Automating Your Network Operations, Part 1 – Ansible Basics

I’ve spent the last couple of years at Red Hat helping customers automate their networks with Ansible. If there is one thing that I’ve learned during that time, it is that network automation is not as easy as many would have you believe. That is not to say that tools like Ansible are not good tools for automation or that anyone is trying to sell you snake oil, but I believe that there is a fundamental impedance mismatch in translating the success Ansible has had with automating systems to automate networks.

Part of this disconnect stems from a fundamental mis-understanding of the capabilities that Ansible provides. According to Red Hat, Ansible is a “common language to describe your infrastructure.” In practice, however, Ansible is more of a framework that brings an inventory of things together with a set of modules, plugins, and Jinja2 capabilities that perform operations on those things. The language, rendered in YAML or JSON, just passes key/value pairs between the modules, plugins, and Jinja2 capabilities. (Yes, that’s a simple description of a complex tool, but one that is accurate to illustrate the point of this and subsequent blogs.)

That is not to say that Ansible is not a powerful framework, but it has no native linguistic ability to describe a network. When I say an “inventory of things,” it is because Ansible really does not care what that thing is. Because of its agentless approach, it can talk to many things: systems, network devices, clouds, lightbulbs, etc. This is a great capability and part of why Ansible is so popular, but Ansible truly does not know one thing from another. It has no innate prowess for automating networks. It is simply a tool for automating what an operator does task by task. You cannot “describe” what you want OSPF to look like on your network. You simply provide a bunch of key/value pairs that get passed to the devices on your network through modules in hopes of yielding the OSPF configuration that you want.

Configuring settings on an IOS device

To illustrate this, let’s look at configuring two simple settings on an IOS device: hostname and NTP servers. Using Ansible parlance, we’ll describe the desired end state of the hostname of a particular device. Hostname is a great use case because it is a scalar (i.e. a single value). To change the hostname, the Ansible ios_config module does a simple textual compare of the configuration. If ‘hostname newname’ is not present, it sends that line to the device. Since hostname is a scaler, the old hostname gets replaced by the desired hostname.

A list of NTP servers, however, is more difficult. Say you’ve set the NTP server to 1.1.1.1 with:

- ios_config:
   lines:
    - ntp server 1.1.1.1

Now you want to change your NTP server to 2.2.2.2, so you do:

- ios_config:
   lines:
    - ntp server 2.2.2.2

Simple, right? But the problem is that you would end up with 2 NTP servers in the configuration:

ntp server 1.1.1.1
ntp server 2.2.2.2

This is because the Ansible ios_config module does not see `ntp server 2.2.2.2` present in the configuration, so it sends the line. Since ntp server is a list, however, it adds a new NTP server instead of replacing the existing one, giving you 2 NTP servers (one that you do not want). To end up with just 2.2.2.2 as your NTP server, you would have to know that 1.1.1.1 was already defined as an NTP server and explicitly remove it… exactly what an operator would do. This is also the case with ACLs, IP prefix-lists, and any other list in IOS. Ansible does not have a native way to describe the desired end state of something simple like NTP servers on a network device, much less something more complex like OSPF, QoS, or Multicast.

Does that mean that Ansible is not a great tool for network automation? No, but like any tool, it needs to be used for the right task and can only complete a complex task when used in concert with other tools. As a framework, it is not a complete solution.

The intent of this blog series is to go beyond the hype and simple demonstrations prevalent in network automation conversations today and to dive more deeply into how and why to automate your network operations. In the next installment, I’ll talk about data models and why they are a critical piece of any automation framework.

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AI Ops and the Self-Optimization of Resources

AI Ops includes the ability to dynamically optimize infrastructure resources through a holistic approach. Cisco Workload Optimization Manager is an important component in our strategy of delivering enhanced customer benefits through AI Ops.

Our Strategy for Delivering the Benefits of AI Ops

Cisco is executing a strategy to consistently enhance the customer benefits we deliver through AI-driven Operations (AI Ops). This blog is the latest in a series that describes our strategy, our open architecture, and how we are implementing each of the benefits. In the first blog in this series we defined four categories of benefits from AI Ops:

1. Improved user experience
2. Proactive support and maintenance
3. Self-optimization of resources
4. Predictive operational analytics

Multi-Dimensional AI Ops Strategy

Vendors use the terms AI, machine learning and AI Ops in a variety of ways. Their focus is primarily on hardware. Our strategy for delivering the customer benefits of AI Ops is a broader architectural vision. This vision includes infrastructure, workloads, and enhanced customer support in on-premises and cloud environments. Cisco’s strategy incorporates an open API framework and integrations with Cisco and partner platforms.

Infrastructure management is one dimension of AI Ops, and Cisco Intersight is an integral component of Cisco’s strategy. Managing workloads is another essential dimension, so Cisco Workload Optimization Manager (CWOM) is also an important component of this strategy.

AI Ops Portfolio Working Together

In a prior blog we explained how Intersight delivers an AI-driven user experience through our open API framework. We posted two blogs in this series to explain how Intersight delivers benefit #2, AI-driven proactive support and proactive maintenance. The proactive support is enabled through the Intersight integration with the Cisco service desk digital intelligence platform. This AI platform (internally referred to as BORG) is  used by the Cisco Technical Assistance Center. It includes AI, analytics, and machine learning. In this blog, I explain how we deliver benefit #3, the self-optimization of resources, through monitoring and automation with Cisco Workload Optimization Manager.

Self-Optimization of Resources

The self-optimization of resources includes both on-premises and public cloud infrastructure. You need to monitor and automate across a variety of virtualized environments, containers and microservices.

In order to ensure that your applications continuously perform, and your IT resources are fully optimized, you need full visibility across compute infrastructure and applications, across networks and clouds…. and you need all this intelligence at your fingertips, so you can quickly and easily make the right decisions, in real-time to assure application performance, operate efficiently and maintain compliance in your IT environment.

Cisco Workload Optimization Manager is an AI-powered platform that delivers this functionality through integrations with Cisco’s multicloud portfolio, ACI, UCS management, HyperFlex, and a broad ecosystem of partner solutions that will continue to grow over time.  CWOM continuously analyzes workload consumption, costs and compliance constraints and automatically allocates resources in real-time.

How Does AI Ops Work?

Resource allocation, workload scheduling and load balancing are concepts that have been critical to efficient IT operations for decades. Workload Optimization Manager uses AI and advanced algorithms to manage complex multicloud environments. It views on-premises resources and the cloud stack as a supply chain of buyers and sellers. CWOM looks for the options for running workloads and manages the resources as “just in time” supply to cost-effectively support workload demands, helping customers maintain a continuous state of application health.

CWOM showing cost analysis of pending actions

Many AI Ops solutions are complex to deploy, and they require require a significant amount of time to accumulate information before they can be effective for analysis. Workload Optimization Manager is easy to install, and the agentless technology will instantly begin to detect all the elements in your environment from applications to individual components. The unique decision engine curates workload demand, so it can generate faster, accurate recommendations after collecting data for a short period of time. CWOM uses three categories of functionality to optimize the use of available resources:

Abstraction: All workloads (applications, VMs, containers) and infrastructure resources (compute, storage, network, fabric, etc.) are abstracted into a common data model, creating a “market” of buyers and sellers of resources.

Analysis: A decision engine applies the principles of supply, demand, and price to the market. There are costs associated with on-premises infrastructure resources, and cloud providers price their resources based on utilization levels. The analytics ensure the right resource decisions are made at the right time.

Automation: Workloads are precisely resourced, automatically, to optimize performance, compliance and cost in real-time. The workloads become self-managing anywhere, spanning on-premises to public cloud environments.

These combined capabilities enable IT to assure application performance, at the lowest cost, while maintaining compliance with policy – from the data center to the public cloud and edge.

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Accelerating Enterprise AI with Network Architecture Search

AI/ML is a dominant trend in the enterprise. While AI/ML is not fundamentally new, the ubiquity of large amounts of observed data, the rise of distributed computing frameworks and the prevalence of large hardware-accelerated computing infrastructure has lead to new wave of breakthroughs in AI in the last 5 years or so. Today enterprises are rushing to apply AI in every part of the organization for a wide range of task, from making better decisions, to optimizing their processes.

However, to reap the benefit of AI, one needs significant investments into teams who understand the entire AI lifecycle, especially how to understand, design and tune the mathematical models that apply to their use cases. Often these models use bespoke techniques that are known to a select few who are highly trained in the field. Without this tuning, an enterprise can spend lots of opex running models by following the canonical models. How can we help the enterprise accelerate this step? One way is AutoML

AutoML is a broad class of techniques that help to solve the pain of iterative designing and tuning of models without the personnel investment. It ranges from tuning an existing model (e.g. in hyper parameter search) to designing new network models automatically. For those leveraging Deep Learning, one way is to use Neural Architecture Search (NAS), which aims to find the best neural network topology for a given task, automatically.

In recent years, several automated NAC  methods have been proposed using techniques such as evolutionary algorithms and reinforcement learning. These methods have found neural network architectures that outperform bespoke, human designed architectures on problems such as image classification and language modeling and have improved the state of the art on accuracy.  However, these methods have been largely limited by the resources needed to search for the best architecture.

We present a method for NAS called Neural Architecture Construction (NAC) – it is a automated method to construct deep network architectures with close to state of art accuracy, in less than 1 GPU day — faster than current state of the art neural architecture search methods.  NAC works by pruning and expansion of a small base network called an EnvelopeNet. It runs a truncated training cycle and compares the utility of different network blocks and prunes and expands the base network based on these statistics.  Most conventional neural architecture search methods iterate through a full training cycle of a number of intermediate networks, comparing their accuracy, before discovering a final network. The time needed to discover the final network is limited by the need to run a full training and evaluation cycle on each intermediate network generated, resulting in large search times. In contrast, NAC speeds up the construction process because the pruning and expansion can be done without needing to wait for a full training cycle to complete.

Figure 1: Results comparing our NAC with other state of the art work. Note the search time for both the dataset. The NAC numbers for ImageNet are preliminary.

Interestingly, our NAC algorithm mirrors theories on the ontogenesis of neurons in the brain. Brain development is believed to consist of neurogenesis, where the neural structure initially develops, gradually followed by apoptosis, where neural cells are eliminated, hippocampal neurogenesis, where more neurons are introduced, and synaptic pruning, where synapses are eliminated. Our NAC algorithm consists of analogous steps run in iterations: model initialization with a prior (neurogenesis), a truncated training cycle, pruning filters (apoptosis), adding new cells (hippocampal neurogenesis), and pruning of skip connections (synaptic pruning). Artificial neurogenesis has been previously studied as, among others, a method for continuous learning in neural networks.

We also open sourced a tool called AMLA, an Automated Machine Learning frAmework for implementing and deploying neural architecture search algorithms.  AMLA is designed to deploy these algorithms at scale and allow comparison of the performance of the networks generated by different AutoML algorithms. Its key architectural features are the decoupling of the network generation from the network evaluation, support for network instrumentation, open model specification, and a microservices based architecture for deployment at scale. In AMLA, AutoML algorithms and training/evaluation code are written as containerized microservices that can be deployed at scale on a public or private infrastructure. The microservices communicate via well defined interfaces and models are persisted using standard model definition formats, allowing the plug and play of the AutoML algorithms as well as the AI/ML libraries. This makes it easy to prototype, compare, benchmark, and deploy different AutoML algorithms in production.

To help users incorporate NAS into their regular AI/ML workflows, we are working on integrating our NAS efforts into Kubeflow, an opensource platform to simplify the management of AI/ML lifecycles on Kubernetes based infrastructure. Once integrated, these NAS tools will help users optimize network architectures in addition to hyper parameter optimization (e.g. Katib tool within Kubeflow).

We believe that this is just the tip of the iceberg (of AutoML and NAS in particular). However these early results have given us confidence that we can design better mechanisms for AutoML that require less resources to operate, in a step towards accelerating the adoption of AI in the enterprise.

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Agentless threat detection for Microsoft Azure workloads with Cisco Stealthwatch Cloud

Detecting threats, such as malware, rogue insiders, and credential abuse, within public cloud environments can be a major challenge. Traditionally, organizations have tried to solve this problem by overlaying a patchwork of agents across the cloud surface to detect bad activity. This approach requires significant costs and effort to deploy, maintain, and manage in dynamic environments such as the cloud.

Instead organizations need a quick, automated, and easy security solution to monitor their cloud environments. Cisco Stealthwatch Cloud can now deliver behavioral security analytics, comprehensive visibility, and effective threat detection in Microsoft Azure environments in minutes.

With the support of network security group (NSG) flow logs version 2 in Azure, Stealthwatch Cloud can deploy in as little as 10 minutes via API with no need for an agent. The update to Flow logs v2 is currently being rolled out to Azure public regions. It is currently available in the Azure West Central US region, with deployment continuing over the next coming weeks.

Here are a few key benefits of Stealthwatch Cloud:

Low-noise, effective threat detection

One of the most resource-draining problems for a security team is false alerts. According to the Cisco 2018 Annual Cybersecurity Report, only 56 percent of security alerts are investigated, and of those, only 34 percent are deemed legitimate alerts. A noisy security solution consumes security analysts’ time, and even worse, may lead to real threats going undetected or uninvestigated.

Stealthwatch Cloud was purpose-built to be a low-noise, valuable security solution. Every time a customer closes an alert, they see the most important dialog window in Stealthwatch Cloud:

To date, 94 percent of Stealthwatch Cloud alerts are rated “helpful” by customers. In other words, when Stealthwatch Cloud asks for your attention, there is a good chance you will be glad you investigated it.

Easy, agentless deployment

With NSG flow logs version 2, which is currently being rolled out, Stealthwatch Cloud can now be deployed in Azure environments without the need for agents or sensors. This allows for quick and easy deployment. In many cases, Stealthwatch Cloud can be deployed in 10 minutes or less with minimal setup or configuration. In addition, this allows Stealthwatch Cloud to keep up with highly dynamic environments and maintain a record of resource activity even after they have been torn down.

Low-configuration needs

Organizations adopt the cloud because it is cost-efficient, highly available, and automated. Security solutions that require heavy management and oversight can undercut these benefits. If a solution requires someone to manually classify resources and configure analytics, it can quickly become a full-time job in the ever-changing environment of the public cloud.

Stealthwatch Cloud is a cloud-native solution that requires minimal oversight and management to be valuable. Using entity modeling technology, Stealthwatch Cloud automatically determines what role each resource plays on the network and how it normally behaves. It then uses this model to identify suspicious and known-bad behaviors. For instance, if a cloud resource never communicates with outside hosts on the Internet, but one day starts sending a large amount of data to a never-before-seen server, it could be a sign of data exfiltration. Stealthwatch Cloud would detect this information in real time and trigger an alert on it.

In addition, Stealthwatch Cloud is a software as a service (SaaS). There is no need to maintain hardware or apply patches, and new features are added monthly. It is available in subscription usage-based or term-based licenses.

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How to Turn Customer Obsession into Customer Success

If you are older than about 30 years old, you will probably remember the VCR—or video cassette recorder. When they first came out these devices were big, bulky and difficult to use. In order to program your VCR you had to consult the manual (400 pages long and written in poor English), press a multitude of buttons in the correct sequence, and hope for the best. Often, you would return home to find that your unmissable TV show or sports match had failed to record.

In those days, you might have taken this as your own technical inadequacy and blamed yourself. Today, we would blame the brand.

Expectations have changed so much in consumer technology that companies now know that their products must be simple, straightforward and even fun to use.

The same shift has happened in business technology. Years ago, in the early days of multiprotocol networking, Cisco equipment was notoriously hard to use. Engineers built it for engineers, with a lot of technical bells and whistles, but not a lot of user friendliness. Today, it’s a different story. You can pretty much take a Cisco Telepresence unit out of the box, plug it in and use it. Our “Network Intuitive” largely configures itself based on how you intend to use it. And connecting to a new wireless network is virtually automatic.

Technology vendors have made huge investments in usability because, as in consumer products, when something doesn’t work right, customers no longer blame themselves, they blame the brand.

That is why “customer obsession” has become such an industry buzzword.

But in order to meet a customer’s needs we first have to have a more nuanced and granular understanding of who the customer is.

In most enterprises, the decision maker for big technology purchases is the CIO or CTO. But is that the “customer”? What’s important to the CIO might be completely different from what’s important to the system engineer who has to pull the equipment out of the box and install it, or the person who uses it. Today, the customer is not just the buyer, and not just the installer, but all the different people who touch the product—the people who benefit from using it, the people who administer and support it, the people who secure it. If our “customer obsession” doesn’t extend past the person who signs the check, we miss opportunities to delight our customers—the full range of them!

Taking a much broader view of who the “customer” is and how they might interact with our products must be a guiding principle of innovation. For the past three years, I’ve led an innovation team at Cisco that develops technologies and business ideas in partnership with some of our largest customers—a process we call CHILL (Cisco Hyperinnovation Living Labs). When creating a new innovation, we always bring together everyone who might touch or interact with the product—from the CEO to the warehouse worker—and include them in the process.

For example, a CHILL lab focused on healthcare included industry leaders and corporate executives from major healthcare companies and corporate benefits providers. But it also included cancer patients and their immediate caregivers—because the solutions we developed had to work for both provider and recipient.

During this process, patients themselves gave us some invaluable feedback. They told us that, wherever possible, they would prefer to find ways of managing their own care and utilizing the support of their extended network of friends, family, and caregivers rather than relying solely on more frequent hospital visits and medical intervention. This insight caused us to dramatically change direction, and we created CircleOf, a startup that provides an integrated solution for enterprises, benefits providers, patients, and their caregivers.

These kinds of direct conversations with customers often yield insights that are surprising and counter-intuitive. And it is these insights that have the potential to bring the most value for Cisco and the most success for its customers.

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Cisco Empowers Partners to Accelerate Their IoT Business

There’s no better time to be in IoT than now! I’m thrilled to be here in Las Vegas at Cisco Partner Summit to share stories I hear from our customers about how IoT is changing their businesses. And to tell you – our partners – what we are doing here at Cisco to accelerate those IoT opportunities.

Let me start by announcing the exciting news that IoT is now a recognized architecture at Cisco. What does that mean for you? Now, Cisco offers you a dedicated IoT specialist team, with more engineering support and a dedicated sales force. It also means comprehensive training, certifications, and resources to help our partners thrive. It’s a clear indication of Cisco’s commitment to the IoT business – for customers, Cisco, and our partners.

We’ve also unveiled focused sales plays and resources to help you sell Cisco IoT solutions.

More for IT

As customers look to extend their connectivity beyond office spaces to warehouses, parking lots, roadways and transportation hubs, Cisco is likewise extending Intent-Based Networking (IBN) into these non-carpeted spaces with purpose-built gear for IoT connectivity and use cases. Both you and your customers can take the “Extended Enterprise”– your familiarity and knowledge of our networking and security products– into these new spaces.

We’ve been hearing more and more from our partners who are leveraging the Extended Enterprise approach. Logicalis, a Cisco UK-based partner specializing in managed services and global IT solutions, offers Cisco IoT solutions to help grow business with existing customers. According to Rüdiger Rath, Head of Europe, Logicalis Group, “Many customers need to connect things at warehouses, transportation hubs, and plant floors. They are excited that Cisco has a full suite of switches and routers purpose-built for this need – and that they can simply extend their enterprise network into these new spaces – and administer these new connections with the same management tools and security policies they use now.”

A Bridge to Operational Technology (OT)

Our IoT solutions open the door to new opportunities within your accounts – specifically the OT sides of your customers’ businesses. Our bundled solutions help enable customers to securely connect their remote and mobile assets.

A great story of delivering OT results comes from Swisscom, a Cisco partner and the largest service provider in Switzerland.  Swisscom is responsible for the operation of the St. Gallen Police Department’s network.  The St. Gallen Police Department has extended their network and deployed an integrated IoT solution so officers can now access Wi-Fi from their police vehicles and seamlessly connect to critical business applications. Whether police officers are working in an office, in the department, or out of their police vehicle, they get the same level of secure connectivity to perform their operational duties.

Computing at the IoT Edge

Increasingly customers want to leverage the telemetry and data that their assets generate at the network’s edge to improve their business operations. With this in mind, Cisco has a rich portfolio of IoT edge-ready productsand an edge compute software framework to help ISVs and partners to enable real-time data collection and action at the edge.

A great example comes from OSIsoft, who have tested and validated their Pi Connectors running on Cisco’s edge compute framework and IR829 with great results. Tim Johnson from OSIsoft said, “The Cisco edge computing platform is a great complement to OSIsoft’s operational data infrastructure as it provides our mutual customers the ability to securely install, update and manage our real-time data connectivity software running on Cisco devices in the field and manage the devices from a centralized, cloud-based dashboard.”

Training and Certification

To help our partners maximize these opportunities, we’ve built an all-new IoT training and certification program for partners designed to help you accelerate your IoT practice with the skills you already have.

Italtel, a multinational information and communication technology Cisco partner based in Italy, utilizes the integrated combination of Cisco IoT hardware and software to help customers securely connect devices and help get the data where it needs to go. According to Ascione Camillo, VP of Strategic Alliance & CTO of Italtel, “Our customers are eager to derive real value from their industrial sensor data to increase efficiency, reduce costs, and accelerate their growth. Cisco’s IoT portfolio provides the foundation we need to support their IoT initiatives – and grow our own business.”

Development and Integration Resources

We have also introduced a dedicated IoT DevNet site with more advanced software development resources and new data practices. As you continue to grow and innovate alongside your customers, you can leverage the rich set of resources at the DevNet site to integrate network and software into IoT solutions for them.

Winning Together with IoT

Cisco remains committed to helping our partners succeed in selling IoT solutions. I encourage you to leverage the skills and knowledge you already have to extend your customers’ enterprises now. Enroll in our new IoT certification and training program to gain new skills and uncover new opportunities from OT buyers. Learn about our new IoT offerings so you can show customers how to deliver real business value with IoT. Together we can continue to capitalize on this huge IoT opportunity! Here’s to a great end of year and 2019!

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Digital Communications in India: Global Aspirations, Local Economics

When I left India to work in the US (1997), little did I realize that I would reconnect with her in a way unimaginable then!

At the time, less than 1% of India’s population had access to the Internet at home.  Since then, Internet connectivity in India has exploded, growing to 426 million users as of January 2018.  And it’s only going to get bigger. India’s country rank in mobile broadband data consumption jumped from 150th to 1st in the world between 2016 and 2017. If we look at IPv6 deployment today (IPv6 is the most recent standard to send information over the Internet), almost half of the world’s users are in India—more than any other country in the world.

India’s economy is gaining huge momentum from this digital intensification.  Gross domestic product has grown from $350B in the 1990s to more than $2.8T today, and this is having a significant socio-economic impact. Every second, three Indians experience the Internet for the very first time. This means a great deal to a farmer or a student—it’s an equalizing opportunity and it’s empowering them. Digital growth will help create jobs, promote innovation, positively influence the GDP, and enable citizen welfare.

Source: Asia Pacific Network Information Centre

From a global perspective, we can characterize the last two decades as an era where we worked relentlessly to scale the internet—scaling to connect the unconnected and to connect everything beyond that requires cost-effectiveness and automation at a level never achieved before. And that requires innovation.

While one school of thought in the industry is that the networking space (routers and switches especially) is mature, innovation has only accelerated in this space. Over the last three to four years of internet build-out, especially in the mobile broadband space, India’s Digital Communication Sector has pushed the envelope in terms of scale and cost (effectiveness), providing world class internet access to the masses over mobile connections.

I would like to share a few important observations. First, it is critical to understand a very important aspect about India. The aspirations of the Indian consumer are no less than the global consumer of digital communication services. However, the economics are radically different. This implies building networks and infrastructure that is second to none.

Second, delivering and operating this requires vision, execution, and courage. My salute to the digital communication providers in India, especially their technology teams, for driving significant innovation and delivering mass-scale, cost-effective networks.

Third, this requires delivering network technology that is better, faster, a lot cheaper, and ruthlessly automated to successfully serve more than 1 billion people.

Cisco has been fortunate to be a part of the rapid scaling of the Internet in India, and during this journey, we have created products, tools, and organizational models that have relevance across the globe. At Cisco, we focused on helping providers in India achieve scale in a way that was difficult to imagine just three years ago.

Some of the important capabilities that Cisco developed to enable IP network scaling in India include:

1. Driving the lowest possible cost per gigabit innovation in silicon, systems, and optics.

2. Enabling flexible consumption of bandwidth in the right form factors – from very small to very large.

3. Delivering environmentally hardened systems to cover the length and breadth of this vast country with ecological challenges and variations.

4. Creating a consistent network architecture (end2end IP/MPLS) followed by building and operating some of the largest scale IP networks in the world.

5. Developing total and complete automation, from the initiation of a network element to deploying a complete network service and its monitoring.

6. Reducing layers and hops in networks to bring users as close as possible to content. Eliminating separate optical networks in high density metro areas.

7. Distributing subscribers to reduce blast radius and improve resilience—using virtualization only where it makes economic sense.

8. Integrating services and product teams on site to deliver automation capabilities at the speed and scale of India.

9. Listening continuously to and innovating in a rapidly evolving market.

Cisco’s participation in the mobile Internet boom in India has many positive spinoffs. The capabilities and ideas developed here have global relevance and are being leveraged beyond India and across the world today.

As the digital communication and services space evolves and scales further within India, we can look forward to the next round of innovation. While connectivity will continue to expand, we will see the network be even more relevant as the whole country and its citizens experience a digital revolution. New services will reach every home, every office, and every premise, and with these new services will come new opportunities for economic development and new challenges.

Moving forward from secure connectivity at scale to secure services at scale over these connections will be an exciting adventure. Cisco is ready to help build this bridge to the future, and India, of course, is!

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