Migration Modeling and Learning Algorithms for Containers in Fog Computing

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Migration Modeling and Learning Algorithms for Containers in Fog Computing

Migration Modeling and Learning Algorithms for Containers in Fog Computing

Abstract

Fog Computing (FC) is a flexible architecture to support cloud-like service quality distributed domain-specific applications. Migration Modeling and Learning Algorithms for Containers in Fog Computing However, when facing many mobile users with diversified application quality requirements, the current FC still lacks the mobility support mechanism.

Migration Modeling and Learning Algorithms for Containers in Fog Computing Such a mobility support mechanism can be critical, as in the industrial internet where humans, products, and devices are mobile.To fill in such gaps, we propose in this paper novel container migration algorithms and architecture to support mobility tasks with different application requirements.

System Configuration

H/W System Configuration
Speed                   : 1.1 GHz
RAM                      : 256 MB(min)
Hard Disk              : 20 GB
Floppy Drive          : 1.44 MB
Key Board             : Standard Windows Keyboard
Mouse                  : Two or Three Button Mouse
Monitor                : SVGA
S/W System Configuration

Platform                     :  cloud computing

Operating system       : Windows Xp,7,
Server                       : WAMP/Apache
Working on                : Browser Like Firefox, IE

Conclusion

In this paper, as a large-scale MDP problem, we have modeled the container migration problem of mobile application tasks in FC. We first define the system model whose cost function consists of delay, power consumption, and cost of migration. Then we proposed the deep container migration algorithms based on Q-learning. In Q-network update, we optimize domaction selection in exploration and DNN training strategy to achieve rapid decision-making.