5 research outputs found
Energy Minimization for Mobile Edge Computing Networks with Time-Sensitive Constraints
Mobile edge computing (MEC) provides users with a high quality experience
(QoE) by placing servers with rich services close to the end users. Compared
with local computing, MEC can contribute to energy saving, but results in
increased communication latency. In this paper, we jointly optimize task
offloading and resource allocation to minimize the energy consumption in an
orthogonal frequency division multiple access (OFDMA)-based MEC networks, where
the time-sensitive tasks can be processed at both local users and MEC server
via partial offloading. Since the optimization variables of the problem are
strongly coupled, we first decompose the original problem into two subproblems
named as offloading selection (PO), and subcarriers and computing resource
allocation (PS), and then propose an iterative algorithm to deal with them in a
sequence. To be specific, we derive the closed-form solution for PO, and deal
with PS by an alternating way in the dual domain due to its NP-hardness.
Simulation results demonstrateComment: IEEE GLOBECOM 2020. arXiv admin note: substantial text overlap with
arXiv:2003.1271
A Parallel Optimal Task Allocation Mechanism for Large-Scale Mobile Edge Computing
We consider the problem of intelligent and efficient task allocation
mechanism in large-scale mobile edge computing (MEC), which can reduce delay
and energy consumption in a parallel and distributed optimization. In this
paper, we study the joint optimization model to consider cooperative task
management mechanism among mobile terminals (MT), macro cell base station
(MBS), and multiple small cell base station (SBS) for large-scale MEC
applications. We propose a parallel multi-block Alternating Direction Method of
Multipliers (ADMM) based method to model both requirements of low delay and low
energy consumption in the MEC system which formulates the task allocation under
those requirements as a nonlinear 0-1 integer programming problem. To solve the
optimization problem, we develop an efficient combination of conjugate
gradient, Newton and linear search techniques based algorithm with Logarithmic
Smoothing (for global variables updating) and the Cyclic Block coordinate
Gradient Projection (CBGP, for local variables updating) methods, which can
guarantee convergence and reduce computational complexity with a good
scalability. Numerical results demonstrate the effectiveness of the proposed
mechanism and it can effectively reduce delay and energy consumption for a
large-scale MEC system.Comment: 15 pages,4 figures, resource management for large-scale MEC. arXiv
admin note: text overlap with arXiv:2003.1284
Deep Reinforcement Learning for Stochastic Computation Offloading in Digital Twin Networks
The rapid development of Industrial Internet of Things (IIoT) requires
industrial production towards digitalization to improve network efficiency.
Digital Twin is a promising technology to empower the digital transformation of
IIoT by creating virtual models of physical objects. However, the provision of
network efficiency in IIoT is very challenging due to resource-constrained
devices, stochastic tasks, and resources heterogeneity. Distributed resources
in IIoT networks can be efficiently exploited through computation offloading to
reduce energy consumption while enhancing data processing efficiency. In this
paper, we first propose a new paradigm Digital Twin Networks (DTN) to build
network topology and the stochastic task arrival model in IIoT systems. Then,
we formulate the stochastic computation offloading and resource allocation
problem to minimize the long-term energy efficiency. As the formulated problem
is a stochastic programming problem, we leverage Lyapunov optimization
technique to transform the original problem into a deterministic per-time slot
problem. Finally, we present Asynchronous Actor-Critic (AAC) algorithm to find
the optimal stochastic computation offloading policy. Illustrative results
demonstrate that our proposed scheme is able to significantly outperforms the
benchmarks.Comment: 10 page
Edge Intelligence for Energy-efficient Computation Offloading and Resource Allocation in 5G Beyond
5G beyond is an end-edge-cloud orchestrated network that can exploit
heterogeneous capabilities of the end devices, edge servers, and the cloud and
thus has the potential to enable computation-intensive and delay-sensitive
applications via computation offloading. However, in multi user wireless
networks, diverse application requirements and the possibility of various radio
access modes for communication among devices make it challenging to design an
optimal computation offloading scheme. In addition, having access to complete
network information that includes variables such as wireless channel state, and
available bandwidth and computation resources, is a major issue. Deep
Reinforcement Learning (DRL) is an emerging technique to address such an issue
with limited and less accurate network information. In this paper, we utilize
DRL to design an optimal computation offloading and resource allocation
strategy for minimizing system energy consumption. We first present a
multi-user end-edge-cloud orchestrated network where all devices and base
stations have computation capabilities. Then, we formulate the joint
computation offloading and resource allocation problem as a Markov Decision
Process (MDP) and propose a new DRL algorithm to minimize system energy
consumption. Numerical results based on a real-world dataset demonstrate that
the proposed DRL-based algorithm significantly outperforms the benchmark
policies in terms of system energy consumption. Extensive simulations show that
learning rate, discount factor, and number of devices have considerable
influence on the performance of the proposed algorithm
CL-ADMM: A Cooperative Learning Based Optimization Framework for Resource Management in MEC
We consider the problem of intelligent and efficient resource management
framework in mobile edge computing (MEC), which can reduce delay and energy
consumption, featuring distributed optimization and efficient congestion
avoidance mechanism. In this paper, we present a Cooperative Learning framework
for resource management in MEC from an Alternating Direction Method of
Multipliers (ADMM) perspective, called CL-ADMM framework. First, in order to
caching task efficiently in a group, a novel task popularity estimating scheme
is proposed, which is based on semi-Markov process model, then a greedy task
cooperative caching mechanism has been established, which can effectively
reduce delay and energy consumption. Secondly, for addressing group congestion,
a dynamic task migration scheme based on cooperative improved Q-learning is
proposed, which can effectively reduce delay and alleviate congestion. Thirdly,
for minimizing delay and energy consumption for resources allocation in a
group, we formulate it as an optimization problem with a large number of
variables, and then exploit a novel ADMM based scheme to address this problem,
which can reduce the complexity of problem with a new set of auxiliary
variables, these sub-problems are all convex problems, and can be solved by
using a primal-dual approach, guaranteeing its convergences. Then we prove that
the convergence by using Lyapunov theory. Numerical results demonstrate the
effectiveness of the CL-ADMM and it can effectively reduce delay and energy
consumption for MEC.Comment: 17 pages, 11 figures, submitted to journa