11 research outputs found
Leveraging Intelligent Computation Offloading with Fog/Edge Computing for Tactile Internet: Advantages and Limitations
[EN] With the recent advancement in wireless communication and networks, we are at the doorstep of the Tactile Internet. The Tactile Internet aims to enable the skill delivery and thereafter democratize the specialized skills for many emerging applications (e.g., remote medical, industrial machinery, remote robotics, autonomous driving). In this article, we start with the motivation of applying intelligent edge computing for computation offloading in the Tactile Internet. Afterward, we outline the main research challenges to leverage edge intelligence at the master, network, and controlled domain of the Tactile Internet. The key research challenges in the Tactile Internet lie in its stringent requirements such as ultra-low latency, ultra-high reliability, and almost zero service outage. We also discuss major entities in intelligent edge computing and their role in the Tactile Internet. Finally, several potential research challenges in edge intelligence for the Tactile Internet are highlighted.This work was supported in part by the National Natural Science Foundation of China under Grant 61901128, and Agile Edge Intelligence for Delay Sensitive IoT (AgilE-IoT) project (Grant No. 9131-00119B) of Independent Research Fund Denmark (DFF).Mukherjee, M.; Guo, M.; Lloret, J.; Zhang, Q. (2020). Leveraging Intelligent Computation Offloading with Fog/Edge Computing for Tactile Internet: Advantages and Limitations. IEEE Network. 34(5):322-329. https://doi.org/10.1109/MNET.001.200000432232934
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 Resource Allocation and Cache Placement for Location-Aware Multi-User Mobile Edge Computing
With the growing demand for latency-critical and computation-intensive
Internet of Things (IoT) services, mobile edge computing (MEC) has emerged as a
promising technique to reinforce the computation capability of the
resource-constrained mobile devices. To exploit the cloud-like functions at the
network edge, service caching has been implemented to (partially) reuse the
computation tasks, thus effectively reducing the delay incurred by data
retransmissions and/or the computation burden due to repeated execution of the
same task. In a multiuser cache-assisted MEC system, designs for service
caching depend on users' preference for different types of services, which is
at times highly correlated to the locations where the requests are made. In
this paper, we exploit users' location-dependent service preference profiles to
formulate a cache placement optimization problem in a multiuser MEC system.
Specifically, we consider multiple representative locations, where users at the
same location share the same preference profile for a given set of services. In
a frequency-division multiple access (FDMA) setup, we jointly optimize the
binary cache placement, edge computation resources and bandwidth allocation to
minimize the expected weighted-sum energy of the edge server and the users with
respect to the users' preference profile, subject to the bandwidth and the
computation limitations, and the latency constraints. To effectively solve the
mixed-integer non-convex problem, we propose a deep learning based offline
cache placement scheme using a novel stochastic quantization based
discrete-action generation method. In special cases, we also attain suboptimal
caching decisions with low complexity leveraging the structure of the optimal
solution. The simulations verify the performance of the proposed scheme and the
effectiveness of service caching in general.Comment: 32 pages, 9 figures, submitted for possible journal publicatio
Joint task assignment and resource allocation for D2D-enabled mobile-edge computing
202404 bckwAuthor’s OriginalOthersNational Natural Science Foundation of China; Education Department of Guangdong Province; National Mobile Communications Research Laboratory, Southeast UniversityPublishedGreen (AO