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 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.

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.

Hierarchical RL for Load Balancing and QoS Management in Multi-Access Networks

2021 ◽  
Author(s):  
Antonio Ornatelli ◽  
Andrea Tortorelli ◽  
Alessandro Giuseppi ◽  
Francesco Delli Priscoli
Keyword(s):  
Load Balancing ◽  
Access Networks ◽  
Qos Management ◽  
Multi Access

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

scholarly journals Performance Evaluation of Integrated Multi-Access Edge Computing and Fiber-Wireless Access Networks

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 30269-30279 ◽  
Author(s):  
Jing Liu ◽  
Guochu Shou ◽  
Yaqiong Liu ◽  
Yihong Hu ◽  
Zhigang Guo
Keyword(s):  
Performance Evaluation ◽  
Access Networks ◽  
Edge Computing ◽  
Wireless Access ◽  
Wireless Access Networks ◽  
Multi Access

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
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