94,772 research outputs found
Context-Awareness Enhances 5G Multi-Access Edge Computing Reliability
The fifth generation (5G) mobile telecommunication network is expected to
support Multi- Access Edge Computing (MEC), which intends to distribute
computation tasks and services from the central cloud to the edge clouds.
Towards ultra-responsive, ultra-reliable and ultra-low-latency MEC services,
the current mobile network security architecture should enable a more
decentralized approach for authentication and authorization processes. This
paper proposes a novel decentralized authentication architecture that supports
flexible and low-cost local authentication with the awareness of context
information of network elements such as user equipment and virtual network
functions. Based on a Markov model for backhaul link quality, as well as a
random walk mobility model with mixed mobility classes and traffic scenarios,
numerical simulations have demonstrated that the proposed approach is able to
achieve a flexible balance between the network operating cost and the MEC
reliability.Comment: Accepted by IEEE Access on Feb. 02, 201
D2D Communications for Large-Scale Fog Platforms: Enabling Direct M2M Interactions
To many, fog computing is considered the next step beyond the current centralized cloud that will support the forthcoming Internet of Things (IoT) revolution. While IoT devices will still communicate with applications running in the cloud, localized fog clusters will appear with IoT devices communicating with application logic running on a proximate fog node. This will add proximity-based machine-to-machine (M2M) communications to standard cloud-computing traffic, and it calls for efficient mobility management for entire fog clusters and energy-efficient communication within them. In this context, long-term evolution-advanced (LTE-A) technology is expected to play a major role as a communication infrastructure that guarantees low deployment costs, native mobility support, and plug-and-play seamless configuration.
We investigate the role of LTE-A in future large-scale IoT systems. In particular, we analyze how the recently
standardized device-to-device (D2D) communication mode can be exploited to effectively enable direct M2M
interactions within fog clusters, and we assess the expected benefits in terms of network resources and
energy consumption. Moreover, we show how the fog-cluster architecture, and its localized-communication
paradigm, can be leveraged to devise enhanced mobility management, building on what LTE-A already has to offer
Echo State Networks for Proactive Caching in Cloud-Based Radio Access Networks with Mobile Users
In this paper, the problem of proactive caching is studied for cloud radio
access networks (CRANs). In the studied model, the baseband units (BBUs) can
predict the content request distribution and mobility pattern of each user,
determine which content to cache at remote radio heads and BBUs. This problem
is formulated as an optimization problem which jointly incorporates backhaul
and fronthaul loads and content caching. To solve this problem, an algorithm
that combines the machine learning framework of echo state networks with
sublinear algorithms is proposed. Using echo state networks (ESNs), the BBUs
can predict each user's content request distribution and mobility pattern while
having only limited information on the network's and user's state. In order to
predict each user's periodic mobility pattern with minimal complexity, the
memory capacity of the corresponding ESN is derived for a periodic input. This
memory capacity is shown to be able to record the maximum amount of user
information for the proposed ESN model. Then, a sublinear algorithm is proposed
to determine which content to cache while using limited content request
distribution samples. Simulation results using real data from Youku and the
Beijing University of Posts and Telecommunications show that the proposed
approach yields significant gains, in terms of sum effective capacity, that
reach up to 27.8% and 30.7%, respectively, compared to random caching with
clustering and random caching without clustering algorithm.Comment: Accepted in the IEEE Transactions on Wireless Communication
A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing
Edge computing is promoted to meet increasing performance needs of
data-driven services using computational and storage resources close to the end
devices, at the edge of the current network. To achieve higher performance in
this new paradigm one has to consider how to combine the efficiency of resource
usage at all three layers of architecture: end devices, edge devices, and the
cloud. While cloud capacity is elastically extendable, end devices and edge
devices are to various degrees resource-constrained. Hence, an efficient
resource management is essential to make edge computing a reality. In this
work, we first present terminology and architectures to characterize current
works within the field of edge computing. Then, we review a wide range of
recent articles and categorize relevant aspects in terms of 4 perspectives:
resource type, resource management objective, resource location, and resource
use. This taxonomy and the ensuing analysis is used to identify some gaps in
the existing research. Among several research gaps, we found that research is
less prevalent on data, storage, and energy as a resource, and less extensive
towards the estimation, discovery and sharing objectives. As for resource
types, the most well-studied resources are computation and communication
resources. Our analysis shows that resource management at the edge requires a
deeper understanding of how methods applied at different levels and geared
towards different resource types interact. Specifically, the impact of mobility
and collaboration schemes requiring incentives are expected to be different in
edge architectures compared to the classic cloud solutions. Finally, we find
that fewer works are dedicated to the study of non-functional properties or to
quantifying the footprint of resource management techniques, including
edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless
Communications and Mobile Computing journa
Addressing the Challenges in Federating Edge Resources
This book chapter considers how Edge deployments can be brought to bear in a
global context by federating them across multiple geographic regions to create
a global Edge-based fabric that decentralizes data center computation. This is
currently impractical, not only because of technical challenges, but is also
shrouded by social, legal and geopolitical issues. In this chapter, we discuss
two key challenges - networking and management in federating Edge deployments.
Additionally, we consider resource and modeling challenges that will need to be
addressed for a federated Edge.Comment: Book Chapter accepted to the Fog and Edge Computing: Principles and
Paradigms; Editors Buyya, Sriram
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