Three Reasons to Prepare for Your Next Broadband Infrastructure Investment

Two years after the COVID-19 pandemic proved the internet invaluable with so many of us working, shopping, educating our children, and accessing health care – all from home – we’re still faced with a digital divide between those who have access to broadband Internet and those who don’t. Efforts by service providers to upgrade their network infrastructure to handle increased load has been both rapid and impressive, but more is needed. There remains a significant percent of the population lacking sufficient broadband to fully participate in the digital economy and society. This must change, but how?

There are three areas we need to focus on if we hope to expand much-needed internet access to those who lack it: bridging the digital divide, locating and securing available funds, and improved expertise and planning. But first let’s examine the numbers as related to the ever-increasing value of the internet and those who lack full access to its benefits.

In March 2022, Cisco released its Global Broadband Index Report surveying more than 60,000 workers across 30 different markets about their home broadband access, quality, and usage. Below are a few stats that caught my eye:

• 84% use the internet at home for four or more hours each day

• 78% agree that everyone should be able to securely connect to fast and reliable internet regardless of location

• 65% believe access to affordable and reliable broadband will become a major issue in the future

• 58% state that they were unable to access critical services during lockdown due to unreliable internet

In the United States, there are about 20 million who lack access to high-speed broadband services, and some 17 million school children don’t have internet access at home. Ensuring broadband access and affordability are critical to closing the digital divide. The problem is significantly greater in rural areas, where about 19.3% of the total U.S. population resides. In rural areas, the cost to build and deliver broadband internet services are much higher due to lower population density, harsher environments, and other factors.

Bridging the digital divide is a great idea, but who’s going to pay for it?

The good news is the U. S. Federal Government is providing another $62 billion in grant dollars on top of the $38 billion pre-pandemic grants for broadband internet build outs. Along with wireless expansion, the government’s funding focus has also shifted to fiber and this new money, provided by the Infrastructure Investment and Jobs Act (IIJA), is part of a five-year program. This funding makes it easier to scale your network infrastructure because with the government helping to fund the last mile, it allows service providers to upgrade their middle mile as well, to support additional users and increased bandwidth. Using federal grants helps you build up the network backbone that might have otherwise been too costly.

The additional $65 billion seeks to address the digital divide and specifically focuses on groups of people that are “underserved” and “unserved” as defined in the law. By underserved we’re talking about those who are served by lower speed broadband that doesn’t exceed a certain threshold, for example 100 Mbps download by 20 Mbps upload. Unserved refers to those having internet speeds below 25 Mbps download by 3 Mbps upload.

Below are some of U.S. federal programs that are in the middle of funding broadband deployments, waiting on program rules, or still waiting for funding to be appropriated.

The most significant grant program for both public and private entities is the Broadband Equity Access and Deployment (BEAD) with $42 billion set aside for last-mile broadband deployment. This is where both public and private entities can win grant money to deploy broadband to the unserved and underserved. This also means there’s a need for new affiliations like Public-Private Partnerships (PPP) which are contracts between a private party and a government agency to offer a public asset or service such as municipality-provided broadband through a partnership with an internet service provider. PPPs make obtaining right of ways much easier because you’re directly partnering with cities and counties.

PPPs provide many benefits to public entities such as Wi-Fi access and improved broadband for schools, and they help scale the economy because you’re adding subscribers who will consume content, shop online, and seek out other internet-based services. They need ISP partners in order to deliver these benefits.

Knowledge and expertise are key to success

Yet, funding alone is not enough to close the digital divide. You need to determine the right combination of solutions for a particular use case, region, and implementation to get the results you expect. This may require extensive expertise and answering all the questions ahead of time has proved difficult—until now.

Cisco is delivering a new generation of network infrastructure technologies and innovation that provide more capacity and greater flexibility at a lower cost per subscriber, helping to import the economics of the Internet. Here are a few examples:

• Capacity at lower cost with Cisco Silicon One and Routed Optical Networking

• Lower OpEx with simplified networks and automation

• Improved sustainability and flexibility for remote deployment scenarios

• Flexible consumption and payment methods that enable you to pay as you grow

These technologies can make it much easier and less expensive for service providers to expand their offerings in rural regions. Now you can experience them up close and in person at the Cisco Broadband Innovation Center located in Research Triangle Park, NC. This is a perfect opportunity to expand your knowledge and expertise in rural broadband development. Not only will you see how to model and address your own specific use cases, but service providers can also focus on how to be more prepared for grant applications by understanding ways to benefit from Cisco’s next-generation network innovations. And it’s important to remember that federal grants will be awarded to the service providers with the best solutions, so it’s critical to work with a proven company at the forefront of rural broadband development.

Source: cisco.com

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Intelligent alert management

The challenge

In cyber security, we all know what alert fatigue is, and we know there is no silver bullet to get out of it. In our previous incarnation, our product was guilty as well. Who wants to go through 20,000 alerts one by one? And this was just from one product.

Building a detection engine

This article is part of a series in which we will explore several features, principles, and the background behind what we consider to be the building blocks of a security detection engine within an extended detection and response (XDR) product.

In this first article, we’ll start with alert fatigue and how we avoid it through the creation of intelligent alerts.

To manage alert fatigue, we are aware of several traditional approaches. “We only pay attention to High and Critical alerts,” some have said. That certainly helps, but at the expense of bringing more problems aboard. Apart from missing a large chunk of the sometimes-notable message that the security product is trying to convey, the “inbox” of the product becomes a dump of unclosed alerts.

“In your next release, could you add elaborate filters and checkboxes so that I can mass close those alerts?” some have asked. We tried this way, but we found ourselves amidst views containing tables within tables, a very baroque system with the delicacy and simplicity on par with the space shuttle.

“We gave up and got a SIEM and a SOAR!” we heard from others. That is all fine, when one wishes to move their SOC staff from security specialist roles to engineering integrators.

To sum up, we observed that in any case, we were really trading one issue for another. Rather than trying to manage the alert fatigue problem, we switched our approach to avoiding it in the first place. We introduced Alert Fusion.

Alert Fusion

In the Alert Fusion system, the basic unit of work is the alert. Rather than having one alert per each security event, we build the alerts intelligently, to mimic the unit of work of the security analyst.

Here is an example of such a unit of work. It covers two assets, having detected an identical set of threats on both. It’s easy to see that WannaCry, SMB service discovery, and Excessive communication likely go together. While remediating these infections, one might want to have a look at the Emotet infection as well. Altogether, neglecting this this unit of work is considered a critical risk, so it easily makes it to the top of the alert list.

The second example has a single ArcadeYum Threat spanning a larger base of 78 assets. It is one of those pesky browser-altering, additional software promoting things that one might want to eradicate en masse, rather than one by one. Admittedly, it isn’t as problematic as WannaCry though, so it is considered a medium risk.

Altogether, these two alerts cover nearly a hundred significant security events and many more contextual ones. Apart from removing the need for manual correlation, we can immediately discern the nature, the breadth, and the depth of the risks presented.

To sum up, an alert serves to collate findings that the analyst might want to solve in ensemble, either by working on it as an incident or getting rid of it due to reasons of his choosing. To prioritize their work, an alert has a risk, and the alerts are ordered using this value.

The risk, as well as the grouping, are determined automatically by the system using what it knows about the detections. Now, let’s dive deeper into the basic ingredients in the cookbook: the threats and the assets.

Threats

A threat is anything we can name as a security risk. In this example, we feature Shlayer. It is important to note that we express threats in the language of threat intelligence and risk management – “what” has been detected as opposed to the technical language of detection means – “why” was it detected. We’ll cover the exact means of detection in a later article. For now, let’s assume that we somehow detected it.

A threat has a severity, in this example it is critical, which serves as a basis for the risk calculation. Threats come with factory default severities which be changed freely to suit the threat model of each customer. For example, some customers may not care as much about crypto mining on their assets when compared to other customers.

We realize that detection methods are not infallible, especially in the world of machine learning. So, we assign a confidence value when a threat is detected. Currently, it can be either high or medium. The latter means the detector is not quite sure of the detection, so the risk is dialed down.

Assets

Similarly, we organize assets into Asset Groups that bear a business value. The organization is up to the customer and their threat model. Some customers have more diverse needs, while others have more of a flat structure. Where possible, we offer an educated guess of the default value for an Asset Group.  For example, servers get a high value, while guests get a low value. In any case, the values can be changed freely. The medium business value has no impact on the risk, while others will either increase or decrease it accordingly.

Reactive system

In summary, we see that Alert Fusion presents alerts which act as units of work and are prioritized by their risk, calculated from customer-applied settings such as threat severity and asset value.

It wouldn’t be realistic to expect that all configuration, if any, was done to the system upfront. For example, a detection on a guest network might make one realize that the business value of this asset group might need to be lowered.  So, we provide the option to tweak alerts on the fly. We support a reactive workflow model.

The existing alerts may be reorganized at any time by turning a few knobs, namely the threat severity and asset value. This gives the option to explore safely. When not satisfied with the change, simply turn them back, rinse, and repeat.

Wrap-up

So, have we tackled alert fatigue successfully? As the saying goes, time will tell. It is already beginning to do so.

Since this system was introduced in 2020, we have seen a significant reduction in alerts per customer, usually in a few orders of magnitude. Our UI does not have to work as hard, in terms of checkboxes, pagination, and filtering. Consequently, more customers reach the nice-to-be-in place of a zero-alert inbox, where 100% of the alerts have been viewed and interacted with.

Source: cisco.com

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New in SecureX: Device Insights

Since its release, Cisco SecureX has helped over 10,000 customers gain better visibility into their infrastructure. As the number of devices in many customer environments continues to increase, so does the number of products with information about those devices. Between mobile device managers (MDM), posture agents, and other security products, a wealth of data is being collected but is not necessarily being shared or, more importantly, correlated. With the new device insights feature in Cisco SecureX, now available for all SecureX customers, we’re changing that.

Introducing Device Insights

Device insights, which is now generally available, extends our open, platform approach to SecureX by allowing you to discover, normalize, and consolidate information about the devices in your environment. But this isn’t just another dashboard pulling data from multiple sources. Device insights fetches data from sources you might expect, like your mobile device manager, but also leverages the wealth of data available in your Cisco Secure products such as Cisco Secure Endpoint, Orbital, Duo, and Umbrella. Combining these sources of data allows you to discover devices that may be sneaking through gaps in your normal device management controls and gain a comprehensive view into each device’s security posture and management status. With device insights, you’ll be able to answer these all-important questions:

◉ What types of devices are connected in our environment?

◉ What users have been accessing those devices?

◉ Where are those devices located?

◉ What vulnerabilities are associated with each device?

◉ Which security agents are installed?

◉ Is the security software is up to date?

◉ What context do we have from technologies beyond the endpoint?

Supported Data Sources

Now, you might ask: what types of data can I bring into device insights? When we created SecureX, we built a flexible architecture based on modules that anyone can create. Device insights extends this architecture by adding a new capability to our module framework. Here’s a look at what data sources will be supported at launch:

Bringing Everything Together

Once you’ve enabled your data sources, device insights will periodically retrieve data from each source and get to work. Some sources can also publish data in real time to device insights using webhooks. We normalize all of the data and then correlate it between sources so you have one view into each of your devices, not a mess of duplicate information. This results in a single, unified dashboard with easy filtering, a high level view into your environment, and a customizable table of devices (which you can export too!). To see more information about a device, just click on one and you’ll see everything device insights knows, including which source provided which data.

Source: cisco.com

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Cisco SD-Access in Healthcare: A Comprehensive Secure Access Solution for a Changing Industry

The healthcare industry is undergoing unprecedented change. The pandemic has accelerated the process of digitization and the need for an always available and secure digital infrastructure. In particular, Healthcare IT (HIT) faces several significant challenges:

◉ Prevent security breaches across hospitals, clinics, and research centers

◉ Protect patient and research data through standards, integration, and governance

◉ Understand and support technological innovations in healthcare

◉ Provide simple, secure access to data and analytics to all key stakeholders

To address these challenges and support the connectivity and security needs of hospitals, branch clinics, and telehealth, HIT needs to build and maintain a resilient network architecture that is secure, automated, and provides a continuous feedback loop with rich analytics.

Cisco Software-Defined Access (SD-Access) is a network controller-based solution that helps organizations enable policy-based automation to address access control and segmentation. With its broad adoption in healthcare organizations worldwide, a set of use cases and best practices have emerged that demonstrate how HIT is using Cisco SD-Access to address changing network requirements and meet the needs of the healthcare workforce and patients.

Cisco SD-Access

Simplify Network Expansion

Healthcare networks in the modern all-digital world must provide service-level resiliency and be modifiable on demand. Cisco SD-Access provides ample support for site additions and site expansions and is flexible enough to spin up a new site in hours. It provides full lifecycle management of existing campus and branch environments in a simple and secure manner.

SD-Access starts with providing workflows for automating the physical network underlay using the LAN automation capabilities in Cisco DNA Center. Lan automation simplifies network operations and provides a zero-touch plug-and-play workflow. LAN automation can also quickly expand the network using extended nodes to spaces such as parking lots and warehouses.

HIT can build an automated network fabric and seamlessly connect external networks to the fabric borders. The network fabric also provides capabilities for HIT to connect their current networks to the fabric edges and extend security and segmentation benefits. SD-Access enables the creation of new branch and remote sites on-demand―from small fabric in a box for branches to extensive deployments with thousands of switches. It provides zero-touch network automation to bring up the routing underlay along with setting up the fabric and managing day-N operations on the network. All of this is possible through an intent-based network interface in Cisco DNA Center.

Built-in Network Fault Tolerance and Service Resiliency

The healthcare network is mission-critical, requiring minimal downtime. The services and the network must be highly resilient to support healthcare workers and patients. Cisco SD-Access is built on a highly fault-tolerant fabric architecture with redundant elements at all critical points. This includes fully redundant network peering points, control plane elements, StackWise Virtual Links (SVLs), and stacking on edge switches. Additionally, the services are always available through a Cisco DNA Center three-node clustered management system and fully distributed multi-node Cisco Identity Service Engine (ISE). The design of the network is flexible to accommodate even the most stringent needs of healthcare networks.

Secure Segmentation Based on Organizational Functions

Healthcare organizations have separate departments performing different and unique functions. HIT has found it highly useful to segment and secure communication among these different organizational entities.

Beyond communications, healthcare systems must safeguard the medical records and financial information of patients. In the U.S., hospitals and medical centers are required to have Health Insurance Portability and Accountability Act (HIPAA)-compliant wired and wireless networks that can provide complete and constant visibility into network traffic. These networks must protect sensitive data and medical devices such as electronic medical records (EMR) servers, vital sign monitors, and nurse workstations from malicious devices that seek to compromise the network. Prescription drug safes should be able to communicate with respective destinations even during a network impact, such as Cisco ISE being temporarily unavailable. Administrators can implement a critical VLAN for fabric edges, where devices like prescription safes reside, when access verification services are unreachable.

Close collaboration between healthcare staff and instantaneous access to a comprehensive view of health-related data, aggregated and collocated from the many disparate segments, is placing increasing demands on the network infrastructure. Cisco SD-Access architecture provides automated network fabric configurations, identity-based policy and segmentation, AI-driven insights, and telemetry services.

Cisco SD-Access addresses the need for complete data and control plane isolation between patient and visitor devices and medical and research facility devices by using macro segmentation. By onboarding devices into different overlay virtual networks (VNs), healthcare facilities can achieve complete data isolation and provide security among different departments and users.

Provide Rich Network Services

One of the biggest demands on the healthcare IT network infrastructure is to handle guest and patient traffic separate from staff and sensitive patient data. Mobility and roaming across campus buildings are therefore key requirements for healthcare networks. Cisco SD-Access has a built-in Fabric Enabled Wireless (FEW) architecture that enables seamless mobility for endpoints and devices connected to the edge of the network.

In a healthcare facility, various medical devices are in different locations but should be managed in a unified manner for proper usage and availability. SD-Access allows IT to place these devices in a separate virtual network and routed to a common border over a tunneled interface. This provides clean and secure segmentation of anchored traffic to a common exit point in the network.

Another important requirement of healthcare networks today is the ability to access medical records, security camera recordings across sites, staff records, and other sensitive data from a central server. In most cases, these data sets need to be accessible on-demand at a subset of branch sites. Cisco SD-Access helps in creating groups of sites that need to receive these types of records through its built-in multicast features.

Improve Network Visibility and Assurance

Network administrators should be able to efficiently manage and monitor their networks to quickly respond to the dynamic needs of healthcare systems. To improve the performance of a network, attached devices, and applications, the deployment should use telemetry to proactively predict performance and security risks.

Cisco DNA Center with Cisco SD-Access Assurance provides a comprehensive solution that addresses not just reactive network monitoring but also enables proactive monitoring with network health and issue dashboards. In addition to the network, client, and application health dashboards, the SD-Access Health Dashboard provides analytics and insights for both network underlay and fabric overlay by correlating actionable insights based on a wide variety of telemetry data ingested from sources throughout the network.

SD-Access provides visibility insights into the fabric, virtual network health, transit, and peer network connectivity health using a health score metric. The health of the fabric is quantified using Key Performance Indicators (KPIs) of the operational state of the fabric. These KPIs are also used to identify issues in the fabric. The operational data is collected from fabric devices using telemetry.

Source: cisco.com

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Hyperconverged Infrastructure with Harvester: The start of the Journey

Deploying and running data center infrastructure management – compute, networking, and storage – has traditionally been manual, slow, and arduous. Data center staffers are accustomed to doing a lot of command line configuration and spending hours in front of data center terminals. Hyperconverged Infrastructure (HCI) is the way out: It solves the problem of running storage, networking, and compute in a straightforward way by combining the provisioning and management of these resources into one package, and it uses software defined data center technologies to drive automation of these resources. At least in theory.

Recently, a colleague and I have been experimenting with Harvester, an open source project to build a cloud native, Kubernetes-based Hyperconverged Infrastructure tool for running data center and edge compute workloads on bare metal servers.

Harvester brings a modern approach to legacy infrastructure by running all data center and edge compute infrastructure, virtual machines, networking, and storage, on top of Kubernetes. It is designed to run containers and virtual machine workloads side-by-side in a data center, and to lower the total cost of data center and edge infrastructure management.

Why we need hyperconverged infrastructure

Many IT professionals know about HCI concepts from using products from VMWare, or by employing cloud infrastructure like AWS, Azure, and GCP to manage Virtual Machine applications, networking, and storage. The cloud providers have made HCI flexible by giving us APIs to manage these resources with less day-to-day effort, at least once the programming is done. And, of course, cloud providers handle all the hardware – we don’t need to stand up our own hardware in a physical location.

Multi-node Harvester cluster

However, most of the current products that support converged infrastructure tend to lock customers to using their company’s own technology, and they also usually come with licensing fees. Now, there is nothing wrong with paying for a technology when it helps you solve your problem. But single-vendor solutions can wall you off from knowing exactly how these technologies work, limiting your flexibility to innovate or react to issues.

If you could use a technology that combines with other technologies you are already required to know today – like Kubernetes, Linux, containers, and cloud native – then you could theoretically eliminate some of the headaches of managing edge compute / data centers, while also lowering costs.

This is what the people building Harvester are attempting to do.

Adapting to the speed of change

Cloud providers have made it easier to deploy and manage the infrastructure surrounding applications. But this has come at the expense of control, and in some cases performance.

HCI, which the cloud providers support and provide, gets us some control back. However, the recent rise of application containers, over virtual machines, changed again how infrastructure is managed and even thought of, by abstracting layers of application packaging, all while making that packaging lighter weight than last-generation VM application packaging. Containers also provide application environments that are  faster to start up, and easier to distribute because of the decreased image sizes. Kubernetes takes container technologies like Docker to the next level by adding in networking, storage, and resource management between containers, in an environment that connects everything together. Kubernetes allows us to integrate microservice applications with automation and speedy deployments.

Kubernetes offers an improvement on HCI technologies and methodologies. It provides a better way for developers to create cloud agnostic applications, and to spin up workloads in containers more quickly than traditional VM applications. Kubernetes did not aim to replace HCI, but it did make a lot of the goals of software deployment and delivery simpler, from an HCI perspective.

In a lot of environments, Kubernetes runs inside VMs. So you still need external HCI technology to manage the underlying infrastructure for the VMs that are running Kubernetes. The problem now is that if you want to run your application in Kubernetes containers on infrastructure you have control of, you have different layers of HCI to support.  Even if you get better application management with Kubernetes, infrastructure management becomes more complex. You could try to use vanilla Kubernetes for every part of your edge-compute / data center stack and run it as your bare metal operating system instead of traditional HCI technologies, but you have to be ok migrating all workloads to containers, and in some cases that is a high hurdle to clear, not to mention the HCI networking that you will need to migrate over to Kubernetes.

The good news is that there are IoT and Edge Compute projects that can help. The Rancher organization, for example is creating a lightweight version of Kubernetes, k3s, for IoT compute resources like the Raspberry Pi and Intel NUC computers. It helps us push Kubernetes onto more bare metal infrastructure. Other orgs, like KubeVirt, have created technologies to run virtual machines inside containers and on top of Kubernetes, which has helped with the speed of deployment for VMs, which then allow us to use Kubernetes for our virtual networking layers and all application workloads (container and VMs). And other technology projects, like Rook and Longhorn, help with persistent storage for HCI through Kubernetes.

If only these could combine into one neat package, we would be in good shape.

Hyperconverged everything

Knowing where we have come from in the world of Hyperconverged Infrastructure for our Data Centers and our applications, we can now move on to what combines all these technologies together. Harvester packages up k3s (light weight Kubernetes), KubeVirt (VMs in containers), and Longhorn (persistent storage) to provide Hyperconverged Infrastructure for bare metal compute using cloud native technologies, and wraps an API / Web GUI bow on it to for convenience and automation.

Source: cisco.com

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Why Transition to BGP EVPN VXLAN in Enterprise Campus

Network Virtualization Convergence in Enterprise Campus

Campus networks are the backbone of enterprises providing connectivity to critical services and applications. Throughout time many of these networks were deployed with a variety of overlay technologies including technologies to accomplish the desired outcome. While these traditional overlay technologies accomplished the technical and business requirements, many of them lacked manageability and scalability introducing complexity into the network. The industry-standard BGP EVPN VXLAN is a converged overlay solution providing unified control-plane-based layer-2 extension and layer-3 segmentation over IP underlay. The purpose-built technology for Enterprise campus and datacenter addresses the well-known classic networking protocols challenges while providing L2/L3 network services with greater flexibility, mobility, and scalability.

Fig #1: BGP EVPN VXLAN converges Layer 2 and Layer 3

Legacy Layer 2 Overlay Networks Departure

Enterprise campus networks have historically been deployed with several types of Layer 2 overlay network extensions as products and technologies evolved. Classic data-plane based Layer 2 extended networks built upon a flood-n-learn basis can be significantly simplified, scaled, and optimized when migrating away to next-generation BGP EVPN VXLAN solution:

◉ STP – Enterprise campus networks have operated spanning-tree protocol (STP) since its inception. Several enhancements and alternatives have been developed to simplify and optimize STP complexity, however, it continued to be challenging. The BGP EVPN VXLAN replaces STP with an L2 overlay enabling new possibilities to IT including controlling flood-domain size, suppressing redundant ARP/ND network traffic, and seamless mobility while retaining the original IPv4/v6 address plan when transitioning from Distribution switch or centralized firewall gateway running over STP network.

◉ 802.1ad – The IEEE 802.3ad (QinQ) is a common multi-tenant Layer 2 network solution. The dual-stack IEEE 802.1Q header tunnels individual tenant VLANs over limited and managed core VLANs to assist in reducing the bridging domain and overlapping tenant VLAN IDs across the core network. BGP EVPN VXLAN enables the opportunity to transform the Layer 2 backbone network with a simplified IP transport utilizing VXLAN and continue to bridge single or dual-stack IEEE 802.1Q VLAN across the fabric. 

◉ L2TPv3 – Layer 2 Protocol Tunnel version 3 (L2TPv3) provides simple point-to-point L2 overlay extension solution over an IP core between statically paired remote network devices. Such flood-n-learn based Layer 2 overlay networks can be migrated to BGP EVPN VXLAN providing far advanced and flexible Layer 2 extension solutions across an IP core network. 

◉ VPWS/VPLS – The standards ratified several Layer 2 network extensions as the industry evolved towards high-speed Metro-Ethernet networking across MAN/WAN. The Enterprise networks quickly evolve adopting Ethernet over MPLS (EoMPLS) or Virtual Private LAN Service (VPLS) solution operating over IP/MPLS based backbone. The Enterprise network can be simplified, optimized, and resilient with BGP EVPN VXLAN supporting flexible Layer 2 overlay topologies with control-plane based Layer 2 extensions that assist in improving end-to-end network performance and user experience. 

Traditional Layer 3 Overlays Convergence

Like Layer 2 extended networks, segmented Layer 3 networks can be deployed with various overlay technologies. The parallel running protocol set with each supporting either routing or bridging may add complexity as network growth and demands expand linearly. As BGP EVPN VXLAN converges routing and bridging capabilities it assists in reducing control-plane and operational tasks resulting in simplicity, scale, and resiliency.

◉ Multi-VRF – A simple hop-by-hop Layer 3 virtual network segmenting Layer 3 physical interface into logical IEEE 802.Q VLAN for each virtual network small to mid-size network environments. As segmentation requirements increase, IT operational challenges and control-plane overhead to manage Multi-VRF also increase. The BGP EVPN leverages IP VRF to dynamically build a segmented routed network environment and with VXLAN the data-plane segmentation is managed at the network edge enabling simplified underlay IP core and scalable Layer 3 overlay routed network solution. 

◉ GRE – An ideal solution for building overlay networks across IP networks without implementing hop-by-hop in the underlay network. The GRE-based overlay solution supports limited point-to-point or point-to-multipoint topologies.  Following similar principles, the BGP EVPN VXLAN can simplify the network with a single control plane, dynamically build VXLAN tunnels, and supports flexible overlay routing topologies. The ECMP based underlay and overlay networks support best-in-class resiliency for mission-critical networks.  

◉ MPLS VPN – The MP-BGP capabilities have been widely adopted in large Enterprises addressing network segmentation across self-managed IP/MPLS managed networks. The well-proven and scalable MPLS VPN in Enterprise overcomes several alternative technologies challenges using shim-layer label switching solution. The MPLS VPN enabled Enterprise networks can extend existing MP-BGP designs and transition VPNv4/VPNv6 to new L2VPN EVPN address-family supporting seamless migration. The edge-to-edge VXLAN data-plane can converge MPLS VPNs, mVPN, and VPLS overlay into a single unified control plane and enable enhanced integrated routing and bridging function. It further assists in greatly simplifying IP core network without MPLS LDP protocol dependencies across the paths. 

Cisco Catalyst 9000 – Seamless and Flexible BGP EVPN VXLAN Transition

Transitioning from classic products and technologies has never been an easier task, especially when mission-critical downtime is practically impossible. The Cisco Catalyst 9000 combined with 30+ years of software innovation with the industry’s most sophisticated network operating system Cisco IOS-XE® provides great levels of flexibility to seamlessly adapt BGP EVPN VXLAN for Enterprise customers as part of an existing operation or planning to begin a new networking journey while maintaining full-backward compatibility with classic products and overlays networks supporting non-stop business communications. 

Fig #2: BGP EVPN VXLAN design alternatives

The end-to-end network and rich feature integration can be enabled independent of how underlying network infrastructure is built as illustrated above.

	Layer 3 Access	Cisco StackWise Virtual	ESI Layer 2 Multihome
Leaf Layer	Access	Distribution	Distribution
Spine Layer	Core or other
Border Layer	Data Center ACI, WAN, DMZ or more
Overlay Network Type Support	Layer 3 Routed, Distributed AnyCast Gateway (Symmetric IRB), Centralized Gateway (Asymmetric IRB) Layer 2 Cross-Connect
Overlay Unicast Support	IPv4 and IPv6 Unicast
Overlay Multicast Support	IPv4 and IPv6 – Tenant Routed Multicast
Wireless Network Integration	Local Mode – Central Switching FlexConnect Mode – Central and Distributed Local Switching
Data Center Integration	BGP EVPN VXLAN – Common EN/DC Fabric Cisco ACI – Nexus 9000 Border Layer 3 Handoff
Multi-site EVPN Domain	Campus Catalyst 9000 switches extending fabric with Nexus 9000 Multi-site Border Gateway integration
External Domain Handoff	L2: Untag, 802.1Q, 802.1ad, EoMPLS, VPLS L3: Multi-VRF, MPLS VPN, SD-WAN, GRE
Data Plane load sharing	L3: ECMP	L2: Per flow Port-Channel Hash L3: ECMP Multicast:S, G + Next Hop	L2: Per Port-VLAN Load Balancing L3: EMCP Multicast: S, G + Next Hop
System Resiliency	Cisco StackWise-1T Cisco StackWise-480 Cisco StackPower Fast Reload Stateful Switchover (SSO) Ext. Fast Software Upgrade In-Service Software Upgrade (ISSU)	Cisco StackWise Virtual Stateful Switchover (SSO) In-Service Software Upgrade (ISSU)	Stateful Switchover (SSO) In-Service Software Upgrade (ISSU)
Network Resiliency	BFD (Single/Multi-Hop) Graceful Restart Graceful Insertion	L2: EtherChannel, UDLD, etc. BFD (Single/Multi-Hop) Graceful Restart Graceful Insertion	L2: UDLD, etc. BFD (Single/Multi-Hop) Graceful Restart Graceful Insertion

Scalable Architecture Matters

IT organizations adopting the BGP EVPN VXLAN solution must consider how to scale multi-dimensionally when building large-scale fabrics. This demands call-to-action to design the right architecture based on proven principles in the networking world. Regardless of physical or virtual networking, it shall be designed with an appropriate level of hierarchy to support the best-in-class scalable solution supporting a large enterprise network. The smaller fault domains and condensed network topologies in core-layer enable resilient networks are well-known benefits of hierarchical networking.

As the number of EVPN leaf nodes increases overlay prefixes and the blast radius in the network grows. The network architects shall consider building a structured Multi-Site overlay networking solution allowing Enterprise campus to grow by dividing fabric domains in different boundaries and using fabric border gateways to interconnect all together.

Stay tuned we’ll share more thoughts on how Cisco Catalyst 9000 and Nexus 9000 can bring next-generation BGP EVPN VXLAN with Multi-site solutions. And as always, if you are already on the journey to design and build a scalable end-to-end BGP EVPN VXLAN campus network, then simply reach out to your Cisco sales team to partner with you and enable the vision. 

Source: cisco.com

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Why Automation Will Unlock The Power of AI in Networking (Part 1)

You have probably heard about the old adage “Correlation does not imply causation”. This idea that one cannot deduce a causal relationship between two events merely because they occur in association has a cool latin name: cum hoc ergo propter hoc (“with this, therefore because of this”), which hints at the fact that this adage is even older than you might think.

What most people don’t know is that all the cool deep learning algorithms out there actually fall prey to this fallacy. No matter how fancy they are, these algorithms merely rely on association, but they have no common sense (which can be thought of as some kind of causal model of the world).

In this article, we will explore a few key ideas around the topics of correlation and causality, and more importantly, why you should care about this and how automation can help us in this regard!

Correlation by chance

If you have an interest in data analytics or statistics, you have probably come across the concept of spurious correlations. This term has been coined by the famous statistician Karl Pearson in the late 19th century, but has been recently popularized by the Spurious Correlations website (and book) by Tyler Vigen, which offers many examples such as this one:

Here we observe that the number of non-commercial space launches in the world happens to match almost perfectly the number of sociology doctorates awarded in the US every year (in terms of relative variation, not in absolute value). These examples are of course meant as jokes, and this makes us laugh because it goes against common sense. There isn’t any connection between space launches and sociology doctorates, so it is pretty clear that something is wrong here.

Now, examples such as this one are not exactly what Karl Pearson had in mind when he coined the term, because they are the result of chance rather than a common cause. Instead, we are dealing with a problem of statistical significance: although the correlation coefficient is nearly 79%, this is based only on 13 data points for each series, which makes the possibility of correlation by chance very real. Actually, statisticians have designed tools to compute the probability that two completely independent processes (such as space launches and sociology doctorates) produce data that have a correlation at least as extreme as a given value: statistical testing (in which case this probability is called a p-value). 

I applied a statistical test for the above example (see this notebook if you want to test it yourself and see other examples), and I obtained a p-value of 0.13%. I also tested this result empirically by generating one million random time-series and counting how many such time-series had a correlation with the number of worldwide non-commercial space launches higher than 78.9%. No surprises here, I get roughly 0.13% of my trials falling in that category. This summarized in this figure:

One important lesson here is: by searching long enough in a large dataset, you will always find some examples of nicely correlated examples. By no means you should conclude that there is some actual relation between them, let alone some causation!

Correlation due to common causes

Now, you can be in a situation where not only the correlation is high, but the sample count is also high, and statistical testing will be of no help (that is, in the above example, you would never be able to generate a random time-series more correlated than your real data). Yet, you cannot conclude that you are in presence of a real situation of causation!

To illustrate this fact vividly, consider the following (made up) example featuring two processes: process A generates a time-series and process B generates discrete events. A realization of these processes is shown below:

We observe a systematic build up of time-series A, followed by an event B. For the sake of the illustration, let us assume that we have a very large dataset of such time-series and event data, and they all look pretty much like my diagram. The above example has a correlation of 27.62% and an infinitesimal p-value, which rules out correlation by chance. The build up of A happens prior to the event B, so it seems clear that it is a cause of B, right?

But what if I told you that A represents the number of people observed on a platform in a train station and that B corresponds to the arrival of a train on this platform? Then it all makes sense of course. Passengers accumulate on the platform, the train arrives, and most passengers hop on the train. Does that mean that the passengers cause the train to arrive? Of course not! These processes do not cause each other, but they share a common cause: the timetable!

Source: cisco.com

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