Internet DRAFT - draft-hongcs-t2trg-rsvwm
draft-hongcs-t2trg-rsvwm
T2TRG Hong, Choong Seon
Internet-Draft Kyung Hee University
Intended status: Standards Track Tun, Yan Kyaw
Expires: December 09, 2022 Kyung Hee University
Shashi Raj Pandey
Kyung Hee University
Kim, Ki Tae
Kyung Hee University
Kang, Seok Won
Kyung Hee University
October 2020
Resource Sharing in Virtualized Wireless Networks:
A Two-Layer Game Approach
draft-hongcs-t2trg-rsvwm-00
Abstract
Wireless network virtualization is one of the auspicious
approach to address the increasing
demand of mobile data services. It enables logically decoupling
the traditional cellular network into infrastructure providers
(InPs) and mobile virtual network operators (MVNOs). It also
offers a virtualized wireless network (VWN), efficient resource
utilization, and isolation between network slices (i.e., MVNOs). In
this paper, we consider wireless network slicing for
a single InP
who owns radio resources and multiple MVNOs who need radio
resources to provide specific services to their mobile users. One
of the challenges in wireless network slicing is how to
efficiently allocate the limited radio resources available at the
InP to the MVNOs. In this paper, we address
the problem of efficient allocation of the InP radio
resources to the MVNOs which aims to maximize the
total network capacity of the InP. To this end,
we decompose our considered problem into two phases. The
first phase is an efficient allocation of the InP radio
resources (bandwidth) to the MVNOs, and the second phase
is an optimal allocation of MVNOs resources gained from
the InP to their mobile users. We then propose
a Generalized Kelly Mechanism framework and the Karush-Kuhn-
Tucker (KKT) conditions to solve the first and the
second phase of our resource allocation problem, respectively.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . .. . . . . . 3
2. Main Idea . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3. IANA Considerations . . . . . . .. . . . . . . . . . . . . . . 5
4. Security Considerations . . . . . . . . . . . . . . . . . . .5
5. References . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
5.1. Normative References . . . . . . . . . . . . . . . . . . . 6
5.2. Informative References . . . . . . . . . . . . . . . . . . 6
Authors' Addresses . . . . . . . . . . . . . . . . . . . . .. . . . 7
1. Introduction
Mobile network operators (MNOs) reduce the capital expenditure
(CAPEX) and operational expenditure (OPEX) by slicing physical radio
resources and base station (BS) hardware [a].
It is also a promising approach to address the issues
associated with the increasing mobile data traffic. In wireless
network slicing, the traditional wireless cellular nework is
decoupled into infrastructure providers (InPs) and mobile
virtual network operators (MVNOs). An InP owns the physical
infrastructure (e.g., base stations, physical resource blocks
(RBs), cell sites) and operates the physical wireless network.
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MVNOs will lease these infrastructure and physical resources
from multiple InPs to create their own virtual networks for
providing specific services to their end mobile users.
Slicing of a wireless network enables coexistence of MVNOs
on a shared physical infrastructure with a flexible network
operation [a]. Although wireless network slicing is a promising technique
for future wireless networks, several challenges such as efficient
resource allocation, inter-isolation, and intra-isolation, control signaling,
mobility management, network management need to be addressed before
deploying network slicing [b]. The biggest challenge in network
slicing is how to efficiently allocate resources such as
physical resource blocks (RBs), bandwidth and transmission power [c].
Efficient resource allocation in wireless network slicing will help
improve resource utilization, and quality of services of each
user, and can avoid interference among different MVNOs.
2. Main Idea
A virtualized wireless network model with an InP that
deploys a base station (BS) operating on the total system
bandwidth R, and a set of MVNOs M where
each MVNO is providing the specific mobile services to
its mobile users. In this virtualized wireless network model,
MVNOs will lease fraction of bandwidth from the InP
to provide services and satisfy the QoS requirement of
their mobile users. At BS, the InP deploys a
hypervisor to virtualize physical resource (eg., bandwidth and power)
for leasing to MVNOs. Here, how the InP will
virtualize its wireless bandwidth among multiple
MVNOs to provide services to their mobile users becomes the
central question. Because it is not possible for the InP
to get direct access of user information: channel
state information (CSI), and QoS requirements. Therefore, a
workable solution would be to allocate bandwidth to MVNOs
first, and then each MVNO can allocate the wireless
bandwidth for its user to provide specific services. This
solution approach is viewed as a two-stage solution approach.
In this work, the bandwidth allocation problem in WVN is
decomposed into two phases. In the first phase, the
bandwidth allocation among different MVNOs is decided by the
InP so that it can maximize the overall network
utility. In the second phase, each MVNO strategically allocates
the obtained proportion of bandwidth from the InP to
its mobile users to maximize its own utility.
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The first phase of the resource allocation problem is
modeled as a Generalized Kelly Mechanism (GKM) [d].
GKM, also known as proportional allocation, is a
kind of auction
that can reduce system complexity and signaling between buyers
and a seller. In proportional allocation, the bidders will
submit bidding values to obtain some items from the auctioneer.
The auctioneer then decides the allocation of those items
amongst the bidders which will maximize the sum of
the valuation of all bidders.
In other words, it will use an allocation strategy
to maximize the social welfare of the system.
Correspondingly, in our model, we consider MVNOs as
bidders and the InP as an auctioneer for bandwidth.
Each MVNO will report its own bidding value to
the InP in each allocation round. Depending on the
bidding values of all MVNOs, the InP proportionally allocate
wireless bandwidth to the MVNOs. The bandwidth allocation amongst
MVNOs is straightforward when an InP knows the valuation
of MVNOs. However, the valuation function of each MVNO
is a private information related with the dynamic channel
conditions of its users.
In the second stage, each MVNO will allocate the resource
it gained from the InP to it mobile users
in order to satisfy the QoS requirement of each
mobile users. Then, Karush-Kuhn-Tuckers conditions are deployed
in order to solve the resource allocaiton problem
in the second stage.
3. IANA Considerations
There are no IANA considerations related to this document.
4. Security Considerations
There are no security considerations related to this document.
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5. References
5.1. Normative References
[a] C. Liang and F. R. Yu, Wireless network
virtualization: A survey, some research issues and
challenges, IEEE Communications Surveys & Tutorials,
vol. 17, no. 1, pp. 358--380, 2015.
[b] R. Kokku, R. Mahindra, H. Zhang, and S. Rangarajan,
NVS: A substrate for virtualizing wireless resources
in cellular networks, IEEE/ACM Transactions on Networking,
vol. 20, no. 5, pp. 1333--1346, 2012.
[c] A. Haider, R. Potter, and A. Nakao,
Challenges in resource allocationin network virtualization,
in 20th ITC Specialist Seminar,
vol. 18, no. 2009, 2009.
[d] Y. Zaki, L. Zhao, C. Goerg, and A. Timm-Giel,
A novel LTE wireless virtualization framework, in Proc.
International Conference on Mobile Networks and Management,
2010, pp. 245--257.
3.2. Informative References
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Authors' Addresses
Choong Seon Hong
Computer Science and Engineering Department, Kyung Hee University
Yongin, South Korea
Phone: +82 (0)31 201 2532
Email: cshong@khu.ac.kr
Yan Kyaw Tun
Computer Science and Engineering Department, Kyung Hee University
Yongin, South Korea
Phone: +82 (010) 4068 8863
Email: ykyawtun7@khu.ac.kr
Shashi Raj Pandey
Computer Science and Engineering Department, Kyung Hee University
Yongin, South Korea
Phone: +82 (010) 3855 8816
Email: shashiraj@khu.ac.kr
Ki Tae Kim
Computer Science and Engineering Department, Kyung Hee University
Yongin, South Korea
Phone: +82 (010) 9189 1551
Email: glideslope@khu.ac.kr
Seok Won Kang
Computer Science and Engineering Department, Kyung Hee University
Yongin, South Korea
Phone: +82 (010) 7184 6634
Email: dudtntdud@khu.ac.kr
