Dynamic Task Offload System Adapting to the State of Network Resources in Mobile Edge Computing

2020 ◽  
Author(s):  
Hayata Satake ◽  
Yuki Kobayashi ◽  
Ryotaro Tani ◽  
Hiroshi Shigeno
Keyword(s):  
The State ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Network Resources ◽  
Dynamic Task

Dynamic Task Offloading and Resource Allocation for Mobile-Edge Computing in Dense Cloud RAN

2020 ◽  
Vol 7 (4) ◽  
pp. 3282-3299 ◽  
Author(s):  
Qi Zhang ◽  
Lin Gui ◽  
Fen Hou ◽  
Jiacheng Chen ◽  
Shichao Zhu ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Dynamic Task ◽  
Dense Cloud ◽  
Cloud Ran ◽  
Task Offloading

Dynamic Task Scheduling in Cloud-Assisted Mobile Edge Computing

2021 ◽  
pp. 1-1
Author(s):  
Xiao Ma ◽  
Ao Zhou ◽  
Shan Zhang ◽  
Qing Li ◽  
Alex X Liu ◽  
...  
Keyword(s):  
Task Scheduling ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Dynamic Task ◽  
Dynamic Task Scheduling

scholarly journals Deep Learning at the Mobile Edge: Opportunities for 5G Networks

2020 ◽  
Vol 10 (14) ◽  
pp. 4735 ◽  
Author(s):  
Miranda McClellan ◽  
Cristina Cervelló-Pastor ◽  
Sebastià Sallent
Keyword(s):  
Large Scale ◽  
Edge Computing ◽  
Connected Vehicles ◽  
Mobile Edge Computing ◽  
5G Networks ◽  
End User ◽  
Network Resources ◽  
Mobility Modeling ◽  
Novel Applications ◽  
Adaptive Resource

Mobile edge computing (MEC) within 5G networks brings the power of cloud computing, storage, and analysis closer to the end user. The increased speeds and reduced delay enable novel applications such as connected vehicles, large-scale IoT, video streaming, and industry robotics. Machine Learning (ML) is leveraged within mobile edge computing to predict changes in demand based on cultural events, natural disasters, or daily commute patterns, and it prepares the network by automatically scaling up network resources as needed. Together, mobile edge computing and ML enable seamless automation of network management to reduce operational costs and enhance user experience. In this paper, we discuss the state of the art for ML within mobile edge computing and the advances needed in automating adaptive resource allocation, mobility modeling, security, and energy efficiency for 5G networks.

scholarly journals A Distributed and Context-Aware Task Assignment Mechanism for Collaborative Mobile Edge Computing

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2423 ◽  
Author(s):  
Bo Gu ◽  
Yapeng Chen ◽  
Haijun Liao ◽  
Zhenyu Zhou ◽  
Di Zhang
Keyword(s):  
Energy Consumption ◽  
Task Assignment ◽  
Edge Computing ◽  
Base Stations ◽  
Mobile Edge Computing ◽  
Network Resources ◽  
Delay Sensitive ◽  
Transmission Energy Consumption ◽  
Task Assignment Problem ◽  
Transmission Energy

Mobile edge computing (MEC) is an emerging technology that leverages computing, storage, and network resources deployed at the proximity of users to offload their delay-sensitive tasks. Various existing facilities including mobile devices with idle resources, vehicles, and MEC servers deployed at base stations or road side units, could act as edges in the network. Since task offloading incurs extra transmission energy consumption and transmission latency, two key questions to be addressed in such an environment are (i) should the workload be offloaded to the edge or computed in terminals? (ii) Which edge, among the available ones, should the task be offloaded to? In this paper, we formulate the task assignment problem as a one-to-many matching game which is a powerful tool for studying the formation of a mutual beneficial relationship between two sets of agents. The main goal of our task assignment mechanism design is to reduce overall energy consumption, while satisfying task owners’ heterogeneous delay requirements and supporting good scalability. An intensive simulation is conducted to evaluate the efficiency of our proposed mechanism.

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