This paper describes a unifying optimization framework to share backhaul network resources across different operators and wireless platforms. The architecture we consider, named LayBack, requires introducing a unifying Software Defined Network (SDN) orchestrator, sited where their respective traffic streams meet: at the wireless network backhaul. The work we present proposes a scalable decomposition of the resource allocation problem across different layers and time-scales.
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A Multi-Layer Multi-Timescale Network Utility Maximization Framework for the SDN-Based LayBack Architecture Enabling Wireless Backhaul Resource Sharing
With the emergence of small cell networks and fifth-generation (5G) wireless networks, the backhaul becomes increasingly complex. This study addresses the problem of how a central SDN orchestrator can flexibly share the total backhaul capacity of the various wireless operators among their gateways and radio nodes (e.g., LTE enhanced Node Bs or Wi-Fi access points). In order to address this backhaul resource allocation problem, we introduce a novel backhaul optimization methodology in the context of the recently proposed LayBack SDN backhaul architecture. In particular, we explore the decomposition of the central optimization problem into a layered dual decomposition model that matches the architectural layers of the LayBack backhaul architecture. In order to promote scalability and responsiveness, we employ different timescales, i.e., fast timescales at the radio nodes and slower timescales in the higher LayBack layers that are closer to the central SDN orchestrator. We numerically evaluate the scalable layered optimization for a specific case of the LayBack backhaul architecture with four layers, namely a radio node (eNB) layer, a gateway layer, an operator layer, and central coordination in an SDN orchestrator layer. The coordinated sharing of the total backhaul capacity among multiple operators lowers the queue lengths compared to the conventional backhaul without sharing among operators.
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- Award ID(s):
- 1716121
- NSF-PAR ID:
- 10174860
- Date Published:
- Journal Name:
- Electronics
- Volume:
- 8
- Issue:
- 9
- ISSN:
- 2079-9292
- Page Range / eLocation ID:
- 937
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Availability and implementation Source code and datasets are available at http://snap.stanford.edu/ohmnet.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/electronics8090937
