Ericsson Review: The benefits of self-organizing backhaul networks

The communications technology journal since 1924 2013 • 10

The benefits of self-organizing backhaul networks September 27, 2013

The benefits of self-organizing backhaul networksThe rise in the number and variety of universally available mobile-broadband (MBB) services is great for users and vital for operator revenue. For backhaul, however, the opportunities presented by MBB are offset by the challenge of an ever-increasing number of nodes and the need to support more and more services – both of which lead to a more complex backhaul and an increased risk of rising operational costs.

controlling costs. Computerization helps to reduce the risk of manual error, facilitates optimization of net-work utilization, supports overall per-formance optimization, and lowers operational costs.

Automation is nothing new when it comes to simplifying network opera-tions; it is, for example, a key part of IP and Ethernet network technologies. However, the rapid expansion of LTE networks on a global scale has placed greater emphasis on automation as an essential tool for all operational areas of radio-access networks.

Studies carried out by Ericsson indi-cate that introducing SON features results in 40 percent faster rollouts, and the daily maintenance of new LTE networks can be reduced by up to 90 percent2.

During the definition of LTE, SON concepts were identified as essential for ensuring optimal user experience, and as a result, 3GPP has developed related standards for radio-access equipment3. These standards include a combination of self-configuration, self-healing and self-optimization functions for use in nodes and network-management- system layers. The importance of SON techniques has also been highlighted by the NGMN Alliance4.

SON techniques have been includ-ed successfully in 2G/3G radio stan-dards2 and are now mature enough to be applied to other network domains, such as backhaul. Although backhaul and radio access are quite different technologies, the operational challeng-es they present are similar, and many are directly related. Both technologies

heterogeneous radio networks become more widespread. These mixed architectures include vast numbers of small cells that complement improved and densified macro layers, and require highly scalable and flexible backhaul solutions to ensure a superior user experience1.

The challengeThe improve-densify-add strategy for building heterogeneous radio net-works facilitates the rapid delivery of extra coverage, capacity and ser-vices. However, this strategy makes it challenging to deploy and operate vast numbers of backhaul nodes while keeping operational costs to a mini-mum – as illustrated in Figure 1.

Introducing a higher degree of inbuilt automation to the network is crucial to speeding up deployment and

SH A H RYA R K H A N, JONA S E DSTA M, BA L ÁZS VA RGA, JONA S ROSE N BE RG, JOH N VOL K E R I NG A N D M A RT I N ST Ü M PE RT

BOX A Terms and abbreviations

ANR Automatic Neighbor RelationsBR border router BSC base station controllerCSR cell site routerDHCP DynamicHostConfiguration ProtocolDoD Downstream-on-DemandE2E end-to-endeNB eNodeBIPsec Internet Protocol Security LDP Label Distribution ProtocolLLDP Link Level Discovery Protocol MBB mobile broadbandMME Mobility Management EntityMPLS multi-protocol label switching

NGMN Next Generation Mobile NetworksNMS network management systemNNI Network-to-Network InterfaceNOC network operations centerPM performance managementRAN radio-access network RCA root cause analysisRNC radio network controllerSGW service gatewaySON self-organizing networksTCO total cost of ownershipUNI User to Network InterfaceVLAN virtual local area network

To address the increasing complexity of the backhaul network, innovation is essential. New technologies and methods are needed that automate or simplify the time-consuming and complex tasks carried out in node management and network management systems (NMSs). Self-organizing networks (SON) methods and technologies have proved to be successful in addressing complexity issues and preventing rising operational costs, when applied to radio-access networks. But these techniques have yet to be applied extensively to backhaul.

Backhaul is a key factor in the overall performance of MBB networks, and one that is increasing in importance as

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E R I C S S O N R E V I E W • SEP TEMB ER 27, 2013

Automation in the backhaul

share the goal of maximizing overall network performance.

By applying similar and complemen-tary SON concepts to all parts of the network, synergies between the radio and backhaul networks can be creat-ed, providing benefits to operators in terms of reduced operational costs, and to users in terms of an optimized indi-vidual experience.

Managing more with lessAs illustrated in Figure 2, SON con-cepts could be applied to many of the key operational areas for backhaul: build, assure, optimize and maintain.

There are a number of SON enablers that play an important role as the trig-gers for SON functions. Performance monitoring, for example, is an enabler for the self-healing and optimization use cases.

BuildBackhaul networks are built in three phases: plan, deploy and provision. The planning phase can be simplified by applying SON techniques. For exam-ple, planning data can be used to auto-matically generate configuration files. During deployment, SON techniques can be used to integrate backhaul nodes in a fully automated way. And during provisioning, SON concepts can be applied to automatically provision network services, such as an E-Line or an L3 VPN service.

As the number of backhaul nodes increases, adopting a minimal-touch approach will become a significant way to reduce operational costs. Use cases 1 and 2, described later in this arti-cle, illustrate how the minimal-touch concept applies.

AssureThe concept of self-healing is not new to the area of IP protocols. However, applying self-healing functions to sev-eral layers of a network can create some coordination issues. For example, the detection of a failure, or degradation in performance in the transport layer, could result in traffic being rerouted in the service layer – to maintain the required service-quality target.

Use case 3 below, describes how self-healing is implemented.

FIGURE 1 The backhaul challenge

Heterogeneous networks

Smart Scalable SimpleSuperior

performance

Convergence

Improve

Densify

Add

TCO

MME

SGW/PGW

RNC/BSC

FIGURE 2 Key operational areas for SON concepts

Provision

Deploy

PlanBuild Optimize

Assure

Self-healing

Maintain

Backhaul

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Use case 1: auto-integrationUsing a zero-touch approach to install and configure nodes reduces the need for skilled technicians onsite. For any kind of network, the potentially large number of nodes to install and config-ure significantly impacts the time and cost taken to complete rollout.

Given the scale of today’s networks with several thousands of nodes and the savings potential in terms of opex and deployment time, the use case for auto-integration of not only base stations but also of backhaul nodes is compelling.

The benefits of auto-integration diminish significantly, however, when it comes to backhaul nodes further up in the network hierarchy – such as in

the aggregation and core domains. The reason is simple: the number of nodes to be installed and integrated at this level is relatively low, and so the bene-fits of automation are few.

Care needs to be taken in how SON techniques are applied, as automating one area may create new problems, such as security issues or additional complexity, elsewhere in the network – and so SON techniques need to be applied in a holistic way. The benefits and applicability of SON need to be balanced carefully, but the gains to be made from applying these techniques could be considerable.

From a node perspective, auto- integration appears to be relatively simple. The concept has existed for some time for residential gateways, and 3GPP has been working in this area to define the auto-integration process for eNBs, for example.

The relationship of an eNB to the existing transport and IP network is that of a client, which primarily uses Ethernet or VLAN to connect with the User to Network Interface (UNI). It is assumed that the management con-nectivity is present in the existing transport and IP network, and just the discovery of a management VLAN to the existing network is sufficient to initiate the auto-integration process.

A cell site router (CSR), on the other hand, is likely to become part of the existing backhaul network, and can use any transport technology such as Ethernet, native IP or MPLS. The chal-lenge for this case is the discovery pro-cess of the management network, as it is also dependent on the existing transport technology – which may vary from network to network. As Figure 3 illustrates, the CSR needs to become part of the existing IP/MPLS network. So, in contrast to the eNB case (which uses UNI to connect), after integration the CSR uses a Network-to-Network Interface (NNI) to connect to the exist-ing network.

Consequently, a SON solution for auto-integration needs to be technol-ogy agnostic and sufficiently flexible to address any type of network tech-nology. Figure 4 shows how auto-inte-gration can be implemented, where the network provides a temporary UNI for the CSR, which is replaced by

OptimizeOperators can apply SON techniques to, for example, use network band-width more efficiently and ensure that energy consumption is kept to a min-imum. Optimization parameters can be prioritized and automatically bal-anced against each other – use case 4 describes how SON techniques can be applied to network optimization.

MaintainAreas such as inventory management, software and hardware upgrades and network-wide troubleshooting could potentially benefit from SON tech-niques. Use case 5 describes the appli-cation of SON to the maintenance of networks.

FIGURE 3 Difference between CSR and eNB auto-integration

eNB

IP/MPLS

Management connectivity

CSR

CSR

NNI

UNI

ETH

FIGURE 4 Temporary UNI as an enabler for SON auto-integration

CSR

IP/MPLS

Management connectivity

TemporaryUNI

(Permanent)NNI

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a permanent NNI at the completion of the process.

The entire auto-integration process for a backhaul node – such as a CSR – is illustrated in Figure 5.

Auto-integration of a cell site routerInitial connectivity to the NOC serv-ers can be established using a tempo-rary SON VLAN (UNI). The SON VLAN provides temporary management con-nectivity in a way that is agnostic in relation to the existing transport tech-nology. Once the installation and ini-tial configuration processes have been completed, the temporary connection is replaced with the preferred perma-nent one (NNI).

Assuming that some form of in-band management connectivity in the exist-ing network to the NOC servers exists, the auto-integration process runs as follows:

Step 1The auto-integration script enables and configures the necessary ports and interfaces.

Step 2The discovery process of the temporary SON VLAN includes DHCP communi-cation and the CSR authentication to establish the connection from the CSR to the NOC servers.

The Link Level Discovery Protocol (LLDP) or the native VLAN are possi-ble methods that can be used to dis-cover the SON VLAN. Once it has been discovered, DHCP communication can begin and authentication of the CSR is possible.

The handling of the DHCP messages between the CSR and the NOC servers is dependent on transport technology and transport service. For example, in the case of an L3 VPN-based connection to NOC servers, the aggregation node upstream from the CSR acts as a DHCP proxy and relays a unicast message to the DHCP server.

In the case of L2-based connectivity, the DHCP broadcast message can be forwarded as is.

Step 3The configuration file is then down-loaded and applied to the CSR.

Step 4Permanent connectivity to the NOC servers also requires configuration update on the existing upstream backhaul node(s). Temporary SON VLAN (UNI) needs to be replaced with the permanent NNI. This could be the final auto-integration step.

The availability of a transport connection to the NOC for manage-ment connectivity cannot always be assumed. Consequently, an inno-vative way to establish temporary connectivity is needed. Figure 4 illustrates one possible way to cir-cumvent the initial connectivity issue by using a smartphone with a mobile data connection and estab-lishing a secure out-of-band com-munication channel between the backhaul node and the NOC servers. The remaining part of the process is then similar to the CSR case.

The return on investment that Ericsson estimates for auto- integration of backhaul nodes includes 15 percent faster rollouts, 50 percent competence cost reduc-tion, only one site visit and an over-all improvement in quality.

FIGURE 5 Use case 1: auto-integration in deployment

eNB CSR DHCPserver

Configuration andsoftware servers

NOC

eNB

eNB

CSRauto-integration

DHCPcommunication

NOCconnectivity

Configuration download

SON VLANdiscovery

1

2

3

4

1,2,3,4

Use case 2: auto-provisioning of LTE X2 connectivityThe X2 interface in LTE is a direct log-ical connection of neighboring base stations that can be used for hando-ver and for advanced interference coordination – with the aim of ensur-ing better user experience at the cell edge. As network architectures prog-ress toward more advanced real-time radio coordination, more stringent delay requirements are placed on the interconnecting backhaul path – creating the need to use the shortest (optimal) path possible. Given that a base station may have several tens of radio neighbors and that the relation-ships between a base station and its neighbors cannot fully be predicted (but must be based on radio network measurements), automating neighbor relations is a good candidate for SON. Indeed the SON function – Automatic Neighbor Relations (ANR) – has been successfully applied in LTE to automate this process, and shown to reduce over-all network planning by 90 percent2. In addition, the setup of fully opti-mized X2 connections in a backhaul network can be a tedious, multi-touch and fairly repetitive task. There

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Snooping X2 control messages is one way to implement a mechanism to dis-cover eNB neighbor relations (X2) on the CSR. For IPsec, however, tunneling requirements for X2 connectivity may prohibit this method; control messag-es for tunnel endpoints residing on the eNBs are encrypted. The implementa-tion of steps 3 and 4 depends on the architecture of the backhaul network.

Consider, for example, the case of a seamless MPLS architecture with LDP Downstream-on-Demand (DoD) in the access part of the network, where IP VPN services are used for LTE connec-tivity. Such an architecture simplifies the setup of inter-area access trans-port paths, as the inherent behavior of LDP DoD alleviates the need to dis-tribute prefixes between access areas. A default route to the BRs is sufficient for X2 transport, and so maintaining complex filters at the BRs is no longer necessary.

Connectivity from the CSRs to the core part of the network is initially provisioned in a hub-and-spoke man-ner, while service provisioning for the shortest (optimal) X2 connectivity is

are a number of factors that con-tribute to this. First, each base station may have several tens of X2 interfaces to optimize. Second, maintaining a cor-rect list of neighbors requires consis-tent and regular coordination between the operator’s radio and backhaul orga-nizations. Third, to setup X2 connec-tions between access areas, the related transport prefixes must be visible to all areas.

For scalability reasons, distribution of transport prefixes between access areas is not typically allowed and enforced using filters on the border routers (BRs). So, to automatically set up X2 connections, a new approach is required to reduce the need to contin-ually update BR filters across multiple areas.One possible way to implement such a change is through a backhaul SON solution that automates the trans-port setup for the X2 communication. To establish the shortest (optimum) path for X2 in the transport network, this solution starts with ANR-based discovery of new neighboring base sta-tions, and all of the steps in this process are shown in Figure 6.

carried out on demand, as part of the backhaul SON solution. The service-provisioning process uses the remote eNB IP addresses, learned in the discov-ery phase, to dynamically create VPN membership-related parameters. This results in the automatic population of a VPN routing table with only the desired eNB prefixes.

Triggering the LDP DoD procedure creates the underlying label-switched path in a dynamic fashion and ensures that only the relevant labels are learned on demand for the respective CSRs.

In effect, the backhaul SON solution for LTE X2 connectivity services results in minimal-touch provisioning. The process can be automated, alleviat-ing the need for coordination between the operator’s backhaul and RAN orga-nizations, resulting in a much faster service rollout.

As the backhaul SON solution reduc-es the number of states to be main-tained in the CSR to a minimum, fewer IP prefixes and MPLS labels are need-ed. This in turn results in a much high-er degree of scalability achievable in transport networks – a major benefit for operators.

This use case highlights the benefits of complementing SON establishment of X2 relations in RAN with an auto-mated setup of the backhaul.

Use case 3: self-healingThe task of assuring performance in mobile backhaul networks has become more critical owing to the rising num-ber of users, network complexity and bandwidth-hungry services. And so, this use case addresses the need to pro-vide interaction between the backhaul and RAN domains for performance-measurement and management functions.

The resulting network information, obtained by mapping and correlating data from different network segments and domains, is a powerful asset; not only does it serve as an important add-on to the standard network KPI report-ing and troubleshooting, but also as a vital indicator and trigger for SON mechanisms.

At Mobile World Congress 2013, Ericsson demonstrated how self-healing applies to a mobile backhaul

FIGURE 6 Use case 2: auto-provisioning of LTE X2 connectivity

eNB

CSR

CSRBR

eNBS+T

discover and create logical X2 interface between each other (ANR)

MME

SGW/PGW

CSR

eNB

eNB

1

S

S

T

T

11

2

3

14

Access

Automated setup of transport path between CSRS+T

and BR

X2 default longer path

X2 direct path

S

T

Source eNB or CSR

Terminating eNB or CSR

CSRS+T

learn remote eNB IP address

CSRS+T

dynamically imports the VPN-related remote eNB prefix(es)

AccessAggregation

RBS site Switch site

RNC/BSC

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FIGURE 7 Use case 3: self-healing

Radio/core NMS

Switch siteRBS site

RAN KPIsreport

Transport KPIsreport

Correlation and RCATransport NMS

E2E PM

2

3

1

MME

SGW/PGW

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SON healingtriggered

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network; the demo included how pro-active rerouting decisions were made, based on performance-measurement data. By correlating performance data collected from the backhaul and radio domains, it is possible to associate per-formance issues in the mobile network with a particular segment of the back-haul network. For example, a data-rate degradation experienced by a user on an HSPA network can be traced to a bot-tleneck in the backhaul network.

The main steps in the process to cre-ate cross-domain radio and backhaul correlation for performance data are illustrated in Figure 7 and may be out-lined as follows:

1. Performance data from radio and core (WCDMA or LTE) networks are collected from network elements via the element or domain manager, and then stored in the common data warehouse, providing the source information for performance reports. In the example illustrated in Figure 7, cell performance in terms of available HSPA rates and numbers of connected users are measured and reported continuously, enabling possible cell-related performance degradations to be quantified.

2. In the backhaul part of the network, the element or domain manager collects performance data from the network elements. Available bandwidth in the network is measured and reported continuously – per interface and per service. Congestion may occur when an interface is highly utilized and may result in delays and discarded or lost transported data, which can directly impact user-perceived service quality.

3. By using topology and service-mapping data, the performance data collected from the radio, core, and backhaul networks can be correlated and tracked over time to identify patterns. This allows operators to identify the causes of reduced performance in bandwidth for specific backhaul services, correlating them, for example, to a temporary reduction in the HSPA rate for a specific radio cell. Correlated performance data can be fed back to the NMS system as performance-management alarms, which will in turn trigger a SON mechanism.

The service and topology map-ping and cross-domain correlation of performance data mechanisms, described in use case 3, provide a key input to network optimization and healing triggers, creating the desired SON feedback loop. A feedback loop is created by using the trigger infor-mation to rehome affected subscrib-ers to another RBS; or to dynamically reprovision the transport path or ser-vice for the RBS to another, unaffected, transport path.This use case highlights the successful application of self-heal-ing concepts to backhaul networks. By introducing cross-domain correlation of performance data, congestion points in the network can be pinpointed, and corrective action taken.

Use case 4: optimize The need to carry out optimization pro-cesses is not as pressing as it is in other use cases. Optimization can be quite complex, and the time frame for such improvements can be weeks or months rather than a few minutes or hours. As with most cases, SON automation is best suited to optimizing events that occur frequently.

Bandwidth optimization is an exam-ple in this use-case category. Over the course of time, changes in traffic pat-terns can give rise to the need for path re-optimization for certain types of traffic. For example, in a backhaul net-work some links can become overuti-lized or underutilized. In such cases, rerouting traffic is a simple solution without having to wait for the next planned capacity upgrade for achiev-ing better optimization of network utilization.

But before selection of the best traf-fic optimization can take place, the traffic trends must be identified, and doing this manually can be time-con-suming and may involve the analysis of large amounts of data. SON tech-niques can help to automate the over-all bandwidth optimization process and provide multiple near-real-time traffic rerouting solutions, which operators can choose from in the final design decision. Similarly, SON meth-ods could also be used to implement energy-optimized traffic steering across the network. The corrective action in such a case would be to turn off nodes with low utilization, and

1. Ericsson, 2012, White Paper, It All Comes Back to Backhaul, available at: http://www.ericsson.com/res/docs/whitepapers/WP-Heterogeneous-Networks-Backhaul.pdf

2. Ericsson, 2012, White paper, Smarter Self-Organizing Networks, available at: http://www.ericsson.com/res/docs/whitepapers/WP-Self-Organizing-Networks.pdf

3. 3GPP,2011,TechnicalSpecification,3GPPTS32.500TelecommunicationManagement; Self-Organizing Networks (SON); Concepts and requirements (Release11),availableat:http://www.3gpp.org/ftp/Specs/html-info/32500.htm

4. NGMN Alliance, 2007, White paper, NGMN Use Cases related to Self Organising Network, Overall Description, available at: http://www.ngmn.org/uploads/media/NGMN_Use_Cases_related_to_Self_Organising_Network__Overall_Description.pdf

References

finer details when required. Additional views should provide the capabilities to ease understanding of resource uti-lization, identify inefficiencies and even provide suggestions on how to get more out of network resources.

The inventory could be used as a trig-ger for automated service provisioning work flows, diagnosis and trouble-shooting as a second step.

ConclusionOperators are under constant pressure to find innovative ways to reduce opex, yet improve service quality and avail-ability of broadband networks. The introduction of SON in the 3GPP radio network is a good example of where innovation has brought benefits.

The use cases for applying SON tech-niques during the deployment phase of the backhaul network are compelling, substantial cost savings can be made – and additional use cases of SON for assurance, optimization and mainte-nance also highlight areas where oper-ators can create a balance between cost and efficiency.

Intelligent SON support in nodes and management systems promises to be a key tool in addressing the challenges posed by evolving broadband networks and helping networks to deliver addi-tional coverage, capacity and services in an agile and cost-effective manner.

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such a decision can be taken on a single-node or centralized basis.

Use case 5: maintainManual tasks that are tedious and labor-intensive are prime candidates for SON automation. Defining the devices in an inventory system typi-cally tends to be both. For a large network, automating the inventory process could provide some benefits, as the current solution – which uses a polling mechanism with scheduling capabilities – offers inconsistent sup-port to discover recently-added net-work elements.

Inventory management relies heav-ily on automation and simplification to limit costs. At the same time, such systems need to be more capable as operators search for ways to create efficiencies and generate new revenue sources. To ensure that system users remain in control, automation should be applied carefully. Full automation might be appropriate in some sce-narios, but in others, a user-assisted or system-guided approach may be preferable.

An inventory management system should provide users with a number of different views of their network resources. High-level views support simplification goals, but administra-tors also need to be able to access the

Ericsson Review is a technology journal designed to open and encourage discussion. The aim of the journal is to high ight current research in information and communications technology.

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Shahryar Khan

joined Ericsson in 2005 and is responsible for managing technology leadership activities

within Development Unit IP & Broadband. He is a senior specialist in the area of IP and MPLS network architectures and solutions. His other areas of interest include IP optical integration, cloud transport and SDN. He also has a history of strategic customer engagements and has received an Outstanding Achievement Award. He holds a B.Sc. (Hons) in electrical engineering from the University of Engineering and Technology, Lahore, Pakistan.

Balázs Varga

joined Ericsson in 2010 and works as chief architect in packet evolution studies to

integrate IP, Ethernet and MPLS technologies for converged mobile and fixed network architectures. Prior to joining Ericsson, he worked for Magyar Telekom and Deutsche Telekom on the enhancement of broadband service portfolios and introduction of new broadband technologies. He has many years experience in fixed and mobile telecommunication and also represents Ericsson in the Broadband Forum. He holds a Ph.D. in telecommunication from the Technical University of Budapest, Hungary.

Jonas Rosenberg

joined Ericsson in 2000 and is currently a systems and solution manager at Development Unit IP &

Broadband. He is a senior specialist in network architecture and solutions with a focus on strategic technologies for orchestration and assurance solutions in mobile transport networks. He holds an M.Sc. in electrical engineering from the KTH Royal Institute of Technology, Stockholm, Sweden.

Jonas Edstam

joined Ericsson in 1995 and is head of portfolio and strategy at Product Line Microwave Networks.

He is also an expert in microwave radio transmission networks. He has many years of experience in this area and has worked in various roles with a wide range of topics, from detailed microwave technology and system design to his current focus on the strategic evolution of packet-based mobile backhaul networks and RANs. He holds a Ph.D. in applied solid-state physics from Chalmers University of Technology, Gothenburg, Sweden.

Martin Stümpert

joined Ericsson in 1993 and is currently working on the network architecture for

transmission networks with a focus on SON, QoS, security and shared networks at Development Unit IB technology. In 2002, he received the Inventor of the Year award from the CEO of Ericsson. He holds an M.Sc. in electrical engineering from the University of Kaiserslautern, Germany.

John Volkering

joined Ericsson in 2007, and is currently a principal network architect within Ericsson’s Product Area

IP and Broadband. He focuses on providing technical sales consultancy for end-to-end operator network architectures and is a senior advisor on network evolution strategies, such as the introduction of SDN architectures. Prior to Ericsson, he worked for several telecom companies including Redback Networks, and in various technical presales positions. He holds a B.Sc. in electrical engineering from the Technical University of Rijswijk, in the Netherlands.

Ericsson Review: The benefits of self-organizing backhaul networks

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