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Cross Stratum Optimization Research Group                        H. Yang
Internet-Draft                                                   YQ. Liu
Intended status: Informational                                  J. Zhang
Expires: May 9, 2019                                               A. Yu
                                                                 QY. Yao
                      Beijing University of Posts and Telecommunications
                                                        November 5, 2018


Multi-dimensional Resource Aggregation in 5G Optical Fronthaul Networks
            draft-multi-dimensional-resource-aggregation-01

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.

Status of This Memo

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   This Internet-Draft will expire on May 9, 2019.

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   Copyright (c) 2018 IETF Trust and the persons identified as the
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   the Trust Legal Provisions and are provided without warranty as
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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??s 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
   function is moved up to the AAU to reduce the transmission bandwidth



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   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
   these three levels .From the view of form, the transmission resource



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   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, H., Zhang, J., and YL. Zhao, "CSO: Cross Stratum
              Optimization for Optical as a Service", Aug 2015.

   [Ref2]     Yang, H. and J. Zhang, "Experimental demonstration of
              multi-dimensional resources integration for service
              provisioning in cloud radio over fiber network", 2016.

   [Ref3]     Yao, L., "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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