scholarly journals Optimal Design of Hierarchical Cloud-Fog&Edge Computing Networks with Caching

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1582 ◽  
Author(s):  
Xiaoqian Fan ◽  
Haina Zheng ◽  
Ruihong Jiang ◽  
Jinyu Zhang
Keyword(s):  
Optimal Design ◽  
Mode Selection ◽  
Optimal Solution ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mixed Integer ◽  
Task Completion ◽  
Transmit Power ◽  
Content Caching ◽  
Power Threshold

This paper investigates the optimal design of a hierarchical cloud-fog&edge computing (FEC) network, which consists of three tiers, i.e., the cloud tier, the fog&edge tier, and the device tier. The device in the device tier processes its task via three computing modes, i.e., cache-assisted computing mode, cloud-assisted computing mode, and joint device-fog&edge computing mode. Specifically, the task corresponds to being completed via the content caching in the FEC tier, the computation offloading to the cloud tier, and the joint computing in the fog&edge and device tier, respectively. For such a system, an energy minimization problem is formulated by jointly optimizing the computing mode selection, the local computing ratio, the computation frequency, and the transmit power, while guaranteeing multiple system constraints, including the task completion deadline time, the achievable computation capability, and the achievable transmit power threshold. Since the problem is a mixed integer nonlinear programming problem, which is hard to solve with known standard methods, it is decomposed into three subproblems, and the optimal solution to each subproblem is derived. Then, an efficient optimal caching, cloud, and joint computing (CCJ) algorithm to solve the primary problem is proposed. Simulation results show that the system performance achieved by our proposed optimal design outperforms that achieved by the benchmark schemes. Moreover, the smaller the achievable transmit power threshold of the device, the more energy is saved. Besides, with the increment of the data size of the task, the lesser is the local computing ratio.

scholarly journals Computation efficiency optimization in UAV-enabled mobile edge computing system with multi-carrier non-orthogonal multiple access

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Fangcheng Xu ◽  
Xiangbin Yu ◽  
Jiali Cai ◽  
Guangying Wang
Keyword(s):  
Multiple Access ◽  
Initial Point ◽  
Optimal Solution ◽  
Arbitrary Point ◽  
Polynomial Time Algorithm ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mixed Integer ◽  
Mobile Edge Computing ◽  
Computation Efficiency

Abstract In this paper, we study the issue of fair resource optimization for an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) system with multi-carrier non-orthogonal multiple access (MC-NOMA). A computation efficiency (CE) optimization problem based on the max-min fairness principle under the partial offloading mode is formulated by optimizing the subchannel assignment, the local CPU frequency, and the transmission power jointly. The formulated problem belongs to the non-convex mixed integer nonlinear programming (MINLP), that is NP-hard to find the global optimal solution. Therefore, we design a polynomial-time algorithm based on the big-M reformulation, the penalized sequential convex programming, and the general Dinkelbach’s method, which can choose an arbitrary point as the initial point and eventually converge to a feasible suboptimal solution. The proposed algorithm framework can be also applied to computation offloading only mode. Additionally, we derive the closed-form optimal solution under the local computing only mode. Simulation results validate the convergence performance of the proposed algorithm. Moreover, the proposed partial offloading mode with the CE maximization scheme outperforms that with the computation bits (CB) maximization scheme with respect to CE, and it can achieve higher CE than the benchmark computing modes. Furthermore, the proposed MC-NOMA scheme can attain better CE performance than the conventional OFDMA scheme.

scholarly journals User-Edge Collaborative Resource Allocation and Offloading Strategy in Edge Computing

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Zhenquan Qin ◽  
Xueyan Qiu ◽  
Jin Ye ◽  
Lei Wang
Keyword(s):  
Resource Allocation ◽  
Mobile Devices ◽  
Large Scale ◽  
Optimal Solution ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mixed Integer ◽  
Execution Costs ◽  
Mixed Integer Nonlinear Optimization ◽  
Iterative Optimization Algorithm

The foundation of urban computing and smart technology is edge computing. Edge computing provides a new solution for large-scale computing and saves more energy while bringing a small amount of latency compared to local computing on mobile devices. To investigate the relationship between the cost of computing tasks and the consumption of time and energy, we propose a computation offloading scheme that achieves lower execution costs by cooperatively allocating computing resources by mobile devices and the edge server. For the mixed-integer nonlinear optimization problem of computing resource allocation and offloading strategy, we segment the problem and propose an iterative optimization algorithm to find the approximate optimal solution. The numerical results of the simulation experiment show that the algorithm can obtain a lower total cost than the baseline algorithm in most cases.

scholarly journals Efficient Computation Offloading in Multi-Tier Multi-Access Edge Computing Systems: A Particle Swarm Optimization Approach

2019 ◽  
Vol 10 (1) ◽  
pp. 203 ◽  
Author(s):  
Luan N. T. Huynh ◽  
Quoc-Viet Pham ◽  
Xuan-Qui Pham ◽  
Tri D. T. Nguyen ◽  
Md Delowar Hossain ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mixed Integer ◽  
5G Networks ◽  
Swarm Optimization ◽  
Multi Access

In recent years, multi-access edge computing (MEC) has become a promising technology used in 5G networks based on its ability to offload computational tasks from mobile devices (MDs) to edge servers in order to address MD-specific limitations. Despite considerable research on computation offloading in 5G networks, this activity in multi-tier multi-MEC server systems continues to attract attention. Here, we investigated a two-tier computation-offloading strategy for multi-user multi-MEC servers in heterogeneous networks. For this scenario, we formulated a joint resource-allocation and computation-offloading decision strategy to minimize the total computing overhead of MDs, including completion time and energy consumption. The optimization problem was formulated as a mixed-integer nonlinear program problem of NP-hard complexity. Under complex optimization and various application constraints, we divided the original problem into two subproblems: decisions of resource allocation and computation offloading. We developed an efficient, low-complexity algorithm using particle swarm optimization capable of high-quality solutions and guaranteed convergence, with a high-level heuristic (i.e., meta-heuristic) that performed well at solving a challenging optimization problem. Simulation results indicated that the proposed algorithm significantly reduced the total computing overhead of MDs relative to several baseline methods while guaranteeing to converge to stable solutions.

scholarly journals Markov Approximation for Task Offloading and Computation Scaling in Mobile Edge Computing

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Wenchen Zhou ◽  
Weiwei Fang ◽  
Yangyang Li ◽  
Bo Yuan ◽  
Yiming Li ◽  
...  
Keyword(s):  
Optimal Solution ◽  
Task Assignment ◽  
Edge Computing ◽  
Computation Offloading ◽  
Approximation Technique ◽  
Markov Approximation ◽  
Mobile Edge Computing ◽  
Computing Services ◽  
Cloud Computing Services ◽  
Task Offloading

Mobile edge computing (MEC) provides cloud-computing services for mobile devices to offload intensive computation tasks to the physically proximal MEC servers. In this paper, we consider a multiserver system where a single mobile device asks for computation offloading to multiple nearby servers. We formulate this offloading problem as the joint optimization of computation task assignment and CPU frequency scaling, in order to minimize a tradeoff between task execution time and mobile energy consumption. The resulting optimization problem is combinatorial in essence, and the optimal solution generally can only be obtained by exhaustive search with extremely high complexity. Leveraging the Markov approximation technique, we propose a light-weight algorithm that can provably converge to a bounded near-optimal solution. The simulation results show that the proposed algorithm is able to generate near-optimal solutions and outperform other benchmark algorithms.

Unsupervised Deep Learning for Binary Offloading in Mobile Edge Computation Network

2021 ◽  
Author(s):  
Xue Chen ◽  
Hongbo Xu ◽  
Guoping Zhang ◽  
Yun Chen ◽  
Ruijie Li
Keyword(s):  
Resource Allocation ◽  
Deep Learning ◽  
Energy Consumption ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Low Complexity ◽  
Computation Offloading ◽  
Mixed Integer ◽  
Unsupervised Deep Learning ◽  
Execution Delay

Abstract Mobile edge computation (MEC) is a potential technology to reduce the energy consumption and task execution delay for tackling computation-intensive tasks on mobile device (MD). The resource allocation of MEC is an optimization problem, however, the existing large amount of computation may hinder its practical application. In this work, we propose a multiuser MEC framework based on unsupervised deep learning (DL) to reduce energy consumption and computation by offloading tasks to edge servers. The binary offloading decision and resource allocation are jointly optimized to minimize energy consumption of MDs under latency constraint and transmit power constraint. This joint optimization problem is a mixed integer nonconvex problem which result in the gradient vanishing problem in backpropagation. To address this, we propose a novel binary computation offloading scheme (BCOS), in which a deep neural network (DNN) with an auxiliary network is designed. By using the auxiliary network as a teacher network, the student network can obtain the lossless gradient information in joint training phase. As a result, the sub-optimal solution of the optimization problem can be acquired by the learning-based BCOS. Simulation results demonstrate that the BCOS is effective to solve the binary offloading problem by the trained network with low complexity.

Computation Offloading and Content Caching in Wireless Blockchain Networks With Mobile Edge Computing

2018 ◽  
Vol 67 (11) ◽  
pp. 11008-11021 ◽  
Author(s):  
Mengting Liu ◽  
F. Richard Yu ◽  
Yinglei Teng ◽  
Victor C. M. Leung ◽  
Mei Song
Keyword(s):  
Edge Computing ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Content Caching

scholarly journals Energy-Efficient Collaborative Task Computation Offloading in Cloud-Assisted Edge Computing for IoT Sensors

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1105 ◽  
Author(s):  
Fagui Liu ◽  
Zhenxi Huang ◽  
Liangming Wang
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mixed Integer ◽  
Integer Programming Problem ◽  
Efficient Computation ◽  
Collaborative Task ◽  
Computing Paradigm ◽  
Task Dependency

As an emerging and promising computing paradigm in the Internet of things (IoT),edge computing can significantly reduce energy consumption and enhance computation capabilityfor resource-constrained IoT devices. Computation offloading has recently received considerableattention in edge computing. Many existing studies have investigated the computation offloadingproblem with independent computing tasks. However, due to the inter-task dependency in variousdevices that commonly happens in IoT systems, achieving energy-efficient computation offloadingdecisions remains a challengeable problem. In this paper, a cloud-assisted edge computing frameworkwith a three-tier network in an IoT environment is introduced. In this framework, we first formulatedan energy consumption minimization problem as a mixed integer programming problem consideringtwo constraints, the task-dependency requirement and the completion time deadline of the IoT service.To address this problem, we then proposed an Energy-efficient Collaborative Task ComputationOffloading (ECTCO) algorithm based on a semidefinite relaxation and stochastic mapping approachto obtain strategies of tasks computation offloading for IoT sensors. Simulation results demonstratedthat the cloud-assisted edge computing framework was feasible and the proposed ECTCO algorithmcould effectively reduce the energy cost of IoT sensors.

scholarly journals Reliable Mobile Edge Service Offloading Based on P2P Distributed Networks

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 821 ◽  
Author(s):  
Lujie Tang ◽  
Bing Tang ◽  
Luyu Tang ◽  
Feiyan Guo ◽  
Jiaming Zhang
Keyword(s):  
Fault Tolerance ◽  
High Speed ◽  
Distributed Networks ◽  
Intelligent Vehicles ◽  
Edge Computing ◽  
Computation Offloading ◽  
Task Completion ◽  
Computing Power ◽  
Moving Vehicles ◽  
Geographically Distributed

Intelligent vehicles and their applications increasingly demand high computing power and low task delays, which poses significant challenges for providing reliable and efficient vehicle services. Mobile edge computing (MEC) is a new model that reduces the completion time of tasks and improves vehicle service by performing computation offloading near the moving vehicles. Considering the high-speed mobility of the vehicles and the unstable connection of the wireless cellular network, symmetric and geographically distributed edge servers are regarded as peers in a peer-to-peer (P2P) network, and a P2P-based vehicle edge offloading model is proposed in this paper to determine the optimal offloading server for the vehicle and the offloading ratio of tasks to achieve the goal of minimizing execution time. Because the edge computing infrastructure is deployed at the edge of the network, the data in the edge nodes are easily damaged or lost. Therefore, a P2P-based edge node fault tolerance mechanism is proposed to improve the reliability and fault tolerance of the system. The feasibility and effectiveness of our proposed system have been verified through simulation experiments, which greatly reduces the task completion delay.

scholarly journals Robust Train Scheduling Problem with Optimized Maintenance Planning on High-Speed Railway Corridors: The China Case

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Chuntian Zhang ◽  
Yuan Gao ◽  
Wenjie Li ◽  
Lixing Yang ◽  
Ziyou Gao
Keyword(s):  
Travel Time ◽  
High Speed ◽  
Mode Selection ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Planning Problem ◽  
Scheduling Problem ◽  
Maintenance Planning ◽  
State Variables ◽  
Train Scheduling

Simultaneously considering train scheduling problem and maintenance planning problem with uncertain travel time, we propose a two-stage integrated optimization model for the sunset-departure and sunrise-arrival trains (SDSA-trains). Specifically, in the first stage, we obtain an optimal solution of the SDSA-trains under each scenario, which leads to the minimum total travel time. In the second stage, a robust SDSA-train schedule is generated based on the optimal solutions of the first stage. The key is that we consider two operation modes to solve the conflict between the SDSA-trains and the maintenances. Some state variables are used to deal with train operation mode selection. Furthermore, some linearization techniques are used to formulate a mixed-integer linear programming (MILP) model. Finally, numerical experiments are implemented to prove the effectiveness of the proposed model and optimization method.

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