scholarly journals Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 58 ◽  
Author(s):  
Xuan-Qui Pham ◽  
Tien-Dung Nguyen ◽  
VanDung Nguyen ◽  
Eui-Nam Huh
Keyword(s):  
Resource Allocation ◽  
High Reliability ◽  
Resource Limitation ◽  
Optimization Techniques ◽  
Edge Computing ◽  
Mixed Integer ◽  
Node Selection ◽  
Computing Services ◽  
Iot Devices ◽  
Multi Access

The resource limitation of multi-access edge computing (MEC) is one of the major issues in order to provide low-latency high-reliability computing services for Internet of Things (IoT) devices. Moreover, with the steep rise of task requests from IoT devices, the requirement of computation tasks needs dynamic scalability while using the potential of offloading tasks to mobile volunteer nodes (MVNs). We, therefore, propose a scalable vehicle-assisted MEC (SVMEC) paradigm, which cannot only relieve the resource limitation of MEC but also enhance the scalability of computing services for IoT devices and reduce the cost of using computing resources. In the SVMEC paradigm, a MEC provider can execute its users’ tasks by choosing one of three ways: (i) Do itself on local MEC, (ii) offload to the remote cloud, and (iii) offload to the MVNs. We formulate the problem of joint node selection and resource allocation as a Mixed Integer Nonlinear Programming (MINLP) problem, whose major objective is to minimize the total computation overhead in terms of the weighted-sum of task completion time and monetary cost for using computing resources. In order to solve it, we adopt alternative optimization techniques by decomposing the original problem into two sub-problems: Resource allocation sub-problem and node selection sub-problem. Simulation results demonstrate that our proposed scheme outperforms the existing schemes in terms of the total computation overhead.

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 Energy-efficient offloading and resource allocation for mobile edge computing enabled mission-critical internet-of-things systems

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yaru Fu ◽  
Xiaolong Yang ◽  
Peng Yang ◽  
Angus K. Y. Wong ◽  
Zheng Shi ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Internet Of Things ◽  
Energy Saving ◽  
Energy Cost ◽  
Cost Minimization ◽  
Edge Computing ◽  
Mixed Integer ◽  
Mobile Edge Computing ◽  
Mission Critical ◽  
Iot Devices

AbstractThe energy cost minimization for mission-critical internet-of-things (IoT) in mobile edge computing (MEC) system is investigated in this work. Therein, short data packets are transmitted between the IoT devices and the access points (APs) to reduce transmission latency and prolong the battery life of the IoT devices. The effects of short-packet transmission on the radio resource allocation is explicitly revealed. We mathematically formulate the energy cost minimization problem as a mixed-integer non-linear programming (MINLP) problem, which is difficult to solve in an optimal way. More specifically, the difficulty is essentially derived from the coupling of the binary offloading variables and the resource management among all the IoT devices. For analytical tractability, we decouple the mixed-integer and non-convex optimization problem into two sub-problems, namely, the task offloading decision-making and the resource optimization problems, respectively. It is proved that the resource allocation problem for IoT devices under the fixed offloading strategy is convex. On this basis, an iterative algorithm is designed, whose performance is comparable to the best solution for exhaustive search, and aims to jointly optimize the offloading strategy and resource allocation. Simulation results verify the convergence performance and energy-saving function of the designed joint optimization algorithm. Compared with the extensive baselines under comprehensive parameter settings, the algorithm has better energy-saving effects.

scholarly journals Energy-Efficient Offloading and Resource Allocation for Mobile Edge Computing Enabled Mission-Critical Internet-of-Things Systems

2020 ◽  
Author(s):  
Yaru Fu ◽  
Xiaolong Yang ◽  
Peng Yang ◽  
Angus K. Y. Wong ◽  
Zheng Shi ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Internet Of Things ◽  
Energy Saving ◽  
Energy Cost ◽  
Cost Minimization ◽  
Edge Computing ◽  
Mixed Integer ◽  
Mobile Edge Computing ◽  
Mission Critical ◽  
Iot Devices

Abstract The energy cost minimization for mission-critical internet-of-things (IoT) in mobile edge computing (MEC) system is investigated in this work. Therein, short data packets are transmitted between the IoT devices and the access points (APs) to reduce transmission latency and prolong the battery life of the IoT devices. The effects of short-packet transmission on the radio resource allocation is explicitly revealed. We mathematically formulate the energy cost minimization problem as a mixed-integer non-linear programming (MINLP) problem, which is difficult to solve in an optimal way. More specifically, the difficulty is essentially derived from the coupling of the binary offloading variables and the resource management among all the IoT devices. For analytical tractability, we decouple the mixed-integer and non-convex optimization problem into two sub-problems, namely, the task offloading decision-making and the resource optimization problems, respectively. It is proved that the resource allocation problem for IoT devices under the fixed offloading strategy is convex. On this basis, an iterative algorithm is designed, whose performance is comparable to the best solution for exhaustive search, and aims to jointly optimize the offloading strategy and resource allocation. Simulation results verify the convergence performance and energy-saving function of the designed joint optimization algorithm. Compared with the extensive baselines under comprehensive parameter settings, the algorithm has better energy-saving effects.

scholarly journals Scalable Edge Computing Deployment for Reliable Service Provisioning in Vehicular Networks

2019 ◽  
Vol 8 (4) ◽  
pp. 51 ◽  
Author(s):  
Federico Tonini ◽  
Bahare Khorsandi ◽  
Elisabetta Amato ◽  
Carla Raffaelli
Keyword(s):  
Vehicular Networks ◽  
Programming Model ◽  
High Reliability ◽  
Cost Optimization ◽  
Access Network ◽  
Computational Effort ◽  
Edge Computing ◽  
Hybrid Strategy ◽  
Radio Access ◽  
Multi Access

The global connected cars market is growing rapidly. Novel services will be offered to vehicles, many of them requiring low-latency and high-reliability networking solutions. The Cloud Radio Access Network (C-RAN) paradigm, thanks to the centralization and virtualization of baseband functions, offers numerous advantages in terms of costs and mobile radio performance. C-RAN can be deployed in conjunction with a Multi-access Edge Computing (MEC) infrastructure, bringing services close to vehicles supporting time-critical applications. However, a massive deployment of computational resources at the edge may be costly, especially when reliability requirements demand deployment of redundant resources. In this context, cost optimization based on integer linear programming may result in being too complex when the number of involved nodes is more than a few tens. This paper proposes a scalable approach for C-RAN and MEC computational resource deployment with protection against single-edge node failure. A two-step hybrid model is proposed to alleviate the computational complexity of the integer programming model when edge computing resources are located in physical nodes. Results show the effectiveness of the proposed hybrid strategy in finding optimal or near-optimal solutions with different network sizes and with affordable computational effort.

scholarly journals Container-Based Fast Service Migration Method for Mobile Edge Computing

2021 ◽  
Author(s):  
Xianyu Meng ◽  
Wei Lu
Keyword(s):  
Dynamic Loading ◽  
File System ◽  
High Reliability ◽  
Rapid Development ◽  
Edge Computing ◽  
Migration Time ◽  
Mobile Edge Computing ◽  
Computing Center ◽  
Service Migration ◽  
Iot Devices

Mobile edge computing (MEC) provides users with low-latency, high-bandwidth, and high-reliability services by migrating the computing power of the cloud computing center to the edge of the network. It is thus being considered an effective solution for the contradiction between the limited computing capabilities of Internet of Things (IoT) devices and the rapid development of delay-sensitive real-time applications. In this study, we propose and design a container union file system based on the differencing hard disk and dynamic loading strategy to address the excessively long migration time caused by the bundling transmission of the file system and container images during container-based service migration. The proposed method involves designing a mechanism based on a remote dynamic loading strategy to avoid the downloading of all container images, thereby reducing the long preparation time before which stateless migration can begin. Furthermore, in view of the excessive latency of the edge service during the stateful migration process, a strategy for avoiding the transmission of the underlying file system and container images is designed to optimize the service interruption time and service quality degradation time. Experiments show that the proposed method and strategy can effectively reduce the migration time of container-based services.

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