4 research outputs found
Joint Optimization of Signal Design and Resource Allocation in Wireless D2D Edge Computing
In this paper, we study the distributed computational capabilities of
device-to-device (D2D) networks. A key characteristic of D2D networks is that
their topologies are reconfigurable to cope with network demands. For
distributed computing, resource management is challenging due to limited
network and communication resources, leading to inter-channel interference. To
overcome this, recent research has addressed the problems of wireless
scheduling, subchannel allocation, power allocation, and multiple-input
multiple-output (MIMO) signal design, but has not considered them jointly. In
this paper, unlike previous mobile edge computing (MEC) approaches, we propose
a joint optimization of wireless MIMO signal design and network resource
allocation to maximize energy efficiency. Given that the resulting problem is a
non-convex mixed integer program (MIP) which is prohibitive to solve at scale,
we decompose its solution into two parts: (i) a resource allocation subproblem,
which optimizes the link selection and subchannel allocations, and (ii) MIMO
signal design subproblem, which optimizes the transmit beamformer, transmit
power, and receive combiner. Simulation results using wireless edge topologies
show that our method yields substantial improvements in energy efficiency
compared with cases of no offloading and partially optimized methods and that
the efficiency scales well with the size of the network.Comment: 10 pages, 7 figures, Accepted by INFOCOM 202
Joint Data compression and Computation offloading in Hierarchical Fog-Cloud Systems
Data compression has the potential to significantly improve the computation
offloading performance in hierarchical fog-cloud systems. However, it remains
unknown how to optimally determine the compression ratio jointly with the
computation offloading decisions and the resource allocation. This joint
optimization problem is studied in the current paper where we aim to minimize
the maximum weighted energy and service delay cost (WEDC) of all users. First,
we consider a scenario where data compression is performed only at the mobile
users. We prove that the optimal offloading decisions have a threshold
structure. Moreover, a novel three-step approach employing convexification
techniques is developed to optimize the compression ratios and the resource
allocation. Then, we address the more general design where data compression is
performed at both the mobile users and the fog server. We propose three
efficient algorithms to overcome the strong coupling between the offloading
decisions and resource allocation. We show that the proposed optimal algorithm
for data compression at only the mobile users can reduce the WEDC by a few
hundred percent compared to computation offloading strategies that do not
leverage data compression or use sub-optimal optimization approaches. Besides,
the proposed algorithms for additional data compression at the fog server can
further reduce the WEDC