scholarly journals Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1484
Author(s):  
Md Delowar Hossain ◽  
Tangina Sultana ◽  
Md Alamgir Hossain ◽  
Md Imtiaz Hossain ◽  
Luan N. T. Huynh ◽  
...  
Keyword(s):  
Dynamic Environment ◽  
Task Completion ◽  
Fuzzy Decision ◽  
Collaborative Task ◽  
High Resource ◽  
Dynamic Decisions ◽  
Optimal Target ◽  
Local Edge ◽  
Multi Access ◽  
Task Offloading

Multi-access edge computing (MEC) is a new leading technology for meeting the demands of key performance indicators (KPIs) in 5G networks. However, in a rapidly changing dynamic environment, it is hard to find the optimal target server for processing offloaded tasks because we do not know the end users’ demands in advance. Therefore, quality of service (QoS) deteriorates because of increasing task failures and long execution latency from congestion. To reduce latency and avoid task failures from resource-constrained edge servers, vertical offloading between mobile devices with local-edge collaboration or with local edge-remote cloud collaboration have been proposed in previous studies. However, they ignored the nearby edge server in the same tier that has excess computing resources. Therefore, this paper introduces a fuzzy decision-based cloud-MEC collaborative task offloading management system called FTOM, which takes advantage of powerful remote cloud-computing capabilities and utilizes neighboring edge servers. The main objective of the FTOM scheme is to select the optimal target node for task offloading based on server capacity, latency sensitivity, and the network’s condition. Our proposed scheme can make dynamic decisions where local or nearby MEC servers are preferred for offloading delay-sensitive tasks, and delay-tolerant high resource-demand tasks are offloaded to a remote cloud server. Simulation results affirm that our proposed FTOM scheme significantly improves the rate of successfully executing offloaded tasks by approximately 68.5%, and reduces task completion time by 66.6%, when compared with a local edge offloading (LEO) scheme. The improved and reduced rates are 32.4% and 61.5%, respectively, when compared with a two-tier edge orchestration-based offloading (TTEO) scheme. They are 8.9% and 47.9%, respectively, when compared with a fuzzy orchestration-based load balancing (FOLB) scheme, approximately 3.2% and 49.8%, respectively, when compared with a fuzzy workload orchestration-based task offloading (WOTO) scheme, and approximately 38.6%% and 55%, respectively, when compared with a fuzzy edge-orchestration based collaborative task offloading (FCTO) scheme.

scholarly journals Linked-Object Dynamic Offloading (LODO) for the Cooperation of Data and Tasks on Edge Computing Environment

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2156
Author(s):  
Svetlana Kim ◽  
Jieun Kang ◽  
YongIk Yoon
Keyword(s):  
Average Power ◽  
Dynamic Environment ◽  
Edge Computing ◽  
Process Data ◽  
Average Power Consumption ◽  
Optimal Target ◽  
Iot Devices ◽  
Local Edge ◽  
Resource Shortage ◽  
Object Dynamic

With the evolution of the Internet of Things (IoT), edge computing technology is using to process data rapidly increasing from various IoT devices efficiently. Edge computing offloading reduces data processing time and bandwidth usage by processing data in real-time on the device where the data is generating or on a nearby server. Previous studies have proposed offloading between IoT devices through local-edge collaboration from resource-constrained edge servers. However, they did not consider nearby edge servers in the same layer with computing resources. Consequently, quality of service (QoS) degrade due to restricted resources of edge computing and higher execution latency due to congestion. To handle offloaded tasks in a rapidly changing dynamic environment, finding an optimal target server is still challenging. Therefore, a new cooperative offloading method to control edge computing resources is needed to allocate limited resources between distributed edges efficiently. This paper suggests the LODO (linked-object dynamic offloading) algorithm that provides an ideal balance between edges by considering the ready state or running state. LODO algorithm carries out tasks in the list in the order of high correlation between data and tasks through linked objects. Furthermore, dynamic offloading considers the running status of all cooperative terminals and decides to schedule task distribution. That can decrease the average delayed time and average power consumption of terminals. In addition, the resource shortage problem can settle by reducing task processing using its distributions.

scholarly journals Fuzzy Based Collaborative Task Offloading Scheme in the Densely Deployed Small-Cell Networks with Multi-Access Edge Computing

2020 ◽  
Vol 10 (9) ◽  
pp. 3115
Author(s):  
Md Delowar Hossain ◽  
Tangina Sultana ◽  
VanDung Nguyen ◽  
Waqas ur Rahman ◽  
Tri D. T. Nguyen ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Failure Rate ◽  
Small Cell ◽  
Edge Computing ◽  
Small Cell Networks ◽  
Task Failure ◽  
Collaborative Task ◽  
Multi Access ◽  
Cell Networks ◽  
Task Offloading

Accelerating the development of the 5G network and Internet of Things (IoT) application, multi-access edge computing (MEC) in a small-cell network (SCN) is designed to provide computation-intensive and latency-sensitive applications through task offloading. However, without collaboration, the resources of a single MEC server are wasted or sometimes overloaded for different service requests and applications; therefore, it increases the user’s task failure rate and task duration. Meanwhile, the distinct MEC server has faced some challenges to determine where the offloaded task will be processed because the system can hardly predict the demand of end-users in advance. As a result, the quality-of-service (QoS) will be deteriorated because of service interruptions, long execution, and waiting time. To improve the QoS, we propose a novel Fuzzy logic-based collaborative task offloading (FCTO) scheme in MEC-enabled densely deployed small-cell networks. In FCTO, the delay sensitivity of the QoS is considered as the Fuzzy input parameter to make a decision where to offload the task is beneficial. The key is to share computation resources with each other and among MEC servers by using fuzzy-logic approach to select a target MEC server for task offloading. As a result, it can accommodate more computation workload in the MEC system and reduce reliance on the remote cloud. The simulation result of the proposed scheme show that our proposed system provides the best performances in all scenarios with different criteria compared with other baseline algorithms in terms of the average task failure rate, task completion time, and server utilization.

Collaborative Task Offloading in Vehicular Edge Multi-Access Networks

2018 ◽  
Vol 56 (8) ◽  
pp. 48-54 ◽  
Author(s):  
Guanhua Qiao ◽  
Supeng Leng ◽  
Ke Zhang ◽  
Yejun He
Keyword(s):  
Access Networks ◽  
Collaborative Task ◽  
Multi Access ◽  
Task Offloading

scholarly journals Collaborative Task Offloading Strategy of UAV Cluster Using Improved Genetic Algorithm in Mobile Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hong Wang
Keyword(s):  
Genetic Algorithm ◽  
Base Station ◽  
Edge Computing ◽  
Base Stations ◽  
Task Completion ◽  
Mobile Edge Computing ◽  
Improved Genetic Algorithm ◽  
Collaborative Task ◽  
Good Signal ◽  
Task Offloading

Aiming at the problem that traditional fixed base stations cannot provide good signal coverage due to geographical factors, which may reduce the efficiency of task offloading, a collaborate task offloading strategy using improved genetic algorithm in mobile edge computing (MEC) is proposed by introducing the unmanned aerial vehicle (UAV) cluster. First, for the scenario of the UAV cluster serving multiple ground terminals, a collaborative task offloading model is formulated to offload the tasks to UAVs or the base station selectively. Then, an objective function and related constraints are put forward to minimize the time delay and energy consumption by analysis of those in the communication and computing process in the system while considering many factors. Then, the improved genetic algorithm is introduced to solve the optimization problem, obtaining the optimal collaborative task offloading strategy. To verify the performance of the proposed method, simulations are conducted on MATLAB. Simulation results showed that the joint utilization of UAV and MEC improves the offloading efficiency of the proposed strategy. When the number of UAVs is 12, the total utility is up to 1.83 and the task completion time does not exceed 110 ms. In this case, the task can be reasonably offloaded to UAVs or accomplished locally.

Security and Energy-aware Collaborative Task Offloading in D2D communication

2021 ◽  
Vol 118 ◽  
pp. 358-373
Author(s):  
Zhongjin Li ◽  
Haiyang Hu ◽  
Hua Hu ◽  
Binbin Huang ◽  
Jidong Ge ◽  
...  
Keyword(s):  
D2d Communication ◽  
Energy Aware ◽  
Collaborative Task ◽  
Task Offloading

Augmented Reality and the Future of Virtual Workspaces

2011 ◽  
pp. 682-698
Author(s):  
James K. Ford ◽  
Tobias Höllerer
Keyword(s):  
Augmented Reality ◽  
Computer Science ◽  
Education And Training ◽  
Decision Makers ◽  
Task Completion ◽  
Future Trends ◽  
Collaborative Task ◽  
The Future ◽  
And Training

Until recently, Augmented Reality (AR) technology has rarely been discussed outside of the computer science world. It has taken years for this technology to become closer to a stable existence, and will most likely take several more years before it will be used by average citizens. However, the technology does exist, it has been applied in several areas, and research is being done to create even more stable systems that are adaptable to various environments. For this reason, it is necessary for decision-makers in establishments where education and training, knowledge distribution, and individual and collaborative task completion are essential to be aware of this technology, its abilities, and the possible impacts to common workspaces and workers. The purpose of this chapter is to inform decision-makers of AR’s history, the completed research and current applications of AR, possible impacts to managers and workers, and the future trends of the technology.

A Cloud–MEC Collaborative Task Offloading Scheme With Service Orchestration

2020 ◽  
Vol 7 (7) ◽  
pp. 5792-5805 ◽  
Author(s):  
Mingfeng Huang ◽  
Wei Liu ◽  
Tian Wang ◽  
Anfeng Liu ◽  
Shigeng Zhang
Keyword(s):  
Service Orchestration ◽  
Collaborative Task ◽  
Task Offloading

scholarly journals Research on a Task Offloading Strategy for the Internet of Vehicles Based on Reinforcement Learning

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6058
Author(s):  
Shuo Xiao ◽  
Shengzhi Wang ◽  
Jiayu Zhuang ◽  
Tianyu Wang ◽  
Jiajia Liu
Keyword(s):  
Reinforcement Learning ◽  
The Internet ◽  
Internet Of Vehicles ◽  
Vehicle Delay ◽  
Multi Agent ◽  
Network Status ◽  
Local Edge ◽  
Communication Methods ◽  
The Internet Of Things ◽  
Task Offloading

Today, vehicles are increasingly being connected to the Internet of Things, which enables them to obtain high-quality services. However, the numerous vehicular applications and time-varying network status make it challenging for onboard terminals to achieve efficient computing. Therefore, based on a three-stage model of local-edge clouds and reinforcement learning, we propose a task offloading algorithm for the Internet of Vehicles (IoV). First, we establish communication methods between vehicles and their cost functions. In addition, according to the real-time state of vehicles, we analyze their computing requirements and the price function. Finally, we propose an experience-driven offloading strategy based on multi-agent reinforcement learning. The simulation results show that the algorithm increases the probability of success for the task and achieves a balance between the task vehicle delay, expenditure, task vehicle utility and service vehicle utility under various constraints.

Sign in / Sign up

Export Citation Format

Share Document