scholarly journals Efficient task offloading using particle swarm optimization algorithm in edge computing for industrial internet of things

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Qian You ◽  
Bing Tang
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Consumption ◽  
Internet Of Things ◽  
Particle Swarm ◽  
Edge Computing ◽  
Industrial Internet Of Things ◽  
Resource Constrained ◽  
Swarm Optimization ◽  
Industrial Internet ◽  
Task Offloading

AbstractAs a new form of computing based on the core technology of cloud computing and built on edge infrastructure, edge computing can handle computing-intensive and delay-sensitive tasks. In mobile edge computing (MEC) assisted by 5G technology, offloading computing tasks of edge devices to the edge servers in edge network can effectively reduce delay. Designing a reasonable task offloading strategy in a resource-constrained multi-user and multi-MEC system to meet users’ needs is a challenge issue. In industrial internet of things (IIoT) environment, considering the rapid increase of industrial edge devices and the heterogenous edge servers, a particle swarm optimization (PSO)-based task offloading strategy is proposed to offload tasks from resource-constrained edge devices to edge servers with energy efficiency and low delay style. A multi-objective optimization problem that considers time delay, energy consumption and task execution cost is proposed. The fitness function of the particle represents the total cost of offloading all tasks to different MEC servers. The offloading strategy based on PSO is compared with the genetic algorithm (GA) and the simulated annealing algorithm (SA) through simulation experiments. The experimental results show that the task offloading strategy based on PSO can reduce the delay of the MEC server, balance the energy consumption of the MEC server, and effectively realize the reasonable resource allocation.

scholarly journals A Risk-Sensitive Task Offloading Strategy for Edge Computing in Industrial Internet of Things

2021 ◽  
Author(s):  
Xiaoyu Hao ◽  
Ruohai Zhao ◽  
Tao Yang ◽  
Yulin Hu ◽  
Bo Hu ◽  
...  
Keyword(s):  
At Risk ◽  
Internet Of Things ◽  
Value At Risk ◽  
Communication Systems ◽  
Edge Computing ◽  
Conditional Value At Risk ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
End To End ◽  
Task Offloading

Abstract Edge computing has become one of the key enablers for ultra-reliable and low-latency communications in the industrial Internet of Things in the fifth generation communication systems, and is also a promising technology in the future sixth generation communication systems. In this work, we consider the application of edge computing to smart factories for mission-critical task offloading through wireless links. In such scenarios, although high end-to-end delays from the generation to completion of tasks happen with low probability, they may incur severe casualties and property loss, and should be seriously treated. Inspired by the risk management theory widely used in finance, we adopt the Conditional Value at Risk to capture the tail of the delay distribution. An upper bound of the Conditional Value at Risk is derived through analysis of the queues both at the devices and the edge computing servers. We aim to find out the optimal offloading policy taking into consideration both the average and the worst case delay performance of the system. Given that the formulated optimization problem is a non-convex mixed integer non-linear programming problem, a decomposition into sub-problems is performed and a two-stage heuristic algorithm is proposed. Simulation results validate our analysis and indicate that the proposed algorithm can reduce the risk in both the queueing and end-to-end delay.

Research on Task-Offloading Decision Mechanism in Mobile Edge Computing based Internet of Vehicle

2021 ◽  
Author(s):  
Jun Cheng ◽  
Dejun Guan
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Cloud Infrastructure ◽  
Swarm Optimization ◽  
Decision Mechanism ◽  
The Impact ◽  
Task Offloading

Abstract As a technology integrated with Internet of Things (IoT), mobile edge computing (MEC) can provide real-time and low latency services to the underlying network, and improve the storage and computation ability of the networks instead of central cloud infrastructure. In Mobile Edge Computing based Internet of Vehicle(MEC-IoV), the vehicle users can deliver their tasks to the associated MEC servers Based on offloading policy, which improves the resource utilization and computation performance greatly. However, how to evaluate the impact of uncertain interconnection between the vehicle users and MEC servers on offloading decision-making and avoid serious degradation of the offloading efficiency are important problems to be solved. In this paper, a task-offloading decision mechanism with particle swarm optimization for IoV-based edge computing is proposed. First, a mathematical model to calculate the computation offloading cost for cloud-edge computing system is defined. Then, the particle swarm optimization (PSO) is applied to convert the offloading of task into the process and obtain the optimal offloading strategy. Furthermore, to avoid falling into local optimization, the inertia weight factor is designed to change adaptively with the value of the objective function. The experimental results show that the proposed offloading strategy can effectively reduce the energy consumption of terminal devices while guarantee the service quality of users.

scholarly journals A risk-sensitive task offloading strategy for edge computing in industrial Internet of Things

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xiaoyu Hao ◽  
Ruohai Zhao ◽  
Tao Yang ◽  
Yulin Hu ◽  
Bo Hu ◽  
...  
Keyword(s):  
At Risk ◽  
Internet Of Things ◽  
Value At Risk ◽  
Communication Systems ◽  
Edge Computing ◽  
Conditional Value At Risk ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
End To End ◽  
Task Offloading

AbstractEdge computing has become one of the key enablers for ultra-reliable and low-latency communications in the industrial Internet of Things in the fifth generation communication systems and is also a promising technology in the future sixth generation communication systems. In this work, we consider the application of edge computing to smart factories for mission critical task offloading through wireless links. In such scenarios, although high end-to-end delays from the generation to completion of tasks happen with low probability, they may incur severe casualties and property loss and should be seriously treated. Inspired by the risk management theory widely used in finance, we adopt the Conditional Value at Risk to capture the tail of the delay distribution. An upper bound of the Conditional Value at Risk is derived through analysis of the queues both at the devices and the edge computing servers. We aim to find out the optimal offloading policy taking into consideration both the average and the worst-case delay performance of the system. Given that the formulated optimization problem is a non-convex mixed integer nonlinear programming problem, a decomposition into subproblems is performed and a two-stage heuristic algorithm is proposed. The simulation results validate our analysis and indicate that the proposed algorithm can reduce the risk in both the queueing and end-to-end delay.

scholarly journals A Risk-Sensitive Task Offloading Strategy for Edge Computing in Industrial Internet of Things

2020 ◽  
Author(s):  
Xiaoyu Hao ◽  
Ruohai Zhao ◽  
Tao Yang ◽  
Yulin Hu ◽  
Bo Hu ◽  
...  
Keyword(s):  
At Risk ◽  
Internet Of Things ◽  
Value At Risk ◽  
Communication Systems ◽  
Edge Computing ◽  
Conditional Value At Risk ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
End To End ◽  
Task Offloading

Abstract Edge computing has become one of the key enablers for ultra-reliable and low-latency communications in the industrial Internet of Things in the fifth generation communication systems, and is also a promising technology in the future sixth generation communication systems. In this work, we consider the application of edge computing to smart factories for mission-critical task offloading through wireless links. In such scenarios, although high end-to-end delays from the generation to completion of tasks happen with low probability, they may incur severe casualties and property loss, and should be seriously treated. Inspired by the risk management theory widely used in finance, we adopt the Conditional Value at Risk to capture the tail of the delay distribution. An upper bound of the Conditional Value at Risk is derived through analysis of the queues both at the devices and the edge computing servers. We aim to find out the optimal offloading policy taking into consideration both the average and the worst case delay performance of the system. Given that the formulated optimization problem is a non-convex mixed integer non-linear programming problem, a decomposition into sub-problems is performed and a two-stage heuristic algorithm is proposed. Simulation results validate our analysis and indicate that the proposed algorithm can reduce the risk in both the queueing and end-to-end delay.

scholarly journals Research on task-offloading decision mechanism in mobile edge computing-based Internet of Vehicle

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Jun Cheng ◽  
Dejun Guan
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Cloud Infrastructure ◽  
Swarm Optimization ◽  
Decision Mechanism ◽  
The Impact ◽  
Task Offloading

AbstractAs a technology integrated with Internet of things, mobile edge computing (MEC) can provide real-time and low-latency services to the underlying network and improve the storage and computation ability of the networks instead of central cloud infrastructure. In mobile edge computing-based Internet of Vehicle (MEC-IoV), the vehicle users can deliver their tasks to the associated MEC servers based on offloading policy, which improves the resource utilization and computation performance greatly. However, how to evaluate the impact of uncertain interconnection between the vehicle users and MEC servers on offloading decision-making and avoid serious degradation of the offloading efficiency are important problems to be solved. In this paper, a task-offloading decision mechanism with particle swarm optimization for MEC-IoV is proposed. First, a mathematical model to calculate the computation offloading cost for cloud-edge computing system is defined. Then, the particle swarm optimization is applied to convert the offloading of task into the process and obtain the optimal offloading strategy. Furthermore, to avoid falling into local optimization, the inertia weight factor is designed to change adaptively with the value of the objective function. The experimental results show that the proposed offloading strategy can effectively reduce the energy consumption of terminal devices while guarantee the service quality of users.

scholarly journals Editorial: Special Section on Pervasive Edge Computing for Industrial Internet of Things

2021 ◽  
Vol 17 (7) ◽  
pp. 5010-5011
Author(s):  
Zhaolong Ning ◽  
Edith Ngai ◽  
Ricky Y. K. Kwok ◽  
Mohammad S. Obaidat
Keyword(s):  
Internet Of Things ◽  
Special Section ◽  
Edge Computing ◽  
Industrial Internet Of Things ◽  
Industrial Internet
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