Abstract
Several task forces are currently working on how to design the future Internet and it is high time for research work to also move a step forward to future mobile networks on a large scale. In this article, we propose a future mobile network management method based on a combination of OpenFlow and the biologically inspired attractor selection method to achieve scalability and energy efficiency. In other words, we propose novel approaches to wireless network management by extending the attractor selection mechanism in path and cluster management for signaling cost reduction. First, in path management, we establish a control method that each mobile node selects the best suited interfaces in accordance with instantaneous live traffic volume. Then, in cluster management, we design a network management method that network devices select the best OpenFlow cluster to join in order to reduce handover signaling cost. Through autonomous decisions of each mobile node and network device, the whole wireless network can be managed in an autonomous, energy efficient, and robust manner.
Highlights
We are facing a new era when the future Internet infrastructure needs to be drastically changed from scratch in order to meet the great variety of requirements from users
Proposed extension of attractor selection model we propose a future mobile network architecture taking into account both the flexibility of OpenFlow and the robustness of attractor selection
This M sequence is categorized as Pseudo Noise (PN) code due to its code generation approach
Summary
We are facing a new era when the future Internet infrastructure needs to be drastically changed from scratch in order to meet the great variety of requirements from users. We extend the above attractor selection mechanism in order to work on large-scale mobile wireless network environments based on OpenFlow technology. We establish an appropriate clustering method to reduce handover signaling cost on OpenFlow-based future mobile networks with attractor selection driven by the difference of the flow directions between user traffic and signaling traffic. The mixture of different access technologies is most likely to produce more and more opportunities for both vertical and horizontal handovers to cause large amount of signaling traffic between OFSs and OFCs. We would like to reduce the above signaling cost caused by handovers and utilize attractor selection in adaptively sustaining appropriate clusters according to environmental dynamics from the viewpoint of signaling cost reduction. According to the output of these calculation results, the best radio interface is selected (Step 5) and it is maintained by iteration of this cycle This Yuragi-based attractor selection equation has two features at the same time.
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