Multiaccess Edge Computing-Based Simulation as a Service for 5G Mobile Applications: A Case Study of Tollgate Selection for Autonomous Vehicles

As the data rate and area capacity are enormously increased with the advent of 5G wireless communication, the network latency becomes a severe issue in a 5G network. Since there are various types of terminals in a 5G network such as vehicles, medical devices, robots, drones, and various sensors which perform complex tasks interacting with other devices dynamically, there is a need to handle heavy computing resource intensive operations. Placing a multiaccess edge computing (MEC) server at the base station, which is located at the edge, can be one of the solutions to it. The application running on the MEC platform needs a specific simulation technique to analyze complex systems inside the MEC network. We proposed and implemented a simulation as a service (SIMaaS) for the MEC platform, which is to offload the simulation using a Cloud infrastructure based on the concept of computation offloading. In the case study, the Monte-Carlo simulations are conducted using the proposed SIMaaS to select the optimal highway tollgate where vehicles are allowed to enter. It shows how clients of the MEC platform use SIMaaS to obtain certain goals.

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Mobility-Aware Service Caching in Mobile Edge Computing for Internet of Things

Sensors ◽

10.3390/s20030610 ◽

2020 ◽

Vol 20 (3) ◽

pp. 610 ◽

Cited By ~ 3

Author(s):

Hua Wei ◽

Hong Luo ◽

Yan Sun

Keyword(s):

Target Location ◽

Geometric Model ◽

Back Propagation ◽

Base Station ◽

Edge Computing ◽

Computation Offloading ◽

Target Area ◽

Mobile Edge Computing ◽

Trajectory Data ◽

Practical Applications

The mobile edge computing architecture successfully solves the problem of high latency in cloud computing. However, current research focuses on computation offloading and lacks research on service caching issues. To solve the service caching problem, especially for scenarios with high mobility in the Sensor Networks environment, we study the mobility-aware service caching mechanism. Our goal is to maximize the number of users who are served by the local edge-cloud, and we need to make predictions about the user’s target location to avoid invalid service requests. First, we propose an idealized geometric model to predict the target area of a user’s movement. Since it is difficult to obtain all the data needed by the model in practical applications, we use frequent patterns to mine local moving track information. Then, by using the results of the trajectory data mining and the proposed geometric model, we make predictions about the user’s target location. Based on the prediction result and existing service cache, the service request is forwarded to the appropriate base station through the service allocation algorithm. Finally, to be able to train and predict the most popular services online, we propose a service cache selection algorithm based on back-propagation (BP) neural network. The simulation experiments show that our service cache algorithm reduces the service response time by about 13.21% on average compared to other algorithms, and increases the local service proportion by about 15.19% on average compared to the algorithm without mobility prediction.

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Mobility-Aware Energy Optimization in Hosts Selection for Computation Offloading in Multi-Access Edge Computing

IEEE Open Journal of the Communications Society ◽

10.1109/ojcoms.2020.3008485 ◽

2020 ◽

Vol 1 ◽

pp. 1056-1065

Author(s):

Shanmuganathan Thananjeyan ◽

Chien Aun Chan ◽

Elaine Wong ◽

Ampalavanapillai Nirmalathas

Keyword(s):

Energy Optimization ◽

Edge Computing ◽

Computation Offloading ◽

Selection For ◽

Multi Access

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

10.21203/rs.3.rs-225233/v1 ◽

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.

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Security and Cost-Aware Computation Offloading via Deep Reinforcement Learning in Mobile Edge Computing

Wireless Communications and Mobile Computing ◽

10.1155/2019/3816237 ◽

2019 ◽

Vol 2019 ◽

pp. 1-20 ◽

Cited By ~ 6

Author(s):

Binbin Huang ◽

Yangyang Li ◽

Zhongjin Li ◽

Linxuan Pan ◽

Shangguang Wang ◽

...

Keyword(s):

Reinforcement Learning ◽

Mobile Devices ◽

Mobile Applications ◽

Mobile User ◽

Edge Computing ◽

Computation Offloading ◽

Security Threats ◽

Mobile Edge Computing ◽

Computing Environment ◽

Markov Decision

With the explosive growth of mobile applications, mobile devices need to be equipped with abundant resources to process massive and complex mobile applications. However, mobile devices are usually resource-constrained due to their physical size. Fortunately, mobile edge computing, which enables mobile devices to offload computation tasks to edge servers with abundant computing resources, can significantly meet the ever-increasing computation demands from mobile applications. Nevertheless, offloading tasks to the edge servers are liable to suffer from external security threats (e.g., snooping and alteration). Aiming at this problem, we propose a security and cost-aware computation offloading (SCACO) strategy for mobile users in mobile edge computing environment, the goal of which is to minimize the overall cost (including mobile device’s energy consumption, processing delay, and task loss probability) under the risk probability constraints. Specifically, we first formulate the computation offloading problem as a Markov decision process (MDP). Then, based on the popular deep reinforcement learning approach, deep Q-network (DQN), the optimal offloading policy for the proposed problem is derived. Finally, extensive experimental results demonstrate that SCACO can achieve the security and cost efficiency for the mobile user in the mobile edge computing environment.

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

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-021-01984-6 ◽

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.

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Edge Computing

International Journal of Fog Computing ◽

10.4018/ijfc.2020010104 ◽

2020 ◽

Vol 3 (1) ◽

pp. 64-74

Author(s):

Minal Parimalbhai Patel ◽

Sanjay Chaudhary

Keyword(s):

Edge Computing ◽

Computation Offloading ◽

Light Weight ◽

Computing Systems

In this article, the researchers have provided a discussion on computation offloading and the importance of docker-based containers, known as light weight virtualization, to improve the performance of edge computing systems. At the end, they have also proposed techniques and a case study for computation offloading and light weight virtualization.

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Efficient Computation Offloading for Service Workflow of Mobile Applications in Mobile Edge Computing

Mobile Information Systems ◽

10.1155/2021/5578465 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Youwei Yuan ◽

Lu Qian ◽

Gangyong Jia ◽

Longxuan Yu ◽

Zixuan Yu ◽

...

Keyword(s):

Mobile Applications ◽

Blocking Probability ◽

Limited Resource ◽

Edge Computing ◽

Computation Offloading ◽

Efficient Computation ◽

Computing Services ◽

Scheduling Method ◽

Np Hard Problem ◽

Environment Service

Edge computing has become a promising solution to overcome the user equipment (UE) constraints such as low computing capacity and limited energy. A key edge computing challenge is providing computing services with low service congestion and low latency, but the computing resources of edge servers were limited. User task randomness and network inhomogeneity brought considerable challenges to limited-resource MEC systems. To solve these problems, the presented paper proposed a blocking- and delay-aware schedule strategy for MEC environment service workflow offloading. First, the workflow was modeled in mobile applications and the buffer queue in servers. Then, the server collaboration area was divided through a collaboration area division method based on clustering. Finally, an improved particle swarm optimization scheduling method was utilized to solve this NP-hard problem. Many simulation results verified the effectiveness of the proposed scheme. This method was superior to existing methods, which effectively reduces the blocking probability and execution delay and ensures the quality of the experience of the user.

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