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

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.

Download Full-text

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

Applied Sciences ◽

10.3390/app10010203 ◽

2019 ◽

Vol 10 (1) ◽

pp. 203 ◽

Cited By ~ 2

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.

Download Full-text

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

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-020-01778-2 ◽

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.

Download Full-text

Collaborative Computation Offloading and Resource Allocation in Cache-Aided Hierarchical Edge-Cloud Systems

Electronics ◽

10.3390/electronics8121430 ◽

2019 ◽

Vol 8 (12) ◽

pp. 1430 ◽

Cited By ~ 4

Author(s):

Yanwen Lan ◽

Xiaoxiang Wang ◽

Chong Wang ◽

Dongyu Wang ◽

Qi Li

Keyword(s):

Resource Allocation ◽

Optimization Problem ◽

Cost Minimization ◽

Linear Optimization ◽

Computation Offloading ◽

Mixed Integer ◽

Service Delay ◽

Mixed Integer Nonlinear Optimization ◽

Cost Minimization Problem ◽

Computational Resources

The hierarchical edge-cloud enabled paradigm has recently been proposed to provide abundant resources for 5G wireless networks. However, the computation and communication capabilities are heterogeneous which makes the potential advantages difficult to be fully explored. Besides, previous works on mobile edge computing (MEC) focused on server caching and offloading, ignoring the computational and caching gains brought by the proximity of user equipments (UEs). In this paper, we investigate the computation offloading in a three-tier cache-assisted hierarchical edge-cloud system. In this system, UEs cache tasks and can offload their workloads to edge servers or adjoining UEs by device-to-device (D2D) for collaborative processing. A cost minimization problem is proposed by the tradeoff between service delay and energy consumption. In this problem, the offloading decision, the computational resources and the offloading ratio are jointly optimized in each offloading mode. Then, we formulate this problem as a mixed-integer nonlinear optimization problem (MINLP) which is non-convex. To solve it, we propose a joint computation offloading and resource allocation optimization (JORA) scheme. Primarily, in this scheme, we decompose the original problem into three independent subproblems and analyze their convexity. After that, we transform them into solvable forms (e.g., convex optimization problem or linear optimization problem). Then, an iteration-based algorithm with the Lagrange multiplier method and a distributed joint optimization algorithm with the adoption of game theory are proposed to solve these problems. Finally, the simulation results show the performance of our proposed scheme compared with other existing benchmark schemes.

Download Full-text

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

Sensors ◽

10.3390/s20061582 ◽

2020 ◽

Vol 20 (6) ◽

pp. 1582 ◽

Cited By ~ 3

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.

Download Full-text

Double Auction-Based Two-Level Resource Allocation Mechanism for Computation Offloading in Mobile Blockchain Application

Mobile Information Systems ◽

10.1155/2021/8821583 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Li Li ◽

Yue Li ◽

Ruotong Li

Keyword(s):

Resource Allocation ◽

Mobile Devices ◽

Large Scale ◽

Social Utility ◽

Computation Offloading ◽

Double Auction ◽

Allocation Mechanism ◽

Core Issue ◽

High Utilization ◽

Resource Allocation Mechanism

It is increasingly popular that platforms integrate various services into mobile applications due to the high usage and convenience of mobile devices, many of which demand high computational capacities and energy, such as cryptocurrency services based on blockchain. However, it is hard for mobile devices to run these services due to the limited storage and computational capacity. In this paper, the problem of computation offloading that requires sufficient computing resources with high utilization in large-scale users and multiprovider MEC system was investigated. A mechanism based on the combinatorial double auction, G-TRAP, is proposed in this paper to solve the above problem. In the mechanism, resources are provided both in the cloud and at the edge of the network. Mobile users compete for these resources to offload computing tasks by the rule that the edge-level resources will be allocated at first while cloud-level resources could be the supplement for the edge level. Given that the proof-of-work (PoW), the core issue of blockchain application, is resource-expensive to implement in mobile devices, we provide resource allocation service to users of blockchain application as experimental subjects. Simulation results show that the proposed mechanism for serving large-scale users in a short execution time outperforms two existing algorithms in terms of social utility and resource utilization. Consequently, our proposed system can effectively solve the intensive computation offloading problem of mobile blockchain applications.

Download Full-text

Unsupervised Deep Learning for Binary Offloading in Mobile Edge Computation Network

10.21203/rs.3.rs-372831/v1 ◽

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.

Download Full-text