Reliable Mobile Edge Service Offloading Based on P2P Distributed Networks

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.

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An Efficient Task Scheduling Strategy Utilizing Mobile Edge Computing in Autonomous Driving Environment

Electronics ◽

10.3390/electronics8111221 ◽

2019 ◽

Vol 8 (11) ◽

pp. 1221 ◽

Cited By ~ 1

Author(s):

Liu ◽

Chen ◽

Wu ◽

Deng ◽

Liu ◽

...

Keyword(s):

High Speed ◽

Scheduling Algorithm ◽

Rapid Development ◽

Autonomous Driving ◽

Intelligent Vehicles ◽

Edge Computing ◽

Prediction Algorithm ◽

Battery Life ◽

Mobile Edge Computing ◽

Computing Power

With the rapid development of various new types of services, autonomous driving has received extensive attention. Due to the dense traffic flow, the limited battery life and computing power of the vehicles, intelligent vehicles are unable to support some computationally intensive and urgent tasks. Autonomous driving imposes strict requirements on the response time of the task. Due to the strong computing power and proximity to the terminal of mobile edge computing (MEC) and the arrival of 5G, the task can be unloaded to MEC, and data can be exchanged in milliseconds, which can reduce the task execution time. However, the resources of the MEC server are still very limited. Therefore we proposed a scheduling algorithm that takes into account the special task of the autopilot. Tasks will select the appropriate edge cloud execution and schedule the execution sequence on the edge cloud by the scheduling algorithm. At the same time, we take the mobility of high-speed vehicles into consideration. The position of the vehicle can be obtained by the prediction algorithm, and the task results are returned to the vehicle by means of other edge clouds. The experimental results show that with the increase of the task amount, the algorithm can effectively schedule more tasks to be completed within the specified time, and in different time slots; it can also predict the location of the vehicle and return the result to the vehicle.

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Implementation analysis of IoT-based offloading frameworks on cloud/edge computing for sensor generated big data

Complex & Intelligent Systems ◽

10.1007/s40747-021-00434-6 ◽

2021 ◽

Author(s):

Karan Bajaj ◽

Bhisham Sharma ◽

Raman Singh

Keyword(s):

High Speed ◽

Smart Cities ◽

Fog Computing ◽

Edge Computing ◽

Computation Offloading ◽

Performance Parameters ◽

Iot Applications ◽

Time Sensitivity ◽

Implementation Analysis ◽

Increasing Demand

AbstractThe Internet of Things (IoT) applications and services are increasingly becoming a part of daily life; from smart homes to smart cities, industry, agriculture, it is penetrating practically in every domain. Data collected over the IoT applications, mostly through the sensors connected over the devices, and with the increasing demand, it is not possible to process all the data on the devices itself. The data collected by the device sensors are in vast amount and require high-speed computation and processing, which demand advanced resources. Various applications and services that are crucial require meeting multiple performance parameters like time-sensitivity and energy efficiency, computation offloading framework comes into play to meet these performance parameters and extreme computation requirements. Computation or data offloading tasks to nearby devices or the fog or cloud structure can aid in achieving the resource requirements of IoT applications. In this paper, the role of context or situation to perform the offloading is studied and drawn to a conclusion, that to meet the performance requirements of IoT enabled services, context-based offloading can play a crucial role. Some of the existing frameworks EMCO, MobiCOP-IoT, Autonomic Management Framework, CSOS, Fog Computing Framework, based on their novelty and optimum performance are taken for implementation analysis and compared with the MAUI, AnyRun Computing (ARC), AutoScaler, Edge computing and Context-Sensitive Model for Offloading System (CoSMOS) frameworks. Based on the study of drawn results and limitations of the existing frameworks, future directions under offloading scenarios are discussed.

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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.

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A ROBUST CALCULUS OFFLOADING STRUCTURE FOR AMBULANT HEALTH INQUIRIES

EPRA International Journal of Research & Development (IJRD) ◽

10.36713/epra8437 ◽

2021 ◽

pp. 144-151

Author(s):

Pitta Rebecca Alekhya ◽

K. Tulasi Krishna Kumar Nainar

Keyword(s):

Resource Utilization ◽

Mobile Health ◽

Completion Time ◽

Edge Computing ◽

Computation Offloading ◽

Utilization Rate ◽

Task Completion ◽

Base Line ◽

Internet Of Vehicles ◽

Multi Access

Recently, research intergrading medicine and Artificial Intelligence has attracted extensive attention. Mobile health has emerged as a promising paradigm for improving people’s work and life in the future. However, high mobility of mobile devices and limited resources pose challenges for users to deal with the applications in mobile health that require large amount of computational resources. In this paper, a novel computation offloading mechanism is proposed in the environments combining of the Internet of Vehicles and Multi-Access Edge Computing. Through the proposed mechanism, mobile health applications are divided into several parts and can be offloaded to appropriate nearby vehicles while meeting the requirements of application completion time, energy consumption, and resource utilization. A particle swarm optimization based approach is proposed to optimize the aforementioned computation offloading problem in a specific medical application. Evaluations of the proposed algorithms against local computing method serves as base line method are conducted via extensive simulations. The average task completion time saved by our proposed task allocation scheme increases continually compared with the local solution. Specially, the global resource utilization rate increased from 71.8% to 94.5% compared with the local execution time. KEY WORDS: Computation Offloading, Mobile Health, Internet of Vehicles, Multi-Access Edge Computing.

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Computation Offloading Game in Multiple UAV-Enabled Mobile Edge Computing Networks

IET Communications ◽

10.1049/iet-com.2020.0131 ◽

2020 ◽

Author(s):

Yanling Ren ◽

Zhibin Xie ◽

Zhenfeng Ding ◽

xiyuan sun ◽

Jie Xia ◽

...

Keyword(s):

Edge Computing ◽

Computation Offloading ◽

Mobile Edge Computing

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Leveraging Intelligent Computation Offloading with Fog/Edge Computing for Tactile Internet: Advantages and Limitations

IEEE Network ◽

10.1109/mnet.001.2000004 ◽

2020 ◽

Vol 34 (5) ◽

pp. 322-329

Author(s):

Mithun Mukherjee ◽

Mian Guo ◽

Jaime Lloret ◽

Qi Zhang

Keyword(s):

Edge Computing ◽

Computation Offloading

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Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing

Sensors ◽

10.3390/s21082628 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2628

Author(s):

Mengxing Huang ◽

Qianhao Zhai ◽

Yinjie Chen ◽

Siling Feng ◽

Feng Shu

Keyword(s):

Optimization Algorithm ◽

Edge Computing ◽

Solution Set ◽

Whale Optimization Algorithm ◽

Computation Offloading ◽

Mobile Edge Computing ◽

Multi Objective ◽

Whale Optimization ◽

Artificial Immune System Algorithm

Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions.

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