MANAGING LTE IP TRANSPORT NETWORKS WITH ROUTE ANALYTICS · Managing LTE IP Transport Networks with Route Analytics ... from statically engineered ATM over SONET architectures to Layer

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MANAGING LTE IP TRANSPORT NETWORKS WITH ROUTE ANALYTICS

Copyright © 2017, Packet Design, LLCPage 2 of 14

Table of Contents

Executive Summary 3

LTE Core and Backhaul Transport Architectures 4

Why IP Networks Are Inherently Unpredictable 5

Traditional Network Management—Many Points of View, No Big Picture 7

Route Analytics—Seeing the Network from the Router’s Point of View 8

Improving LTE Network Management with Route Analytics 10

Conclusion 13

Managing LTE IP Transport Networks with Route Analytics


Executive Summary

Mobile operators today confront a major evolution in their network architecture, service traffic, and economics. The explosion of smartphones, tablets and High Speed Packet Access (HSPA) mobile broadband traffic drove the roll-out of Long Term Evolution (LTE) based on 3GPP standards. While mobile operators have relied for years on IP/MPLS networks for their mobile core backbone communications, LTE has driven a significant transformation of many mobile backhaul networks from statically engineered ATM over SONET architectures to Layer 3 IP/MPLS networks, particularly in the aggregation or High Radio Access Network (HRAN) layer. While the precise nature of this transformation varies dramatically depending on the legacy network assets and services, there’s no question that the evolution to IP/MPLS requires a new approach to network Operations and Management (OAM).

An inherent OAM challenge of IP is its dynamic nature. Unlike circuit-based and TDM network architectures of the past, IP networks can continuously and automatically reroute traffic paths around link failures and other changes in the network infrastructure. The result is an intelligent but unpredictable network topology that can not only cause delays to sensitive voice and broadband traffic, but also make management visibility and operational processes much more challenging. Traditional network management tools do not provide visibility into IP network dynamics, without which it is difficult to reduce operating expense Key Performance Indicators (KPIs) such as Mean Time to Detection (MTTD) and Mean Time to Repair (MTTR). In addition, lack of insight into dynamic network behavior impedes accurate maintenance and capacity planning, leading to costly operations errors and CAPEX waste.

Route analytics technology, which taps into the network’s live routing protocol control plane to provide real-time, network-wide insight of the operational routing topology and the traffic flowing across all network paths and links, is a key OAM technology for LTE mobile core and backhaul IP networks. Deployed by hundreds of telecom and mobile operators today, route analytics solutions are transforming IP/MPLS network management processes, helping engineering and operations teams deliver optimal service performance, speed problem resolution, strengthen change management processes, proactively uncover network vulnerabilities, increase capacity planning efficiency and ensure network and service resilience.

Effective integration of route analytics within the LTE mobile core and backhaul IP/MPLS network OAM portfolio can help mobile operators ensure higher service quality, leading to lower subscriber churn and reacquisition costs while reducing IP network OPEX and CAPEX.



LTE Core and Backhaul Transport Architectures

Long Term Evolution (LTE) and System Architecture Evolution (SAE) as defined by the Third-Generation Partnership Project (3GPP) introduced significant architectural changes to mobile operator networks. Of greatest interest to those responsible for deploying and managing the underlying transport is the fact that unlike previous standards, LTE and SAE together introduce an completely IP- based communications paradigm: Evolved Packet System (EPS). EPS employs an IP-based “Bearer” concept, essentially IP packet flows with defined Quality of Service (QoS), as the communications channel between handsets and tablets (the User Equipment or UE in 3GPP parlance) and the Internet via core network (CN) gateways (see Figure 1).

Mobile operators have for years operated IP/MPLS VPN backbone networks to interconnect their core nodes. With LTE, the much larger metro area backhaul networks that transport traffic from eNodeB cell sites must transform to handle IP traffic flows. Some mobile operators are choosing to utilize IP-friendly Layer 2 networks based on carrier Ethernet for backhaul transport, but for most operators IP/MPLS networks will be the transport network architecture of choice for LTE backhaul.

Figure 1: System Architecture Evolution/Evolved Packet Core (source: 4G Americas)



There are a number of different approaches to architecting these IP/MPLS backhaul networks. For example, due to the need to accommodate legacy ATM traffic some operators are using point-to-point MPLS Layer 2 VPN tunnels to transport backhaul traffic between eNodeB sites and core gateways. Others are choosing to utilize Layer 3 VPNs in the backhaul networks. While there are many ways to architect these IP/MPLS backhaul networks, they all must offer a high degree of resilience via router, link and path redundancy, as well as automated traffic re-routing around failures via standards-based OSPF or IS-IS protocols. In some cases, mobile operators will additionally implement MPLS Traffic Engineering (TE) via RSVP-TE and Segment Routing tunnels to ensure bandwidth availability and fast re-route (FRR) for failure recovery. Given the size and the complexity of these networks, it is critical that mobile operators possess strong OAM capabilities based on next- generation technologies and tools that can address the dynamic nature of IP/MPLS networks and also provide visibility into their Layer 2 VPN tunnels.

Why IP Networks Are Inherently Unpredictable

LTE is just the latest example of the convergence of literally all types of communication over IP. A major reason that IP became the de facto worldwide standard for data communications networks is its automated resiliency based on intelligent IP routing protocols that control the traffic routing topology. But while IP’s distributed routing intelligence makes it efficient and resilient, it also makes IP network behavior unpredictable and harder to manage. IP routing protocols automatically calculate traffic routes or paths from any point to any other point in the network based on the latest known state of network elements. Any change to those elements causes the routing topology to be recalculated dynamically. While this means highly resilient traffic delivery with low administrative overhead, it also creates endless variability in the active routing topology. Large networks with many redundant links can be in any one of millions of possible active routing topology states, which makes it much harder to understand and manage how traffic will be delivered (see Figure 2).

The lack of network management visibility into dynamic network behavior can be seen in the time-consuming process of correlating service problems to non device-specific network causes. For example, when a user reports a service performance problem that doesn’t stem from an obvious hardware failure, pinpointing the root cause can be quite difficult because in a large, complex IP network, IT engineers have no way to know the route the traffic took through the network, the relevant links servicing the traffic, whether those links were congested at the time of the problem, or even which devices were servicing the traffic. Without understanding these factors, troubleshooting processes become slow, inefficient guesswork games played by highly paid escalation engineers, increasing MTTR and raising OPEX. Change management processes suffer from the same problem, since engineers making planned configuration changes in the network have little or no idea of how the network-wide routing and traffic delivery behavior will change once the configuration change is made. This can lead to higher OPEX because changes must be rolled back or corrected, impacting service reliability and customer satisfaction.



For relatively non-critical applications like email and web browsing, the impact of routing and traffic changes may be slight, but for mobile voice, SMS, data services, interactive gaming, and streaming media which have sensitive latency requirements, the impact can be dire.

Next-generation OAM approaches are needed to manage IP network unpredictability, prevent and mitigate the service impacts of routing and traffic changes, and lower costs.

Figure 2: The dynamic nature of IP routing presents a mathematically daunting challenge to network management. In the illustrated network above, there are only 4 core routers and 5 edge routers. If one assumes that traffic only enters the network from the edges, the routed topology (combination

of routed paths) can be in any one of 55 (3125) possible states, or 53 (125) probable states. As a network grows in the number of interconnected routers, the complexity of understanding the network’s

behavior grows exponentially.



Traditional Network Management—Many Points of View, No Big Picture

Network management’s purpose is to overcome the complexity inherent in a large network and provide better visibility to network operations and engineering. The overarching architectural principle of today’s network management is to gather information on a vast number of different “points” in the network, and then correlate various point data to infer service conditions. The key mechanism for doing this is the Simple Network Management Protocol (SNMP), which gathers information at point devices such as routers, switches, security devices and servers, and their interfaces. The type of data gathered is:

• Device health: uptime, current status, CPU and memory utilization• Fault indicators: up/down status, uptime, dropped packets, errors• Traffic information: interface utilization, bytes in/out, packets in/out, configuration• Service utilization information: utilization per class of service, threshold violations

Having this point data is critical – for example, an interface or device that fails, runs out of memory, or is congested with traffic can have a direct impact on service traffic. However, the sum of all this point data often doesn’t provide the level of understanding needed to reduce detection and repair times. Just knowing that an interface is full of traffic doesn’t tell you why it is full. Where is the traffic coming from and where is it going? Is the traffic usually on this interface, or was there a change in the network or elsewhere that caused it to shift to this interface? If so - from where, when and for how long? Without answers to these questions, there is no real understanding of the behavior of the network as a whole, which robs the point data of much of its meaning.

While there are correlation algorithms for deducing certain types of network conditions, the fact of the matter is that SNMP was never designed to understand the complexities within routed IP networks. SNMP’s key limitation is that it is too periodic – polling cycles from 30 seconds to several minutes simply cannot produce an accurate portrait of the network’s routing state, with its sometimes rapid and high-volume state changes. Even speeding up the polling cycle – say, to every five seconds – would still miss many routing state changes, and anyway would generate so much management traffic overhead as to be impractical.

What’s needed is a network management approach that can complement traditional, polled, device data collection with real-time tracking of routing protocol and traffic flow message “events”. Routing protocols such as OSPF, IS- IS and BGP broadcast event messages to notify their peers of routing changes such as a routed link going down, or a new routed prefix being added to the network. Likewise, routers utilizing traffic flow technologies such as NetFlow, broadcast event messages carrying the volume of traffic in specific IP flows. It is critical to monitor and be able to analyze these routing and traffic events in order to understand and manage IP networks’ dynamic behavior.



Route Analytics—Seeing the Network from the Router’s Point of View

Route analytics technology, adopted globally by hundreds of service providers, mobile operators, cable MSOs, large enterprises, and government agencies, provides a new level of network visibility. Route analytics is built on the foundation of a different type of network visibility, afforded by tapping into the routing protocols – the source of intelligence that determines how IP/MPLS VPN networks deliver traffic.

Figure 3: Route analytics technology passively peers with, listens to and analyzes routing protocols to provide a real-time, network-wide understanding of all IP/MPLS VPN topology changes.



Route analytics is made possible by a sophisticated data collection technique. Using a collector that acts like a passive router, peering with selected routers across a network, and using routing protocols—OSPF, IS-IS, EIGRP, BGP and MP- BGP— the control messages that routers use to calculate how traffic will be sent across the network can be recorded (see Figure 3). By processing this information just the way routers do – albeit in a more comprehensive fashion – every Layer 3 routed path in the network can be calculated, from every host to every other host. Thus, a routing topology of the entire network can be created and maintained for operational and engineering analysis. Since routing protocols report changes to the topology within milliseconds, the topology map is continuously updated in real-time and always reflects exactly the way the real network is operating.

Route analytics integrates traffic information into this live topology map by collecting flow data (statistical information on unidirectional IP traffic streams generated by routers, such as IPFIX, NetFlow) from key traffic ingress points such as IP edge routers and Internet and roaming peering points. Using knowledge of the precise path that every flow takes at any time through the network, route analytics project the traffic data onto the component links of that path (see Figure 4). The result is a highly

Figure 4: Route analytics integrates NetFlow traffic statistics information into the IP/MPLS VPN routing topology by mapping traffic flows onto their routed paths. The result is a real-time, integrated routing

and traffic topology.



accurate, integrated routing and traffic map that shows the volume of class-of-service (CoS) traffic on every link in the network. Since both routing and traffic data is generated and stored continuously into a database, it is possible to view the network conditions exactly as they were at a past moment in time. In addition, since the topology is algorithmically calculated, it is possible to model routing and traffic changes, and simulate changes in network-wide behavior.

Improving LTE Network Management with Route Analytics

Route analytics technology provides new network management visibility to mobile operators who must manage their mobile core and backhaul IP networks to deliver excellent service quality. For the first time, network engineers can understand the relationship between service delivery and network operations. The results are greatly improved accuracy and efficiency of key business processes, contained costs, increased subscriber loyalty and lower customer churn.

Real-Time, Network-Wide Routing and Traffic Monitoring and Alerting: Route Analytics provide monitoring visibility into traffic flows on all internal and external links in the network. Operations can now easily monitor critical traffic paths, MPLS VPNs, RSVP-TE and Segment Routing tunnels to ensure that the network architecture maintains adherence to design specifications, and traffic remains under utilization thresholds across the entire network. Real-time alerts via SNMP, syslog or console views help reduce MTTD of service-impacting issues and enable more proactive customer experience management. For example, operators can monitor reachability to distributed eNodeB’s in the network, detect when any have lost reachability or have a path change that is suboptimal. Route Analytics can also reveal IP signaling plane stability issues such as excessive overall Layer 3 network churn, problems like link flaps or the loss of routing redundancy to key Internet Autonomous Systems (AS) or peering partners.

Benefit: Network operations can detect and anticipate problems much faster, reducing and preventing service impacts, lowering MTTD for packet core network issues by up to 60%

Layer 2 VPN Visibility Extends Analytics Capabilities: By monitoring Layer 2 VPNs, the power of route analytics is expanded to include the same link level data on Virtual Leased Lines (VLL) as with other typical Layer 2 links. For example, traps and alerts can be generated for Virtual Private Wire Service (VPWS) VLL State, VLL Redundancy and Service availability. This enables mobile operators to monitor pseudo-wires being used for backhauling cell site data. Network engineers also can verify that the actual deployments of Layer 2 VPNs conform to design specifications (see Figure 5).

Benefit: Service resiliency and redundancy can be confirmed and status monitored for quicker response to failures.



“Rewindable” Routing and Traffic History for Improved Troubleshooting: By continuously recording the state of routing and traffic over time, route analytics technology accurately portrays the network-wide state of all links, peerings, paths, and prefixes along with all traffic flows at any point in time. Engineers can “rewind” the network topology to pinpoint the precise MPLS VPN, RSVP-TE or Segment Routing tunnel, and routed path that the service traffic took through the network, as well as the utilization on the component links at the time of a problem. For example, in the case of a suboptimal VLL path from the mobile core to an eNodeB, engineers can see exactly what precipitated the change at what time and visualize the resulting path and traffic levels along the path. Using these historical forensics, engineers can solve hard-to-find intermittent problems in less time, increasing operations efficiency.

Benefits: MTTR for packet core network issues lowered by 20% to 40%. Improved network and service quality, and customer service responsiveness, results in higher customer satisfaction and lower churn.

Figure 5: Route Analytics incorporate path information from transported Layer 2 VPNs (eg. ATM, E-Line, Frame Relay, SONET/SDH and TDM) providing tools for managing mobile backhaul

resiliency and redundancy.



Network Modeling for Strengthened Change Management Processes: Route analytics provides a powerful modeling capability that can be used to greatly strengthen change management processes. Industry research shows that 80 percent of unplanned downtime is caused by people and process issues, including poor change management practices, while the remainder is caused by technology failures and disasters. Route analytics allows engineers to model and simulate planned IP, L2 and L3 MPLS VPN and Traffic Engineering tunnel routing and traffic changes, and to accurately predict the entire network’s behavior and any impact on service levels. For example, highly accurate modeling can help ensure path resilience between eNodeB and core gateway nodes. After making the changes, engineers can use route analytics to validate the correct network-wide routing and traffic behavior in real time.

Benefit: Reduces service impacts from change management errors by 25%

Internet Routing and Analysis for Improved Service Performance: Mobile broadband service customers who are accessing the Internet judge their mobile operator’s service quality by how well they can access their favorite applications and websites. Route analytics for BGP Internet routing help network engineers and planners identify important sources of traffic for key customer groups. Then, using simulation and modeling, they can find ways to optimize routing between multiple Internet peerings to achieve the shortest number of AS hops from those sources and reduce traffic latency from those key sites. Real-time monitoring of BGP AS Paths to critical external networks can alert network operators to a loss of redundancy so that they can take measures to ensure service delivery continuity.

In addition, route analytics can be used to ensure acceptable external peering utilization levels and optimize transit and peering arrangements, which can significantly reduce mobile operator operating costs. Route analytics provides engineers with the most complete set of capabilities, including the ability to monitor peering or transit traffic to ensure it is within contracted ranges, as well as analyze, identify and justify new peering relationships. Engineers can also accurately simulate proposed peering changes to project exactly how traffic would behave with the proposed changes, helping them to make more informed investment decisions. Whether moving traffic from paid transit to settlement-free peering, or balancing between multiple transit providers, route analytics provide the intelligence operators need to optimize their peering traffic and maximize their bottom line.

Benefit: Improved customer service quality experience, increases customer loyalty and lowers customer churn. Reduces peering and transit operating costs by up to 20%.

Network-Wide Routing Health Audits for Improved Service Continuity: One of the hardest challenges in the midst of complex network operations is to anticipate and avoid problems. There is often no insight into potential causes of service impacts. Route analytics provide a network-wide audit of routing health by systematically examining the network for problems and vulnerabilities, such as out-of-policy asymmetric routes, routing black holes, lack of or potential loss of path diversity between critical service nodes, underutilized assets and potential redundancy failures. By proactively



identifying problems in the network, route analytics technology enables engineers to prioritize proactive fixes, increase network quality and prevent service impacts.

Benefit: Higher network quality, lower service impacts and improved customer satisfaction.

Network-Wide Capacity Planning for Reduced Capex: One of the most important network management processes for capital-intensive mobile operator networks is accurate capacity planning. Unfortunately, network planners often lack accurate and comprehensive information about the network’s traffic utilization over time, leading to inaccurate planning exercises, sub-optimally deployed resources and wasted capital expenditures. By maintaining an always-accurate model of the entire network’s routing and traffic behavior, route analytics technology provides the basis for rapid, iterative and automated capacity planning and traffic trending.

Benefit: Capital Expense Savings of up to 20% for evolved packet core network infrastructure.

Conclusion

This paper has established the need for insight into the dynamic behavior of LTE core and backhaul IP transport networks, and how route analytics technology meets this need. Mobile operators that leverage route analytics to increase the automation of IP/MPLS and Layer 2 VPN network operations and engineering will gain a sustained competitive advantage due to the ability to deliver more reliable and higher quality services while increasing profitability.

Packet Design’s Route ExplorerTM System combines route analytics for all IGP and BGP protocols with path- aware traffic flow analytics. In a single code base, it offers:

• Real-time visibility into routing and traffic behavior plus intelligent alerts for proactive operational monitoring and more efficient triage of service interruptions

• DVR-like replay and analysis of routing events for faster troubleshooting of intermittent and hard- to-find service delivery issues

• Interactive simulation of configuration changes for risk-free network maintenance• Predictive analysis of new workloads for better capacity planning



To learn more about Packet Design and the Explorer Suite, please visit www.packetdesign.com

MANAGING LTE IP TRANSPORT NETWORKS WITH ROUTE ANALYTICS · Managing LTE IP Transport Networks with Route Analytics ... from statically engineered ATM over SONET architectures to Layer

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