache the content to serve the
future requests for the same content locally. Several ICN architectures have
been proposed in the literature along with various caching algorithms for
caching and cache replacement at the routers en-route. The aim of this paper is
to critically evaluate various caching policies using Named Data Networking
(NDN), an ICN architecture proposed in literature. We have presented the
performance comparison of different caching policies naming First In First Out
(FIFO), Least Recently Used (LRU), and Universal Caching (UC) in two network
models; Watts-Strogatz (WS) model (suitable for dense short link networks such
as sensor networks) and Sprint topology (better suited for large Internet
Service Provider (ISP) networks) using ndnSIM, an ns3 based discrete event
simulator for NDN architecture. Our results indicate that UC outperforms other
caching policies such as LRU and FIFO and makes UC a better alternative for
both sensor networks and ISP networks
A Content-based Centrality Metric for Collaborative Caching in Information-Centric Fogs
Information-Centric Fog Computing enables a multitude of nodes near the
end-users to provide storage, communication, and computing, rather than in the
cloud. In a fog network, nodes connect with each other directly to get content
locally whenever possible. As the topology of the network directly influences
the nodes' connectivity, there has been some work to compute the graph
centrality of each node within that network topology. The centrality is then
used to distinguish nodes in the fog network, or to prioritize some nodes over
others to participate in the caching fog. We argue that, for an
Information-Centric Fog Computing approach, graph centrality is not an
appropriate metric. Indeed, a node with low connectivity that caches a lot of
content may provide a very valuable role in the network.
To capture this, we introduce acontent-based centrality (CBC) metric which
takes into account how well a node is connected to the content the network is
delivering, rather than to the other nodes in the network. To illustrate the
validity of considering content-based centrality, we use this new metric for a
collaborative caching algorithm. We compare the performance of the proposed
collaborative caching with typical centrality based, non-centrality based, and
non-collaborative caching mechanisms. Our simulation implements CBC on three
instances of large scale realistic network topology comprising 2,896 nodes with
three content replication levels. Results shows that CBC outperforms benchmark
caching schemes and yields a roughly 3x improvement for the average cache hit
rate
Content Delivery Latency of Caching Strategies for Information-Centric IoT
In-network caching is a central aspect of Information-Centric Networking
(ICN). It enables the rapid distribution of content across the network,
alleviating strain on content producers and reducing content delivery
latencies. ICN has emerged as a promising candidate for use in the Internet of
Things (IoT). However, IoT devices operate under severe constraints, most
notably limited memory. This means that nodes cannot indiscriminately cache all
content; instead, there is a need for a caching strategy that decides what
content to cache. Furthermore, many applications in the IoT space are
timesensitive; therefore, finding a caching strategy that minimises the latency
between content request and delivery is desirable. In this paper, we evaluate a
number of ICN caching strategies in regards to latency and hop count reduction
using IoT devices in a physical testbed. We find that the topology of the
network, and thus the routing algorithm used to generate forwarding
information, has a significant impact on the performance of a given caching
strategy. To the best of our knowledge, this is the first study that focuses on
latency effects in ICN-IoT caching while using real IoT hardware, and the first
to explicitly discuss the link between routing algorithm, network topology, and
caching effects.Comment: 10 pages, 9 figures, journal pape
Fog-enabled Edge Learning for Cognitive Content-Centric Networking in 5G
By caching content at network edges close to the users, the content-centric
networking (CCN) has been considered to enforce efficient content retrieval and
distribution in the fifth generation (5G) networks. Due to the volume,
velocity, and variety of data generated by various 5G users, an urgent and
strategic issue is how to elevate the cognitive ability of the CCN to realize
context-awareness, timely response, and traffic offloading for 5G applications.
In this article, we envision that the fundamental work of designing a cognitive
CCN (C-CCN) for the upcoming 5G is exploiting the fog computing to
associatively learn and control the states of edge devices (such as phones,
vehicles, and base stations) and in-network resources (computing, networking,
and caching). Moreover, we propose a fog-enabled edge learning (FEL) framework
for C-CCN in 5G, which can aggregate the idle computing resources of the
neighbouring edge devices into virtual fogs to afford the heavy delay-sensitive
learning tasks. By leveraging artificial intelligence (AI) to jointly
processing sensed environmental data, dealing with the massive content
statistics, and enforcing the mobility control at network edges, the FEL makes
it possible for mobile users to cognitively share their data over the C-CCN in
5G. To validate the feasibility of proposed framework, we design two
FEL-advanced cognitive services for C-CCN in 5G: 1) personalized network
acceleration, 2) enhanced mobility management. Simultaneously, we present the
simulations to show the FEL's efficiency on serving for the mobile users'
delay-sensitive content retrieval and distribution in 5G.Comment: Submitted to IEEE Communications Magzine, under review, Feb. 09, 201
ADN: An Information-Centric Networking Architecture for the Internet of Things
Forwarding data by name has been assumed to be a necessary aspect of an
information-centric redesign of the current Internet architecture that makes
content access, dissemination, and storage more efficient. The Named Data
Networking (NDN) and Content-Centric Networking (CCNx) architectures are the
leading examples of such an approach. However, forwarding data by name incurs
storage and communication complexities that are orders of magnitude larger than
solutions based on forwarding data using addresses. Furthermore, the specific
algorithms used in NDN and CCNx have been shown to have a number of
limitations. The Addressable Data Networking (ADN) architecture is introduced
as an alternative to NDN and CCNx. ADN is particularly attractive for
large-scale deployments of the Internet of Things (IoT), because it requires
far less storage and processing in relaying nodes than NDN. ADN allows things
and data to be denoted by names, just like NDN and CCNx do. However, instead of
replacing the waist of the Internet with named-data forwarding, ADN uses an
address-based forwarding plane and introduces an information plane that
seamlessly maps names to addresses without the involvement of end-user
applications. Simulation results illustrate the order of magnitude savings in
complexity that can be attained with ADN compared to NDN.Comment: 10 page
Offloading Content with Self-organizing Mobile Fogs
Mobile users in an urban environment access content on the internet from
different locations. It is challenging for the current service providers to
cope with the increasing content demand from a large number of collocated
mobile users. In-network caching to offload content at nodes closer to users
alleviate the issue, though efficient cache management is required to find out
who should cache what, when and where in an urban environment, given nodes
limited computing, communication and caching resources. To address this, we
first define a novel relation between content popularity and availability in
the network and investigate a node's eligibility to cache content based on its
urban reachability. We then allow nodes to self-organize into mobile fogs to
increase the distributed cache and maximize content availability in a
cost-effective manner. However, to cater rational nodes, we propose a coalition
game for the nodes to offer a maximum "virtual cache" assuming a monetary
reward is paid to them by the service/content provider. Nodes are allowed to
merge into different spatio-temporal coalitions in order to increase the
distributed cache size at the network edge. Results obtained through
simulations using realistic urban mobility trace validate the performance of
our caching system showing a ratio of 60-85% of cache hits compared to the
30-40% obtained by the existing schemes and 10% in case of no coalition
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