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 This Internet-Draft is submitted in full conformance with the provisions of BCP 78 and BCP 79. Internet-Drafts are working documents of the Internet Engineering Task Force (IETF). Note that other groups may also distribute working documents as Internet-Drafts. The list of current Internet- Drafts is at https://datatracker.ietf.org/drafts/current/. 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Expires May 9, 2019 [Page 1] Internet-Draft CSO Architecture for OaaS November 2018 the Trust Legal Provisions and are provided without warranty as described in the Simplified BSD License. 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 Yang, et al. Expires May 9, 2019 [Page 2] Internet-Draft CSO Architecture for OaaS November 2018 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 Yang, et al. Expires May 9, 2019 [Page 3] Internet-Draft CSO Architecture for OaaS November 2018 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 Yang, et al. Expires May 9, 2019 [Page 4] Internet-Draft CSO Architecture for OaaS November 2018 ----------------------------------------------------------------------------- | 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 Yang, et al. Expires May 9, 2019 [Page 5] Internet-Draft CSO Architecture for OaaS November 2018 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. Yang, et al. Expires May 9, 2019 [Page 6] Internet-Draft CSO Architecture for OaaS November 2018 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. Yang, et al. Expires May 9, 2019 [Page 7] Internet-Draft CSO Architecture for OaaS November 2018 +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+ | | 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. Yang, et al. Expires May 9, 2019 [Page 8] Internet-Draft CSO Architecture for OaaS November 2018 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/ Yang, et al. Expires May 9, 2019 [Page 9] Internet-Draft CSO Architecture for OaaS November 2018 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/ Yang, et al. Expires May 9, 2019 [Page 10]