Internet DRAFT - draft-multi-dimensional-resource-aggregation
draft-multi-dimensional-resource-aggregation
Cross Stratum Optimization Research Group H. Yang
Internet-Draft YQ. Liu
Intended status: Informational J. Zhang
Expires: May 1, 2020 Beijing University of Posts and Telecommunications
Y. Ao
Y. Qiuyan
Beijing University of Posts and Telecommunications.
October 29, 2019
Multi-dimensional Resource Aggregation in 5G Optical Fronthaul Networks
draft-multi-dimensional-resource-aggregation-02
Abstract
We propose a resource assignment scheme based on multi-dimensional
resource aggregation in 5G optical fronthaul networks. This new
scheme can suit to the higher demand of flexible resource allocation
of the fronthaul in the new 5G scenario.
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. 5G FRONTHAUL MODEL . . . . . . . . . . . . . . . . . . . . . 3
3. Multi-dimensional RESOURCE aggregation ALGORITHM . . . . . . 5
3.1. SIMULATION AND RESULTS . . . . . . . . . . . . . . . . . 7
4. CONCLUSION . . . . . . . . . . . . . . . . . . . . . . . . . 8
5. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 9
6. Informative References . . . . . . . . . . . . . . . . . . . 9
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 9
1. Introduction
With the development of computer technology, the application of 5G
technology has become more and more extensive. For its ultra-high
transmission rate and huge data capacity, 5G technology has made
great achievements in our daily life and work. The future 5G network
will integrate artificial intelligence, SDN, NFV, and cloud computing
technologies to adapt to more and more complex application scenarios.
The 5G network architecture is totally different from the 4G network.
The application of cloud technology has emerged in the 5G network
architecture. In the traditional C-RAN, all the base station
computing resources are aggregated into the BBU pool, and distributed
radio frequency signals are collected by RRH[1][2]. Parts of the 5G
network are centralized into several clouds according to their
separate functions which are controlled to form the "three clouds"
architecture of the 5G network. The access cloud supports multiple
wireless access modes, including converged centralized and
distributed. It is able to be adaptable in various backhaul links
and increase flexibility in the whole network. The control cloud is
used to achieve local and global session control and realize the
mobility management and QOS. It also builds an open interface for
business-oriented network capabilities. The transmit cloud improves
the reliability and reduces the latency of the whole network. It
also achieves efficient transmission of massive traffic data flow
under the control of the control cloud [3]. Moreover, compared with
the 4G network architecture, the 5G architecture separates the base
station processing unit, and reconstructs the BBU unit according to
the real-time nature of the processing content into two functional
entities which are CU and DU. The CU is mainly responsible for the
deployment of some core network functions sinking and edge
application services. The DU mainly handles the functions of the
physical layer and real-time requirements. The original BBU baseband
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function is moved up to the AAU to reduce the transmission bandwidth
between the DU and the RRU. Centralized deployment of CUs can
facilitate flexible resource allocation [4].
Based on the situation where the networking is dense, the resource
allocation is complex and diverse under the background of 5G network
and there are many allocation schemes which have been proposed. We
can use mobile cloud computing (MCC) technology to achieve joint
energy minimization [5]. From the perspective of cross-layer
resource allocation, we can consider this question as a mixed integer
nonlinear programming (MINLP), jointly consider elastic service
scaling, RRH selection and Combine beamforming, and optimize it with
a pruning algorithm. However, this greatly increases the complexity
of the algorithm and reduces the timeliness of resource allocation
[6]. Also, there is hybrid coordinated multi-point transmission
scheme (H-COMP) for downlink transmission between C-RAN and FUN-LLS
[7]. They can all improve the efficiency of resource allocation and
suggest the idea of ??joint scheduling, but they ignored the
separating and sinking 5G-RAN structure.
It becomes an important issue that we should use resources
efficiently as the 5G network architecture changes and the
application scenarios are more complex. In this paper, we have a
more detailed division of the resources in the 5G scenario. In the
second section, we define the functional model of 5G resource
allocation. In the third section, we propose a resource allocation
algorithm which adapts to the new requirements of the new scenario.
In the fourth section, we perform the simulation and obtain the
results. Finally, we will analyze the results and make out the
conclusions.
2. 5G FRONTHAUL MODEL
The 5G Wireless Access Network (RAN) is expected to increase the
number of access users while reducing latency to handle more and more
connected devices and data rates[8]. In the 5G RAN architecture, the
AAU (Active Antenna Processing Unit) includes some physical units of
the formal RRH, BBU, and transmits radio frequency signals to the DU.
The signal transmission of this part is defined as the transmission
in 5G fronthaul. Due to the separation of the BBU (base station
processing unit) in the 5G network, the CU which processes the
virtual resource and the DU which processes the physical layer
function are logically independent. So the resource transmission
between DU and AAU can be separately analyzed and optimized.
According to the 5G fronthaul network architecture, resources can be
divided into three levels: DU resources, AAU resources, and
transmission resources. Thus we can optimize resources allocation in
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these three levels .From the view of form, the transmission resource
and the computing resource span the transmission layer and the DU
processing layer in the horizontal direction. In terms of the
capacity ability, the multi-layer structure and networking are
working in the vertical direction, which is shown in Figure.1. Based
on this virtual mode, a 5G fronthaul network functional architecture
can be proposed. According to the classified resource types, the DU
controller DC, the AAU controller AC, and the transmission controller
TC are respectively used to control each part.
The AC (AAU controller) is used to control the allocation of AAU
resources. It can acquire and manage virtual radio resources and
perform radio frequency allocation on them. The DC (DU controller)
is used to control and obtain the DU resource information through
external triggers and interact with the TC. The TC (transfer
controller) is used to control the transmission resource. When the
service request arrives, the TC performs the resource estimation
algorithm on the DU, the AAU, and the transmission resource, and
performs resource allocation according to the algorithm result. (As
is demonstrated in Figure.2).
-----------------------------------------
| ---------- |
| | AAU | |
| ---------- |
| | |
| ---------- |
| | WDM | |
| ---------- |
| | |
| ------ ---------- ------- |
| | DU |--| TRAMSFER |--| DU | |
| ------ ---------- ------- |
| |
-----------------------------------------
Fig.1 5G network architecture
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-----------------------------------------------------------------------------
| AAU ----------- ------------- ----------- |
| | AAU |-------| AAU |-------| AAU | |
| CONTROLLER |ALLOCATION | | MONITORING | | MODEL | |
| ----------- ------------- ----------- |
| | |
--------------------|--------------------------------------------------------
--------------------|--------------------------------------------------------
| TRANSFER ----------- ------------- ----------- |
| | TRANSFER |-------| PCE+ |--------| DBM | |
| CONTROLLER | CONTROL | | OPENFLOW | | | |
| ----------- ------------- | | |
| | | | | |
| ----------- ------------- | | |
| | CSO | | RAA |--------| | |
| ----------- ------------- ----------- |
--------------------|--------------------------------------------------------
--------------------|--------------------------------------------------------
| DU ----------- ------------- ----------- |
| CONTROLLER |CSO AGENT |-------|DU MONITORING|--------| DU MODEL | |
| ----------- ------------- ----------- |
-----------------------------------------------------------------------------
Fig.2 5G function model
3. Multi-dimensional RESOURCE aggregation ALGORITHM
Considering the resource allocation in the 5G application scenario,
we use AAU, DU, and transmission resources to optimize multi-layer
resources. Compared with the traditional situation where only one
resource model optimization is considered to evaluate resource
utilization, the resource allocation scheme in 4G context is no
longer applicable to 5G technology scenarios. Based on the proposed
functional architecture, we design a resource allocation algorithm
for 5G scenarios.
First, the node is defined and expressed as G (A, A', R, R', T, T',
C) according to the functional architecture mentioned above. Here, A
= {a1, a2, ... an} and A' = {a1', a2', ... an'} represent a
collection of AAU transmission nodes. R = {r1, r2, ... rn} and R' =
{r1', r2', ... rn'} represent a bidirectional transmission link group
between A and A'. T = {t1, t2, ... tn} and T' = {t1',t2', ... tn'}
represent the set of spectra on each link. Also, A, A', R, R', T,
T', and C represent the number of all types of nodes. For DU
resources, two time -varying- processing parameters are used to
describe and represent the case of resource utilization, including
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the resource storage rate U0 and CPU memory usage U1. In addition,
the transmission layer parameters include the candidate path hop
count H and the weight W of each link occupied bandwidth. The AU
processing layer parameters include the symbol rate Br and the radio
frequency Fr. DU is used to provide storage capacity and computing
resources.
We denote a request as SRi(S, B, U0, U1) according to its attributes.
B denotes the bandwidth. The resource allocation algorithm selects
the corresponding path and DU according to the state parameters
acquired by the DU, the states of the AC, and the TC. In order to
comprehensively consider the resource scheduling of all the three
levels of DU, AAU, and transport layer, a resource allocation factor
?? is used to jointly allocate the resources of these three
dimensions. For the DU layer, two parameters U0 and U1 are used to
describe the current resource usage of the DU part, and a
normalization factor ?? is used to coordinate the storage utilization
and CPU usage in the DU layer, which is shown in formula (1). In the
case of the transport layer, the traffic weights W and the candidate
path hop count H are used to indicate the load balance of the
transmission link. For the bearer link, the larger the traffic
weight is, the smaller the link redundancy of the barer space is.
Therefore, the traffic should be selected. A link with a small
weight is better as expressed in formula (2). For the AAU layer, the
radio frequency spectrum resources and symbol rate occupancy should
be considered. Considering the symbol parameter Fr and the radio
frequency parameter Br, since the radio frequency is negatively
correlated with the carrying capacity, the AAU layer resource is
represented by the formula (3). DU parameters, transmission
parameters, and AAU parameters are represented by fa, fb, and fc,
respectively.
The nodes with the smallest processing function in the DU, AAU, and
transmission layer are respectively represented as Fa, Fb, Fc. And
the two resource coordination factors of ?? and ?? are combined to
perform multi-layer resources which are normalized by Fa, Fb, and Fc.
The normalization process is expressed as equation (4). When the
minimum value is obtained according to ??, the most appropriate path
and node are selected, and corresponding resource allocation is
performed.
The relevant algorithm flowchart is given in Figure3. First, we
obtain the relevant resource utilization of each layer of the input
service request SRi. Then we use it to calculate the parameters of
each layer and get the resource allocation parameter ??. And
additionally, we compare all the parameters and find the minute one.
Finally, find the path and node corresponding to the min ?? and
perform radio frequency allocation.
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3.1. SIMULATION AND RESULTS
In order to test the optimization of the resource allocation of the
scheme and verify its efficiency, we also made several comparisons
between the proposed algorithm and the traditional one. The
traditional way for resource allocation optimizes the processing of
spectrum resources based on the virtualization of network functions.
It combines both the centralized and distributed elements. It can
also independently develop centralized control platforms, such as
virtualization and sectioning of network[9]. They use network
throughput as the optimization goal and consider the use of only one
certain resource in a single way. They do not refine the resources
according to the difference of user services and the architecture of
network development.
Based on the software test platform, we build a simulation model. We
use the Open vSwitch proxy controller to control the interaction
between the nodes. In the 5G fronthaul, the heavy traffic load is
from 40 Erlang to 150 Erlang. For the proposed model and the
Openflow-based control platform, three virtual machine deployment
planes are used: the TC server supports the interaction between AC
and DC. The DC server is used to acquire and supervise the DU
computing resources. The AC server obtains the radio distribution.
On the established platform, the optimization of the proposed
solution is demonstrated by testing the resource occupancy rate and
path provision latency of the server. Based on the proposed resource
allocation algorithm, the preset weight ?? is set to 50%, so that the
CPU occupancy rate and the resource storage rate occupy the same
proportion, and then the preset weights ??, ?? are set to 33.33%, so
that the resources occupy of the three layers can gain the same
weight. And the CPU storage rate occupied by each service is
randomly allocated between 0 and 1%. When the request reaches, the
best path and node will be calculated according to the formula, and
the corresponding RF resources will be provided. Then we obtain the
relevant indicators and compare them. In order to get the
optimization of time precision, we compared the path provision
latency between our way and the traditional way. And the results are
shown in Figure.4 where GES represents the scheme above and CSO
represents the traditional one. What??s more, in order to obtain the
resource utilization of the proposed method, we also compared the
resource occupation rate between those two ways. The experiments
proved that the scheme we proposed could improve the efficiency of
resource allocation. The path provision latency is lower and the
resource occupation rate is higher. It means that this solution has
many advantages for 5G fronthaul resource allocation and can improve
the flexibility of the whole network.
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+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
| | path provision |
| Traffic load +-+-+-+-+-+-+-+-+-+-+-+-+-+
| | CSO | GES |
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
| 40 | 29.1 | 25.7 |
| 60 | 32.7 | 27.3 |
| 80 | 35.1 | 28.8 |
| 100 | 36.6 | 32.7 |
| 120 | 42.5 | 38.0 |
| 140 | 49.4 | 43.3 |
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
Tab.1 path provision of two strategies
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
| |resource occupation rate |
| | path provision |
| Traffic load +-+-+-+-+-+-+-+-+-+-+-+-+-+
| | CSO | GES |
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
| 40 | 0.05 | 0.06 |
| 60 | 0.11 | 0.14 |
| 80 | 0.19 | 0.23 |
| 100 | 0.32 | 0.37 |
| 120 | 0.40 | 0.50 |
| 140 | 0.51 | 0.58 |
+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+
Tab.2 resource occupation rate of two strategies
4. CONCLUSION
In summary, this paper considers the resource allocation requirements
in the 5G technology scenario. According to the changes of the 5G
network architecture and the multiple use of resources, we
redistribute the resources and propose the corresponding functional
models. It is used to adopt a resource allocation algorithm to
optimize the resource allocation of each layer and realize the joint
deploy and utilization of multi-layer resources. In the traditional
resource allocation model, we used to consider the utilization of
only one certain type of resource. This solution realizes the global
deployment of 5G fronthaul resources, which is able to improve the
flexibility of the 5G fronthaul network.
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5. Acknowledgments
This work has been supported in part by NSFC project (61501049),
Fundamental Research Funds for the Central Universities (2018XKJC06)
and State Key Laboratory of Information Photonics and Optical
Communications (BUPT), P. R. China (No. IPOC2017ZT11).
6. Informative References
[Ref1] Yang, Hui., Zhang, Jie., and Yongli. Zhao, "CSO: Cross
Stratum Optimization for Optical as a Service", Aug 2015.
[Ref2] Yang, H. and Jie. Zhang, "Experimental demonstration of
multi-dimensional resources integration for service
provisioning in cloud radio over fiber network", 2016.
[Ref3] Yao, Li., "Joint Optimization of BBU Pool Allocation and
Selection for C-RAN Networks", 2018.
[Ref4] Ramon, Casellas., "Control, Management, and Orchestration
of Optical Networks: Evolution, Trends, and Challenges",
2018.
Authors' Addresses
Hui Yang
Beijing University of Posts and Telecommunications
No.10,Xitucheng Road,Haidian District
Beijing 100876
P.R.China
Phone: +8613466774108
Email: yang.hui.y@126.com
URI: http://www.bupt.edu.cn/
Yiqian Liu
Beijing University of Posts and Telecommunications
No.10,Xitucheng Road,Haidian District
Beijing 100876
P.R.China
Phone: +8613177087617
Email: 497706153@qq.com
URI: http://www.bupt.edu.cn/
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Jie Zhang
Beijing University of Posts and Telecommunications
No.10,Xitucheng Road,Haidian District
Beijing 100876
P.R.China
Phone: +8613911060930
Email: lgr24@bupt.edu.cn
URI: http://www.bupt.edu.cn/
Ao Yu
Beijing University of Posts and Telecommunications.
No.10,Xitucheng Road,Haidian District
Beijing 100876
P.R.China
Email: yuaoupc@163.com
URI: http://www.bupt.edu.cn/
Qiuyan Yao
Beijing University of Posts and Telecommunications.
No.10,Xitucheng Road,Haidian District
Beijing 100876
P.R.China
Email: yqy86716@126.com
URI: http://www.bupt.edu.cn/
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