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Virtual Network Embedding for Collaborative Edge Computing in Optical-Wireless Networks

Gong, Xiaoxue ; Guo, Lei ; Shen, Gangxiang and Tian, Guoda LU (2017) In Journal of Lightwave Technology 35(18). p.3980-3990
Abstract

As an open integrated environment deployed with wired and wireless infrastructures, the smart city heavily relies on the wireless-optical broadband access network. Smart home data are usually sent to neighbor optical network units (ONUs) through front-end wireless mesh networks (WMNs) and finally reach the optical line terminal (OLT) for decision making via the passive optical network (PON) backhaul. To reduce backhaul bandwidth saturated by this conventional approach, smart edge devices (EDs) should be deployed at sensors and ONUs so that collaborative edge computing can be performed in front-end WMNs. Moreover, the cooperation of EDs at different ONUs is also promising for computing tasks that cannot be handled within front-end WMNs... (More)

As an open integrated environment deployed with wired and wireless infrastructures, the smart city heavily relies on the wireless-optical broadband access network. Smart home data are usually sent to neighbor optical network units (ONUs) through front-end wireless mesh networks (WMNs) and finally reach the optical line terminal (OLT) for decision making via the passive optical network (PON) backhaul. To reduce backhaul bandwidth saturated by this conventional approach, smart edge devices (EDs) should be deployed at sensors and ONUs so that collaborative edge computing can be performed in front-end WMNs. Moreover, the cooperation of EDs at different ONUs is also promising for computing tasks that cannot be handled within front-end WMNs due to the local bottleneck, leading to collaborative edge computing in the PON backhaul. In this paper, network virtualization is utilized to support the coordination of computing and network resources. We also describe the relationship between virtual networks and requirements of computing tasks for substrate resources. First, a graph-cutting algorithm is employed to embed as many virtual networks as possible onto the common network infrastructure in front-end WMNs, aiming at minimizing the total transmitting power. Next, we transform impossibly embedded virtual networks into new ones that must be processed through the PON backhaul where the wavelength consumption will be optimized. Simulations results demonstrate that 1) the total transmitting power assigned for nodes is effectively reduced using the graph-cutting algorithm if all computing tasks can be solved by front-end WMNs; 2) otherwise, our method accepts more virtual networks with the improvement ratio of 77%, through the PON backhaul. In addition, there is a good match between the algorithm result and the optimal number of consumed wavelengths per optical fiber cable.

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Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Collaborative edge computing, smart city, virtual network embedding, wireless-optical broadband access network (WOBAN)
in
Journal of Lightwave Technology
volume
35
issue
18
article number
7924315
pages
11 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:85029852157
ISSN
0733-8724
DOI
10.1109/JLT.2017.2703311
language
English
LU publication?
yes
id
6560cf62-dd8b-493b-839c-556d0bbd4872
date added to LUP
2022-04-01 15:25:50
date last changed
2022-04-25 00:08:13
@article{6560cf62-dd8b-493b-839c-556d0bbd4872,
  abstract     = {{<p>As an open integrated environment deployed with wired and wireless infrastructures, the smart city heavily relies on the wireless-optical broadband access network. Smart home data are usually sent to neighbor optical network units (ONUs) through front-end wireless mesh networks (WMNs) and finally reach the optical line terminal (OLT) for decision making via the passive optical network (PON) backhaul. To reduce backhaul bandwidth saturated by this conventional approach, smart edge devices (EDs) should be deployed at sensors and ONUs so that collaborative edge computing can be performed in front-end WMNs. Moreover, the cooperation of EDs at different ONUs is also promising for computing tasks that cannot be handled within front-end WMNs due to the local bottleneck, leading to collaborative edge computing in the PON backhaul. In this paper, network virtualization is utilized to support the coordination of computing and network resources. We also describe the relationship between virtual networks and requirements of computing tasks for substrate resources. First, a graph-cutting algorithm is employed to embed as many virtual networks as possible onto the common network infrastructure in front-end WMNs, aiming at minimizing the total transmitting power. Next, we transform impossibly embedded virtual networks into new ones that must be processed through the PON backhaul where the wavelength consumption will be optimized. Simulations results demonstrate that 1) the total transmitting power assigned for nodes is effectively reduced using the graph-cutting algorithm if all computing tasks can be solved by front-end WMNs; 2) otherwise, our method accepts more virtual networks with the improvement ratio of 77%, through the PON backhaul. In addition, there is a good match between the algorithm result and the optimal number of consumed wavelengths per optical fiber cable.</p>}},
  author       = {{Gong, Xiaoxue and Guo, Lei and Shen, Gangxiang and Tian, Guoda}},
  issn         = {{0733-8724}},
  keywords     = {{Collaborative edge computing; smart city; virtual network embedding; wireless-optical broadband access network (WOBAN)}},
  language     = {{eng}},
  number       = {{18}},
  pages        = {{3980--3990}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{Journal of Lightwave Technology}},
  title        = {{Virtual Network Embedding for Collaborative Edge Computing in Optical-Wireless Networks}},
  url          = {{http://dx.doi.org/10.1109/JLT.2017.2703311}},
  doi          = {{10.1109/JLT.2017.2703311}},
  volume       = {{35}},
  year         = {{2017}},
}