12,862 research outputs found
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
QuLa: service selection and forwarding table population in service-centric networking using real-life topologies
The amount of services located in the network has drastically increased over the last decade which is why more and more datacenters are located at the network edge, closer to the users. In the current Internet it is up to the client to select a destination using a resolution service (Domain Name System, Content Delivery Networks ...). In the last few years, research on Information-Centric Networking (ICN) suggests to put this selection responsibility at the network components; routers find the closest copy of a content object using the content name as input.
We extend the principle of ICN to services; service routers forward requests to service instances located in datacenters spread across the network edge. To solve this problem, we first present a service selection algorithm based on both server and network metrics. Next, we describe a method to reduce the state required in service routers while minimizing the performance loss caused by this data reduction. Simulation results based on real-life networks show that we are able to find a near-optimal load distribution with only minimal state required in the service routers
Internames: a name-to-name principle for the future Internet
We propose Internames, an architectural framework in which names are used to
identify all entities involved in communication: contents, users, devices,
logical as well as physical points involved in the communication, and services.
By not having a static binding between the name of a communication entity and
its current location, we allow entities to be mobile, enable them to be reached
by any of a number of basic communication primitives, enable communication to
span networks with different technologies and allow for disconnected operation.
Furthermore, with the ability to communicate between names, the communication
path can be dynamically bound to any of a number of end-points, and the
end-points themselves could change as needed. A key benefit of our architecture
is its ability to accommodate gradual migration from the current IP
infrastructure to a future that may be a ubiquitous Information Centric
Network. Basic building blocks of Internames are: i) a name-based Application
Programming Interface; ii) a separation of identifiers (names) and locators;
iii) a powerful Name Resolution Service (NRS) that dynamically maps names to
locators, as a function of time/location/context/service; iv) a built-in
capacity of evolution, allowing a transparent migration from current networks
and the ability to include as particular cases current specific architectures.
To achieve this vision, shared by many other researchers, we exploit and expand
on Information Centric Networking principles, extending ICN functionality
beyond content retrieval, easing send-to-name and push services, and allowing
to use names also to route data in the return path. A key role in this
architecture is played by the NRS, which allows for the co-existence of
multiple network "realms", including current IP and non-IP networks, glued
together by a name-to-name overarching communication primitive.Comment: 6 page
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
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