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Joint Communication, Computation, Caching, and Control in Big Data Multi-access Edge Computing
The concept of multi-access edge computing (MEC) has been recently introduced
to supplement cloud computing by deploying MEC servers to the network edge so
as to reduce the network delay and alleviate the load on cloud data centers.
However, compared to a resourceful cloud, an MEC server has limited resources.
When each MEC server operates independently, it cannot handle all of the
computational and big data demands stemming from the users devices.
Consequently, the MEC server cannot provide significant gains in overhead
reduction due to data exchange between users devices and remote cloud.
Therefore, joint computing, caching, communication, and control (4C) at the
edge with MEC server collaboration is strongly needed for big data
applications. In order to address these challenges, in this paper, the problem
of joint 4C in big data MEC is formulated as an optimization problem whose goal
is to maximize the bandwidth saving while minimizing delay, subject to the
local computation capability of user devices, computation deadline, and MEC
resource constraints. However, the formulated problem is shown to be
non-convex. To make this problem convex, a proximal upper bound problem of the
original formulated problem that guarantees descent to the original problem is
proposed. To solve the proximal upper bound problem, a block successive upper
bound minimization (BSUM) method is applied. Simulation results show that the
proposed approach increases bandwidth-saving and minimizes delay while
satisfying the computation deadlines
The edge cloud: A holistic view of communication, computation and caching
The evolution of communication networks shows a clear shift of focus from
just improving the communications aspects to enabling new important services,
from Industry 4.0 to automated driving, virtual/augmented reality, Internet of
Things (IoT), and so on. This trend is evident in the roadmap planned for the
deployment of the fifth generation (5G) communication networks. This ambitious
goal requires a paradigm shift towards a vision that looks at communication,
computation and caching (3C) resources as three components of a single holistic
system. The further step is to bring these 3C resources closer to the mobile
user, at the edge of the network, to enable very low latency and high
reliability services. The scope of this chapter is to show that signal
processing techniques can play a key role in this new vision. In particular, we
motivate the joint optimization of 3C resources. Then we show how graph-based
representations can play a key role in building effective learning methods and
devising innovative resource allocation techniques.Comment: to appear in the book "Cooperative and Graph Signal Pocessing:
Principles and Applications", P. Djuric and C. Richard Eds., Academic Press,
Elsevier, 201
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