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Run idnits with the --verbose option for more detailed information about the items above. -------------------------------------------------------------------------------- 2 Cross Stratum Optimization Research Group H. Yang 3 Internet-Draft YQ. Liu 4 Intended status: Informational J. Zhang 5 Expires: May 9, 2019 A. Yu 6 QY. Yao 7 Beijing University of Posts and Telecommunications 8 November 5, 2018 10 Multi-dimensional Resource Aggregation in 5G Optical Fronthaul Networks 11 draft-multi-dimensional-resource-aggregation-01 13 Abstract 15 We propose a resource assignment scheme based on multi-dimensional 16 resource aggregation in 5G optical fronthaul networks. This new 17 scheme can suit to the higher demand of flexible resource allocation 18 of the fronthaul in the new 5G scenario. 20 Status of This Memo 22 This Internet-Draft is submitted in full conformance with the 23 provisions of BCP 78 and BCP 79. 25 Internet-Drafts are working documents of the Internet Engineering 26 Task Force (IETF). Note that other groups may also distribute 27 working documents as Internet-Drafts. The list of current Internet- 28 Drafts is at https://datatracker.ietf.org/drafts/current/. 30 Internet-Drafts are draft documents valid for a maximum of six months 31 and may be updated, replaced, or obsoleted by other documents at any 32 time. It is inappropriate to use Internet-Drafts as reference 33 material or to cite them other than as "work in progress." 35 This Internet-Draft will expire on May 9, 2019. 37 Copyright Notice 39 Copyright (c) 2018 IETF Trust and the persons identified as the 40 document authors. All rights reserved. 42 This document is subject to BCP 78 and the IETF Trust's Legal 43 Provisions Relating to IETF Documents 44 (https://trustee.ietf.org/license-info) in effect on the date of 45 publication of this document. Please review these documents 46 carefully, as they describe your rights and restrictions with respect 47 to this document. Code Components extracted from this document must 48 include Simplified BSD License text as described in Section 4.e of 49 the Trust Legal Provisions and are provided without warranty as 50 described in the Simplified BSD License. 52 Table of Contents 54 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2 55 2. 5G FRONTHAUL MODEL . . . . . . . . . . . . . . . . . . . . . 3 56 3. Multi-dimensional RESOURCE aggregation ALGORITHM . . . . . . 5 57 3.1. SIMULATION AND RESULTS . . . . . . . . . . . . . . . . . 7 58 4. CONCLUSION . . . . . . . . . . . . . . . . . . . . . . . . . 8 59 5. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 9 60 6. Informative References . . . . . . . . . . . . . . . . . . . 9 61 Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 9 63 1. Introduction 65 With the development of computer technology, the application of 5G 66 technology has become more and more extensive. For its ultra-high 67 transmission rate and huge data capacity, 5G technology has made 68 great achievements in our daily life and work. The future 5G network 69 will integrate artificial intelligence, SDN, NFV, and cloud computing 70 technologies to adapt to more and more complex application scenarios. 72 The 5G network architecture is totally different from the 4G network. 73 The application of cloud technology has emerged in the 5G network 74 architecture. In the traditional C-RAN, all the base station 75 computing resources are aggregated into the BBU pool, and distributed 76 radio frequency signals are collected by RRH[1][2]. Parts of the 5G 77 network are centralized into several clouds according to their 78 separate functions which are controlled to form the "three clouds" 79 architecture of the 5G network. The access cloud supports multiple 80 wireless access modes, including converged centralized and 81 distributed. It??s able to be adaptable in various backhaul links 82 and increase flexibility in the whole network. The control cloud is 83 used to achieve local and global session control and realize the 84 mobility management and QOS. It also builds an open interface for 85 business-oriented network capabilities. The transmit cloud improves 86 the reliability and reduces the latency of the whole network. It 87 also achieves efficient transmission of massive traffic data flow 88 under the control of the control cloud [3]. Moreover, compared with 89 the 4G network architecture, the 5G architecture separates the base 90 station processing unit, and reconstructs the BBU unit according to 91 the real-time nature of the processing content into two functional 92 entities which are CU and DU. The CU is mainly responsible for the 93 deployment of some core network functions sinking and edge 94 application services. The DU mainly handles the functions of the 95 physical layer and real-time requirements. The original BBU baseband 96 function is moved up to the AAU to reduce the transmission bandwidth 97 between the DU and the RRU. Centralized deployment of CUs can 98 facilitate flexible resource allocation [4]. 100 Based on the situation where the networking is dense, the resource 101 allocation is complex and diverse under the background of 5G network 102 and there are many allocation schemes which have been proposed. We 103 can use mobile cloud computing (MCC) technology to achieve joint 104 energy minimization [5]. From the perspective of cross-layer 105 resource allocation, we can consider this question as a mixed integer 106 nonlinear programming (MINLP), jointly consider elastic service 107 scaling, RRH selection and Combine beamforming, and optimize it with 108 a pruning algorithm. However, this greatly increases the complexity 109 of the algorithm and reduces the timeliness of resource allocation 110 [6]. Also, there is hybrid coordinated multi-point transmission 111 scheme (H-COMP) for downlink transmission between C-RAN and FUN-LLS 112 [7]. They can all improve the efficiency of resource allocation and 113 suggest the idea of ??joint scheduling, but they ignored the 114 separating and sinking 5G-RAN structure. 116 It becomes an important issue that we should use resources 117 efficiently as the 5G network architecture changes and the 118 application scenarios are more complex. In this paper, we have a 119 more detailed division of the resources in the 5G scenario. In the 120 second section, we define the functional model of 5G resource 121 allocation. In the third section, we propose a resource allocation 122 algorithm which adapts to the new requirements of the new scenario. 123 In the fourth section, we perform the simulation and obtain the 124 results. Finally, we will analyze the results and make out the 125 conclusions. 127 2. 5G FRONTHAUL MODEL 129 The 5G Wireless Access Network (RAN) is expected to increase the 130 number of access users while reducing latency to handle more and more 131 connected devices and data rates[8]. In the 5G RAN architecture, the 132 AAU (Active Antenna Processing Unit) includes some physical units of 133 the formal RRH, BBU, and transmits radio frequency signals to the DU. 134 The signal transmission of this part is defined as the transmission 135 in 5G fronthaul. Due to the separation of the BBU (base station 136 processing unit) in the 5G network, the CU which processes the 137 virtual resource and the DU which processes the physical layer 138 function are logically independent. So the resource transmission 139 between DU and AAU can be separately analyzed and optimized. 141 According to the 5G fronthaul network architecture, resources can be 142 divided into three levels: DU resources, AAU resources, and 143 transmission resources. Thus we can optimize resources allocation in 144 these three levels .From the view of form, the transmission resource 145 and the computing resource span the transmission layer and the DU 146 processing layer in the horizontal direction. In terms of the 147 capacity ability, the multi-layer structure and networking are 148 working in the vertical direction, which is shown in Figure.1. Based 149 on this virtual mode, a 5G fronthaul network functional architecture 150 can be proposed. According to the classified resource types, the DU 151 controller DC, the AAU controller AC, and the transmission controller 152 TC are respectively used to control each part. 154 The AC (AAU controller) is used to control the allocation of AAU 155 resources. It can acquire and manage virtual radio resources and 156 perform radio frequency allocation on them. The DC (DU controller) 157 is used to control and obtain the DU resource information through 158 external triggers and interact with the TC. The TC (transfer 159 controller) is used to control the transmission resource. When the 160 service request arrives, the TC performs the resource estimation 161 algorithm on the DU, the AAU, and the transmission resource, and 162 performs resource allocation according to the algorithm result. (As 163 is demonstrated in Figure.2). 165 ----------------------------------------- 166 | ---------- | 167 | | AAU | | 168 | ---------- | 169 | | | 170 | ---------- | 171 | | WDM | | 172 | ---------- | 173 | | | 174 | ------ ---------- ------- | 175 | | DU |--| TRAMSFER |--| DU | | 176 | ------ ---------- ------- | 177 | | 178 ----------------------------------------- 180 Fig.1 5G network architecture 182 ----------------------------------------------------------------------------- 183 | AAU ----------- ------------- ----------- | 184 | | AAU |-------| AAU |-------| AAU | | 185 | CONTROLLER |ALLOCATION | | MONITORING | | MODEL | | 186 | ----------- ------------- ----------- | 187 | | | 188 --------------------|-------------------------------------------------------- 189 --------------------|-------------------------------------------------------- 190 | TRANSFER ----------- ------------- ----------- | 191 | | TRANSFER |-------| PCE+ |--------| DBM | | 192 | CONTROLLER | CONTROL | | OPENFLOW | | | | 193 | ----------- ------------- | | | 194 | | | | | | 195 | ----------- ------------- | | | 196 | | CSO | | RAA |--------| | | 197 | ----------- ------------- ----------- | 198 --------------------|-------------------------------------------------------- 199 --------------------|-------------------------------------------------------- 200 | DU ----------- ------------- ----------- | 201 | CONTROLLER |CSO AGENT |-------|DU MONITORING|--------| DU MODEL | | 202 | ----------- ------------- ----------- | 203 ----------------------------------------------------------------------------- 205 Fig.2 5G function model 207 3. Multi-dimensional RESOURCE aggregation ALGORITHM 209 Considering the resource allocation in the 5G application scenario, 210 we use AAU, DU, and transmission resources to optimize multi-layer 211 resources. Compared with the traditional situation where only one 212 resource model optimization is considered to evaluate resource 213 utilization, the resource allocation scheme in 4G context is no 214 longer applicable to 5G technology scenarios. Based on the proposed 215 functional architecture, we design a resource allocation algorithm 216 for 5G scenarios. 218 First, the node is defined and expressed as G (A, A', R, R', T, T', 219 C) according to the functional architecture mentioned above. Here, A 220 = {a1, a2, ... an} and A' = {a1', a2', ... an'} represent a 221 collection of AAU transmission nodes. R = {r1, r2, ... rn} and R' = 222 {r1', r2', ... rn'} represent a bidirectional transmission link group 223 between A and A'. T = {t1, t2, ... tn} and T' = {t1',t2', ... tn'} 224 represent the set of spectra on each link. Also, A, A', R, R', T, 225 T', and C represent the number of all types of nodes. For DU 226 resources, two time -varying- processing parameters are used to 227 describe and represent the case of resource utilization, including 228 the resource storage rate U0 and CPU memory usage U1. In addition, 229 the transmission layer parameters include the candidate path hop 230 count H and the weight W of each link occupied bandwidth. The AU 231 processing layer parameters include the symbol rate Br and the radio 232 frequency Fr. DU is used to provide storage capacity and computing 233 resources. 235 We denote a request as SRi(S, B, U0, U1) according to its attributes. 236 B denotes the bandwidth. The resource allocation algorithm selects 237 the corresponding path and DU according to the state parameters 238 acquired by the DU, the states of the AC, and the TC. In order to 239 comprehensively consider the resource scheduling of all the three 240 levels of DU, AAU, and transport layer, a resource allocation factor 241 ?? is used to jointly allocate the resources of these three 242 dimensions. For the DU layer, two parameters U0 and U1 are used to 243 describe the current resource usage of the DU part, and a 244 normalization factor ?? is used to coordinate the storage utilization 245 and CPU usage in the DU layer, which is shown in formula (1). In the 246 case of the transport layer, the traffic weights W and the candidate 247 path hop count H are used to indicate the load balance of the 248 transmission link. For the bearer link, the larger the traffic 249 weight is, the smaller the link redundancy of the barer space is. 250 Therefore, the traffic should be selected. A link with a small 251 weight is better as expressed in formula (2). For the AAU layer, the 252 radio frequency spectrum resources and symbol rate occupancy should 253 be considered. Considering the symbol parameter Fr and the radio 254 frequency parameter Br, since the radio frequency is negatively 255 correlated with the carrying capacity, the AAU layer resource is 256 represented by the formula (3). DU parameters, transmission 257 parameters, and AAU parameters are represented by fa, fb, and fc, 258 respectively. 260 The nodes with the smallest processing function in the DU, AAU, and 261 transmission layer are respectively represented as Fa, Fb, Fc. And 262 the two resource coordination factors of ?? and ?? are combined to 263 perform multi-layer resources which are normalized by Fa, Fb, and Fc. 264 The normalization process is expressed as equation (4). When the 265 minimum value is obtained according to ??, the most appropriate path 266 and node are selected, and corresponding resource allocation is 267 performed. 269 The relevant algorithm flowchart is given in Figure3. First, we 270 obtain the relevant resource utilization of each layer of the input 271 service request SRi. Then we use it to calculate the parameters of 272 each layer and get the resource allocation parameter ??. And 273 additionally, we compare all the parameters and find the minute one. 274 Finally, find the path and node corresponding to the min ?? and 275 perform radio frequency allocation. 277 3.1. SIMULATION AND RESULTS 279 In order to test the optimization of the resource allocation of the 280 scheme and verify its efficiency, we also made several comparisons 281 between the proposed algorithm and the traditional one. The 282 traditional way for resource allocation optimizes the processing of 283 spectrum resources based on the virtualization of network functions. 284 It combines both the centralized and distributed elements. It can 285 also independently develop centralized control platforms, such as 286 virtualization and sectioning of network[9]. They use network 287 throughput as the optimization goal and consider the use of only one 288 certain resource in a single way. They do not refine the resources 289 according to the difference of user services and the architecture of 290 network development. 292 Based on the software test platform, we build a simulation model. We 293 use the Open vSwitch proxy controller to control the interaction 294 between the nodes. In the 5G fronthaul, the heavy traffic load is 295 from 40 Erlang to 150 Erlang. For the proposed model and the 296 Openflow-based control platform, three virtual machine deployment 297 planes are used: the TC server supports the interaction between AC 298 and DC. The DC server is used to acquire and supervise the DU 299 computing resources. The AC server obtains the radio distribution. 300 On the established platform, the optimization of the proposed 301 solution is demonstrated by testing the resource occupancy rate and 302 path provision latency of the server. Based on the proposed resource 303 allocation algorithm, the preset weight ?? is set to 50%, so that the 304 CPU occupancy rate and the resource storage rate occupy the same 305 proportion, and then the preset weights ??, ?? are set to 33.33%, so 306 that the resources occupy of the three layers can gain the same 307 weight. And the CPU storage rate occupied by each service is 308 randomly allocated between 0 and 1%. When the request reaches, the 309 best path and node will be calculated according to the formula, and 310 the corresponding RF resources will be provided. Then we obtain the 311 relevant indicators and compare them. In order to get the 312 optimization of time precision, we compared the path provision 313 latency between our way and the traditional way. And the results are 314 shown in Figure.4 where GES represents the scheme above and CSO 315 represents the traditional one. What??s more, in order to obtain the 316 resource utilization of the proposed method, we also compared the 317 resource occupation rate between those two ways. The experiments 318 proved that the scheme we proposed could improve the efficiency of 319 resource allocation. The path provision latency is lower and the 320 resource occupation rate is higher. It means that this solution has 321 many advantages for 5G fronthaul resource allocation and can improve 322 the flexibility of the whole network. 324 +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ 325 | | path provision | 326 | Traffic load +-+-+-+-+-+-+-+-+-+-+-+-+-+ 327 | | CSO | GES | 328 +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ 329 | 40 | 29.1 | 25.7 | 330 | 60 | 32.7 | 27.3 | 331 | 80 | 35.1 | 28.8 | 332 | 100 | 36.6 | 32.7 | 333 | 120 | 42.5 | 38.0 | 334 | 140 | 49.4 | 43.3 | 335 +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ 337 Tab.1 path provision of two strategies 339 +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ 340 | |resource occupation rate | 341 | | path provision | 342 | Traffic load +-+-+-+-+-+-+-+-+-+-+-+-+-+ 343 | | CSO | GES | 344 +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ 345 | 40 | 0.05 | 0.06 | 346 | 60 | 0.11 | 0.14 | 347 | 80 | 0.19 | 0.23 | 348 | 100 | 0.32 | 0.37 | 349 | 120 | 0.40 | 0.50 | 350 | 140 | 0.51 | 0.58 | 351 +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ 353 Tab.2 resource occupation rate of two strategies 355 4. CONCLUSION 357 In summary, this paper considers the resource allocation requirements 358 in the 5G technology scenario. According to the changes of the 5G 359 network architecture and the multiple use of resources, we 360 redistribute the resources and propose the corresponding functional 361 models. It is used to adopt a resource allocation algorithm to 362 optimize the resource allocation of each layer and realize the joint 363 deploy and utilization of multi-layer resources. In the traditional 364 resource allocation model, we used to consider the utilization of 365 only one certain type of resource. This solution realizes the global 366 deployment of 5G fronthaul resources, which is able to improve the 367 flexibility of the 5G fronthaul network. 369 5. Acknowledgments 371 This work has been supported in part by NSFC project (61501049), 372 Fundamental Research Funds for the Central Universities (2018XKJC06) 373 and State Key Laboratory of Information Photonics and Optical 374 Communications (BUPT), P. R. China (No. IPOC2017ZT11). 376 6. Informative References 378 [Ref1] Yang, H., Zhang, J., and YL. Zhao, "CSO: Cross Stratum 379 Optimization for Optical as a Service", Aug 2015. 381 [Ref2] Yang, H. and J. Zhang, "Experimental demonstration of 382 multi-dimensional resources integration for service 383 provisioning in cloud radio over fiber network", 2016. 385 [Ref3] Yao, L., "Joint Optimization of BBU Pool Allocation and 386 Selection for C-RAN Networks", 2018. 388 [Ref4] Ramon, Casellas., "Control, Management, and Orchestration 389 of Optical Networks: Evolution, Trends, and Challenges", 390 2018. 392 Authors' Addresses 394 Hui Yang 395 Beijing University of Posts and Telecommunications 396 No.10,Xitucheng Road,Haidian District 397 Beijing 100876 398 P.R.China 400 Phone: +8613466774108 401 Email: yang.hui.y@126.com 402 URI: http://www.bupt.edu.cn/ 404 Yiqian Liu 405 Beijing University of Posts and Telecommunications 406 No.10,Xitucheng Road,Haidian District 407 Beijing 100876 408 P.R.China 410 Phone: +8613177087617 411 Email: 497706153@qq.com 412 URI: http://www.bupt.edu.cn/ 413 Jie Zhang 414 Beijing University of Posts and Telecommunications 415 No.10,Xitucheng Road,Haidian District 416 Beijing 100876 417 P.R.China 419 Phone: +8613911060930 420 Email: lgr24@bupt.edu.cn 421 URI: http://www.bupt.edu.cn/ 423 Ao Yu 424 Beijing University of Posts and Telecommunications 425 No.10,Xitucheng Road,Haidian District 426 Beijing 100876 427 P.R.China 429 Email: yuaoupc@163.com 430 URI: http://www.bupt.edu.cn/ 432 Qiuyan Yao 433 Beijing University of Posts and Telecommunications 434 No.10,Xitucheng Road,Haidian District 435 Beijing 100876 436 P.R.China 438 Email: yqy86716@126.com 439 URI: http://www.bupt.edu.cn/