Delay-Energy Aware Task Offloading and VM Migration Policy for Mobile Edge Computing

2021 ◽  
Author(s):  
Vaishali Joshi ◽  
Kishor Patil
Keyword(s):  
Migration Policy ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Energy Aware ◽  
Vm Migration ◽  
Task Offloading

scholarly journals Energy-aware task offloading with deadline constraint in mobile edge computing

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Zhongjin Li ◽  
Victor Chang ◽  
Jidong Ge ◽  
Linxuan Pan ◽  
Haiyang Hu ◽  
...  
Keyword(s):  
Performance Metrics ◽  
High Energy ◽  
Traffic Monitoring ◽  
Edge Computing ◽  
Action Space ◽  
Mobile Edge Computing ◽  
Energy Aware ◽  
Space Problem ◽  
Deadline Constraint ◽  
Task Offloading

AbstractWith the development of the wireless network, increasing mobile applications are emerging and receiving great popularity. These applications cover a wide area, such as traffic monitoring, smart homes, real-time vision processing, objective tracking, and so on, and typically require computation-intensive resources to achieve a high quality of experience. Although the performance of mobile devices (MDs) has been continuously enhanced, running all the applications on a single MD still causes high energy consumption and latency. Fortunately, mobile edge computing (MEC) allows MDs to offload their computation-intensive tasks to proximal eNodeBs (eNBs) to augment computational capabilities. However, the current task offloading schemes mainly concentrate on average-based performance metrics, failing to meet the deadline constraint of the tasks. Based on the deep reinforcement learning (DRL) approach, this paper proposes an Energy-aware Task Offloading with Deadline constraint (DRL-E2D) algorithm for a multi-eNB MEC environment, which is to maximize the reward under the deadline constraint of the tasks. In terms of the actor-critic framework, we integrate the action representation into DRL-E2D to handle the large discrete action space problem, i.e., using the low-complexity k-nearest neighbor as an approximate approach to extract optimal discrete actions from the continuous action space. The extensive experimental results show that DRL-E2D achieves better performance than the comparison algorithms on all parameter settings, indicating that DRL-E2D is robust to the state changes in the MEC environment.

scholarly journals Pricing-Driven Service Caching and Task Offloading in Mobile Edge Computing

2021 ◽  
pp. 1-1
Author(s):  
Jia Yan ◽  
Suzhi Bi ◽  
Lingjie Duan ◽  
Ying-Jun Angela Zhang
Keyword(s):  
Edge Computing ◽  
Mobile Edge Computing ◽  
And Task ◽  
Task Offloading

scholarly journals Distributed Task Offloading Optimization with Queueing Dynamics in Multi-Agent Mobile-Edge Computing Networks

2021 ◽  
pp. 1-1
Author(s):  
Jianshan Zhou ◽  
Daxin Tian ◽  
Zhengguo Sheng ◽  
Xuting Duan ◽  
Xuemin Shen
Keyword(s):  
Edge Computing ◽  
Mobile Edge Computing ◽  
Multi Agent ◽  
Task Offloading

A Novel Framework for Mobile Edge Computing By Optimizing Task Offloading

2021 ◽  
pp. 1-1
Author(s):  
Naouri Abdenacer ◽  
Hangxing Wu ◽  
Nouri Nabil Abdelkader ◽  
Sahraoui Dhelim ◽  
Huansheng Ning
Keyword(s):  
Edge Computing ◽  
Mobile Edge Computing ◽  
Task Offloading

Joint wireless power transfer and task offloading in mobile edge computing: a survey

2021 ◽  
Author(s):  
Ehzaz Mustafa ◽  
Junaid Shuja ◽  
S. Khaliq uz Zaman ◽  
Ali Imran Jehangiri ◽  
Sadia Din ◽  
...  
Keyword(s):  
Wireless Power Transfer ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Power Transfer ◽  
Wireless Power ◽  
And Task ◽  
Task Offloading
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