Challenge Your Inner Hybrid Creativity with Cisco and Google Cloud

In recent years, Kubernetes has risen up in popularity, especially with the developer community. And why do developers love Kubernetes? Because it offers incredible potential for speed, consistency, and flexibility for managing containers. But containers are not all sunshine and roses for enterprises – with big benefits come some big challenges. Nobody loves deploying, monitoring, and managing container lifecycles, especially across multiple public and private clouds. On top of that, there are many choices when it comes to environments, which can also create a lot of complexity – there are simply too many tools and too little standardization.

Production grade container environments powered by Kubernetes

That’s why earlier this year Cisco launched the Cisco Container Platform, a turnkey-solution for production grade container environments powered by Kubernetes. The Cisco Container Platform automates the repetitive functions and simplifies the complex ones so everyone can go back to enjoying the magic of containers. The Cisco Container Platform is a key element of Cisco’s overall container strategy and another way Cisco provides our customers with choices to various public clouds.

Figure 1: Cisco Hybrid Cloud for Google Cloud

Hybrid cloud applications are the next big thing for developers

At the beginning of the year Cisco joined forces with Google Cloud on a hybrid cloud offering that, among other things, allows enterprises to deploy Kubernetes-based containers on-premises and securely connect with Google Cloud Platform.

In July at Google Cloud Next ’18, we kicked off the Cisco & Google Cloud Challenge.  (You still have until November 1, 2018 to enter the challenge and win prizes.) The idea behind it is to give developers a window into the possibilities for building hybrid cloud applications. Hybrid cloud applications are the next frontier for developers. There are so many innovation possibilities for the hybrid cloud infrastructure. That’s why we even named it “Two Clouds, infinite possibilities.”

Figure 2: Timeline for the Cisco & Google Cloud Challenge

An IoT edge use case for inspiration

Consider the following use case –assume we have a factory which generates a huge amount of data from sensors deployed across the physical building. We would like to analyze that data on-premises, but take advantage of cloud services in Google Cloud Platform for further analysis. This could include running predictive analysis with Machine Learning (ML) on that data (i.e., which machine part is going to break next). “Edge” here represents a generic class of use cases with these characteristics:

◈ Limited Network Bandwidth – Many manufacturing environments are remote, with limited bandwidth. Collecting data from hundreds of thousands of devices requires processing, buffering, and storage at the edge when bandwidth is limited. For instance, an offshore oil rig collects more than 50,000 data points per second, but less than 1% of this can be used in business decision making due to bandwidth constraints. Instead, analytics and logic can be applied at the edge, and summary decisions rolled up to the cloud.

◈ Data Separation & Partitioning – Often data from a single source needs to go to different and/or multiple locations or cloud services for analytics processing. Parsing the data at the edge to identify its final destination based on the desired analytics outcome allows you to route data more effectively, lower cloud costs and management overhead, and provide for the ability to route data based on compliance or data sovereignty needs. For example sending PCI, PII, or GDPR classified data to one cloud or service, while device or telemetry data routes to others. Additionally, data pre-processing can occur at the edge to munge data such as time series formats into aggregate, reducing complexity in the cloud.

◈ Data Filtering – Most data just isn’t interesting. But you don’t know that until you’ve received it at a cloud service and decide to drop it on the floor. For example, fire alarms send the most boring data 99.999% of the time. Until they send data that is incredibly important! There is often no need to store or forward this data until it is relevant to your business. Additionally, many data scientists now desire to run individually trained models at the edge, and if data no longer fits that model or is an exception, to send the entire data set to the cloud for re-training. Filtering with complex models also allows intelligent filtering at the edge that support edge decision making.

◈ Edge Decision Making & Model Training – Training and storing ML models directly at the edge allows storing ephemeral models that may otherwise not be possible due to compliance or data sovereignty requirements. These models can act on ephemeral data that is not stored or forwarded, but still garner information and outcomes that can then be sent to centralized locations. Alternatively, models can be trained centrally in the cloud and pushed to the edge to perform any of the other listed edge functions. And when data no longer fits that model (such as collecting long tail time-series data) the entire data set can be aggregated to the cloud for retraining, and the model re-deployed to the edge endpoints.

Figure 3: Hybrid Cloud, Edge Compute Use-case

As a real-life example, here in Cisco DevNet, we developed a use-case for doing Object Recognition using video streams from IP cameras. The video gateway at the edge analyzed the video streams in real-time, did object detection at the edge and passed the object to the Cisco Container Platform which further did object recognition. The recognized object, and all the associated meta-data, were stored at this layer. An application to query this data was written in the public cloud to track the path of the object.

Give the Cisco & Google Cloud Challenge a try

There’s no doubt about the popularity of Kubernetes in the developer community. Cisco Hybrid Cloud Platform for Google Cloud takes away the complexity of managing private clusters and lets developers concentrate on the things they want to innovate on. Start with our DevNet Sandbox for CCP, reserve your instance and test-drive it for yourself.

The Cisco & Google Cloud Challenge is an awesome way to brainstorm and solve some real customer problems and even win some prizes while you are at it. So, consider this blog as me inviting everyone to give the Challenge a try, and wishing you the very best! You have until Nov 1, 2018 to enter the challenge and win prizes.

Continue reading

Enabling Enterprise-Grade Hybrid Cloud Data Processing with SAP and Cisco – Part 2

In part 1 of this blog series I talked about how data processing landscapes are getting more complex and heterogeneous creating roadblocks for customer who want to adopt truly hybrid cloud data applications. In the beginning of this year, Cisco and SAP decided to join forces and to bring the SAP Data Hub to the Cisco Container Platform. The goal is to provide a real end-to-end solution to help customers tackle the challenges described above and enable them to become a successful intelligent enterprise. We are focusing on providing a turn-key enterprise-scale solution that fosters a seamless interplay of powerful hardware and sophisticated software.

Figure 1 Unified data integration and orchestration for enterprise data landscapes.

SAP brings into the game its novel data orchestration and refinery solution ‘SAP Data Hub’. The solution brings a number of features that allow customers to manage and process data in complex data landscapes involving on-premise systems and across multiple clouds. SAP Data Hub supports connecting the different systems in a landscape to a central hub to gain a first overview of all systems involved in data processing within a company. Above that the Data Hub is able to scan, profile and crawl those sources to retrieve the metadata and characteristics of the data stored in those sources. With that the SAP Data Hub provides a holistic data landscape overview in a central catalog and allows companies to answer the central questions about data positioning and governance.

Furthermore, the SAP Data Hub allows the definition of data pipelines that allow a data processing and landscape orchestration across all connected systems. Data pipelines consist of operators—small independent computation units—that form a joint computation graph. The functionality an operator provides can reach from very simple read operations and transformations (e.g. change the date format from US to EU), over interacting with a connected system, towards invoking a complex machine learning model. The operators are invoking their functionality and applying their transformations, while the data flows through the defined pipeline. This kind of data processing changes the paradigm of static, transactional ETL processes to a more dynamic flow-based data processing model.

With all of this functionality, we kept in mind that for being successful in bridging enterprise data and big data, we need to be open with respect to connecting not only SAP enterprise systems, but common systems used within the Big Data space (compare Figure 2). For this purpose, the SAP Data Hub is focusing on an open connectivity paradigm providing a huge number of connectors to different kinds of cloud and on-premise data management systems fostering the integration between enterprise data and big data.

All of that makes the SAP Data Hub a powerful enterprise application that allows customer to orchestrate and manage their complex system landscape. However, a solution like the Data Hub would be nothing without a powerful and flexible platform. Customers are increasingly turning towards containerized applications and Kubernetes as the orchestrator of choice, to handle the requirements to efficiently process large volumes of data. For this reason, it was a clear decision to move the SAP Data Hub also in this direction. The SAP Data Hub is completely containerized and uses Kubernetes as its platform and foundation.

Figure 2 SAP & Cisco delivering turn-key solutions for complex enterprise data landscapes.

This is where Cisco with its advanced Cisco Container Platform (CCP) on its hyperconverged hardware solution Cisco Hyperflex comes into the game. Providing elastically scalable container clusters as a single turnkey solution covering on-premise and cloud environments with a single infrastructure stack is key for enterprise customer involved in big data analytics. With the Cisco Container Platform on Hyperflex 3.0 Cisco offers a fully integrated and flexible ‘container as a service’ offering with lifecycle management for hardware and software components. It provides a 100% upstream Kubernetes with integrated networking, system management and security. In addition, it utilizes modern technologies such as ‘istio’ and ‘Cloud Connect VPN’ to efficiently bridge on-premise and cloud services from different cloud providers. Accordingly, it accelerates a cloud-native transformation and application delivery in hybrid cloud enterprise environments, clearly embracing the multi-cloud world and helping to solve the multi-cloud challenges. Furthermore, the CCP allows to monitor the entire hardware and Kubernetes platform to allow customers to identify issues and non-beneficial usage patterns pro-actively and troubleshoot container clusters with fast pace.

Accordingly, the CCP is the perfect foundation for deploying the SAP Data Hub in complex, multi-cloud and hybrid cloud customer landscapes. We complemented the solution with Scality Ring an enterprise-ready scale-out file and object storage that fulfills major characteristics for production-ready usage; e.g. guaranteed reliability, availability and durability. This adds a data lake to the on-premise solution allowing price-efficient storage for mass data. In addition, we added network traffic load balancing with the advanced AVI Networks load balancers. They provide intelligent automation and monitoring for improved routing decisions. Both additions greatly benefit the CCP and complete it towards a full big data management and processing foundation.

With the release of the SAP Data Hub on the Cisco Container Platform running on Hyperflex 3.0 and complemented with Scality Ring and AVI Networks load balancers during SAP TechEd Las Vegas, customers will have the option to receive a turn-key, full-stack solution to tackle the challenges of modern enterprise data landscapes. They can start fast, they remain flexible and they receive full-stack support from Cisco’s world class engineering support and SAP’s advanced support services. Accordingly, SAP and Cisco together enable customers to win the race for the best data processing in the digital economy.

Continue reading

Enabling Enterprise-Grade Hybrid Cloud Data Processing with SAP and Cisco – Part 1

The journey towards the intelligent enterprise

When talking about modern data processing in the digital economy, data is often regarded as the new oil. Enterprise companies are already competing in a race for the best mining, extraction and processing technologies to gain better insights into their companies, deals and processes. Winning in this race will ultimately lead to a competitive advantage in the market, since companies with a deep understanding for their businesses will be able to take the most profitable decisions and establish the most beneficial optimizations. For this reason, the way companies are handling data and analytics is changing from pure transactional, ETL-like processing towards adopting modern technologies such as machine learning, intelligent analytics, stream processing on-premise and in the cloud. We regard to this transition as the move towards the ‘intelligent enterprise’.

However, the transition also leads to the fact that data processing landscapes are getting more complex and more heterogeneous. Data is processed in a growing collection of different system, it is distributed over different places; its volume is growing by the day and customers need to orchestrate and integrate cloud technologies with classical on-premise systems. Among all the challenges that come with the journey towards digitalization and the ‘intelligent enterprise’ the following three main challenges emerge as the most pressing points in our customer base:

Figure 1 Enterprise data landscapes are growing increasingly complex.

1. The tale about data governance and the lack of data knowledge, security and visibility 

One of the biggest challenges in complex modern enterprise data landscape is the distribution of the data over a growing number of stores and processing systems. This leads to a missing knowledge about data positioning, data characteristics and governance. “What data is available in which store?”, “What are the major characteristics of my data sets?”, “Who changed the data, in what way and who has the permissions to access it?” are typical questions that are hard to answer even within a single company. However, it is key to find a strategy that allows a holistic data governance and data management across the entire company.

2. The legend about enterprise readiness of big data technologies 

In the world of modern data processing technologies and big data management, we observe an incredible growth in tools and technologies a customer can choose from. While in the first place, choice seems to be an advantage, one quickly recognizes that this leads to a zoo of non-integrated system each exhibiting different characteristics, life cycles and environments. It is left to the customer to manage, organize and orchestrate those systems leading to a very high effort to arrive at an enterprise ready data landscape with a well-organized interplay of all components.

3. The story about easily processing enterprise data and big data together 

The adoption of modern big data technologies mainly comes from the fact that augmenting classical enterprise data such as sales figures and revenue data with big data such as sensor streams, social media data collections or mobile device data, allows to create deeper and advanced analyses. However, in most cases enterprise data and big data are kept in different silos and exhibit totally different characteristics. Enterprise data typically comes from classical transactional systems such as ERP systems or transactional data bases, it is well structured and adheres a standardized schema. On the other hand, big data often arrives in its pure form as data streams or data collections stored in data lakes (e.g. Hadoop, S3, GCS). It is often unstructured, misses clear data types and might not adhere to a clear data schema. Accordingly, creating an end-to-end data pipeline across the enterprise that combines business data with big data comes with a considerable effort.

SAP and Cisco jointly recognize that our mutual customers need innovative new solutions that would help them overcome these hurdles in order to fully leverage the value of their distributed data and turn them into actionable insights.

Continue reading

Curated Code Repos Get Your Integration Project Done Faster, Better

Having a hard time getting started on your next big integration with Cisco products? Found the platform and API docs on DevNet but need help turning this into running code? Check out Code Exchange, one more way DevNet makes it easy for developers to be successful with Cisco products and platforms.

Code Exchange is an online, curated set of code repositories that help you develop applications with/on Cisco platforms and APIs. Inside Code Exchange, you will find hundreds of code repositories – code created and maintained by Cisco engineering teams, ecosystem partners, technology and open source communities, and individual developers. Anyone can use this code to jumpstart their app development with Cisco platforms, products, application programming interfaces (APIs), and software development kits (SDKs).

Curated for quality

There is a large and growing amount of sample code and applications, helpful tools and libraries, and open source projects related to Cisco technologies on GitHub. However, finding up-to-date content best suited for your immediate needs can be difficult. Code Exchange helps you tackle this challenge.

To get things started, our team of DevNet Developer Advocates identified candidate repositories using GitHub crawlers and an algorithm that scores repositories based on a number of criteria. We then reviewed top repos to make sure they are in good shape and of general interest to the DevNet community. While we do not actively maintain all of the code, we provide confirmation that the code is a worthwhile investment of your time before accepting it into Code Exchange.

Simple filters for technology space and programming language may be used independently or in combination with keywords you provide to zero in on the set of repos most relevant to your immediate needs. Want more guidance? Sorting by those most recommended by DevNet Developer Avocates, or the date the repo was last updated, presents you with the best and brightest projects.

Key Features:

1. Curated view of code repositories related to all Cisco platforms

2. Easy discoverability using filters and search features

3. Link to repository on GitHub for direct access to code and contributors

For example, let’s say you are looking for some sample code written in Python for automation of Cisco IOS XR platforms using APIs defined by native and standard YANG models. Simply enter that in the Code Exchange search field and filters and back comes a set of highly relevant resources.

Or perhaps you’re looking for Javascript for an integration with Cisco’s collaboration platforms? Let Code Exchange do the heavy lifting.

Community contributions provide even more options

At present, the majority of the code in Code Exchange comes from GitHub organizations managed by employees at Cisco. These include some obvious ones, such as Cisco DevNet, Cisco, Cisco Systems, and Cisco SE, as well as others that are less obvious at first glance, such as Talos, IOS-XR, and Meraki.

That said, we realize, and very much appreciate, that a huge amount of very useful code for working with Cisco technologies exists throughout the community at large. We encourage and welcome contributions to Code Exchange from the entire DevNet community, including code in your personal GitHub account.

Follow these base requirements to prepare your GitHub repositories related to Cisco technologies:

1. Include a LICENSE in the repository

2. Add a clear README

3. Ensure repository is publicly available

4. Show evidence of repository being maintained

Then fill out the form and DevNet Developer Advocates will take a look!

How to make sure your repo is accepted

What better way to get your application, your company, your name out in the community working with Cisco products and APIs than to have your repo featured in Code Exchange? In addition to meeting the requirements enforced by the submission form, there are several things you can do to help us realize how great your code is and gladly accept it into Code Exchange.

Your README should provide new users with all the information they need to understand what your repo contains, including a getting started section with step by step instructions for how to install, run, and/or use it, and where to turn to get answers or to provide feedback. Your README will show best in Code Exchange if written in Markdown (i.e. README.md). We are in the process of adding support for reStructured Text (i.e. README.rst).

It is also highly recommended to include a CONTRIBUTING.md file that outlines how best to contribute back to the project by reporting issues, fixing bugs, adding new functionality, etc. Is it best to fork the project and send a pull request? Should an issue be opened first? What if I simply want to ask a question? Make it clear and easy for others to not only use your code but also help make it better.

Tips for enhancing the discoverability of your repos

At the time of project submission, you can identify the set of technologies to which your code is related. Identifying all and only those that truly apply is very helpful. Equally important is to add a meaningful description and GitHub topics. The search functionality of Code Exchange relies on these as well as first and second level headings in your README.

Continue reading

Cisco Intersight: AI-Driven IT Operations Strategy

Cisco launched our cloud-based platform for AI-driven IT operations (a.k.a. AI Ops), Cisco Intersight, last September. It already delivers significant benefits, and we intend to take it to the next level with artificial intelligence and machine learning.

A New Era in Operations Management

“Work smarter, not harder” is critical to improving IT efficiency. Organizations are adopting a multicloud strategy, so you need scalable and consistent management across data centers, private clouds, edge, and branch environments. Cisco Intersight delivers this consistent management, automation and policy enforcement across a variety of servers and hyperconverged infrastructure (HCI). It helps you work smarter by delivering proactive support and actionable intelligence through artificial intelligence (AI) and machine learning (ML), so that you can proactively manage complex environments and reduce risk.

Traditional IT operations management tools are deployed on-premise, they are vendor and device focused, difficult to maintain, and have limited ability to scale. We introduced a new era of systems management with Cisco Intersight. It provides the simplicity of software as a service (SaaS) with unlimited scalability. Intersight is enhanced by AI and ML to provide users with actionable intelligence.

Requirements for AI Operations

Gartner introduced the concept of AI Operations (AI Ops) a few years ago. AI Ops should not be confused with AI systems, like the Cisco UCS C480 ML M5 we announced earlier this month. This definition:

AIOps platforms utilize big data, modern machine learning and other advanced analytics technologies to directly and indirectly enhance IT operations (monitoring, automation and service desk) functions with proactive, personal and dynamic insight. 

A recent IDC report also provides more information regarding AI-driven systems management.

Our product management teams looks at the requirements for AI Ops from a practical perspective. We try to offer functionality that will provide actionable insights and automation to simplify and enhance day to day operations. Something that we ask ourselves everyday is, how can Intersight make daily routines easier and faster across operations groups and teams? In order to achieve these objectives, we believe AI Ops needs to provide the following:

◈ Open automation to streamline routine processes

◈ Consistent management services across a wide variety of infrastructure

◈ Ability to proactively recognize problems and assist users to respond quickly

Open and Unified AI Ops

If your going to make it easier to work across teams, you have to provide an open framework for AI Ops. One of our competitors claims to deliver “AI for the data center”. However, the vendor’s platform currently supports only their storage products. Their strategy is vendor focused. When you read about their roadmap, the vendor only plans to support their systems. Customers use systems from a variety of vendors, so their marketing does not align with reality.

An open and unified framework is foundational. That’s why we designed Cisco Intersight to provide an open framework for AI Ops and infrastructure as a service. Intersight supports Cisco UCS and HyperFlex systems today, and we are working with partners to provide support for third party systems.

One of the reasons many IT processes are manual is because tools used by different teams have separate data stores and user interfaces. When you can aggregate data from a wide range of servers, fabric, storage and hyperconverged infrastructure, you have a common repository that is necessary for effective analysis. There are currently hundreds of thousands of devices connected to Intersight. The vast amount of data we are collecting is used with AI and ML algorithms to identify potential problems and provide users with actionable intelligence. We have integrated Intersight with Cisco Technical Assistance Center (TAC) cognitive support, so we have can leverage their AI and ML capabilities as well as best practices.

The Four Benefits of AI Operations

As we continue to enhance Intersight and the Cisco management portfolio, our primary goal is to increase the customer benefits we can deliver through AI Ops. We have defined four categories of benefits:

1. Improved user experience

2. Proactive support and maintenance

3. Predictive operational analytics

4. Self-optimization of resources

Cisco is executing on our strategy to consistently enhance the customer benefits we deliver through AI Ops. We will be posting a series of blogs to explain how we are currently delivering the benefits in each of the four categories and our plans for the future.

Continue reading

Improve Office 365 Connectivity with Cisco SD-WAN

As more applications move to the cloud, the traditional approach of backhauling traffic over expensive WAN circuits to the data center or a centralized Internet gateway via a hub-and-spoke architecture is no longer relevant. Traditional WAN infrastructure was not designed for accessing applications in the cloud. It is expensive and introduces unnecessary latency that degrades the user experience. The scale-up effect of the centralized network egress model coupled with perimeter stacks optimized to handle conventional Internet browsing often pose bottlenecks and capacity ceilings, which can hinder or bring to a stall customer transition to the SaaS cloud.

As enterprises aggressively adopt SaaS applications such as Office 365, the legacy network architecture poses major problems related to complexity and user experience. In many cases, network administrators have minimal visibility into the network performance characteristics between the end user and software-as-a-service (SaaS) applications. ‘One size fits all’ approach focusing on perimeter security without application awareness, which legacy network architectures often have, do not allow enterprises to differentiate and optimize sanctioned and more trusted cloud business applications from recreational Internet use, resulting the former to be subject to expensive and intrusive security scanning further slowing down user experience.

Massive transformations are occurring in enterprise networking as network architects are reevaluating the design of their WANs to support a cloud transition, reduce network costs, increase visibility and manageability of their cloud traffic, while ensuring an excellent user experience. These architects are turning to software-defined WAN (SD-WAN) to take advantage of inexpensive broadband Internet services and to find ways to intelligently route trusted SaaS cloud bound traffic directly from remote branches. Cisco SD-WAN fabric is an industry-leading platform that delivers an elegant and simplified secure, end-to-end hybrid WAN solution that can facilitate policy based, local and direct connectivity from users to your trusted, mission critical SaaS applications, such as Office 365, straight from your branch office. Enterprises can use this fabric to build large-scale SD-WAN networks that have advanced routing, segmentation, and security capabilities with zero-touch bring-up, centralized orchestration, visibility and policy control. The result is a SaaS cloud-ready network that is easy to manage and more cost-efficient to operationalize and that empowers enterprises to deliver on their business objectives.

A fundamental tenet of the Cisco SD-WAN fabric is connecting users at the branch to applications in the cloud in a seamless, secure, and reliable fashion. Cisco delivers this comprehensive capability for SaaS applications with the Cloud onRamp for SaaS solution in alignment with Microsoft’s connectivity principles for Office 365.

With Cloud OnRamp for SaaS, the SD-WAN fabric continuously measures the performance of a designated SaaS application through all permissible paths from a branch and assign a score. This score gives network administrators visibility into application performance that has never before been available. Most importantly, the fabric automatically makes real-time decisions to choose the best-performing path between the end users at a remote branch and the cloud SaaS application. Enterprises have the flexibility to deploy this capability in multiple ways, according to their business needs and security requirements.

In some deployments, enterprises connect remote branches to the SD-WAN fabric using inexpensive broadband Internet circuits, and they want to apply differentiated security policies depending on the type of services users are connecting to.  For example, instead of sending all branch traffic to a secure web gateway (SWG) or cloud access security broker (CASB), an enterprise may wish to enforce their IT security policies in a targeted manner – by routing regular Internet traffic through SWG, while allowing performance optimal direct connectivity for a limited set of sanctioned and trusted SaaS applications, such as Office 365. In such scenarios, Cloud onRamp for SaaS can be set up to dynamically choose the optimal path among multiple ISPs for both applications permitted to go directly and for applications routable per enterprise policy through SWG.

To learn more about Cloud onRamp for Office 365, read our white paper. For more information about Cisco SD-WAN, click here.

If you’re attending Microsoft Ignite in Orlando next week, make sure to visit Cisco at booth #418. I’d love to show you how to improve your Office 365 connectivity and user experience using Cisco SD-WAN.

Continue reading

Updated IOS-XR Programmability Learning Labs and Sandbox Expand Your Options

A few weeks back I shared this blog post New XR Programmability Learning Labs and Sandbox introducing the new IOS-XR Learning Lab and a dedicated sandbox environment for IOS-XR programmability. This IOS-XR Programmability sandbox and learning labs provide an environment where developers and network engineers can explore the programmability options available in this routing platform.

So, great news for all you IOS-XR programmability fans, we are pleased to bring you even more great XR Programmability learning content. Here is the full list of content, broken down by module and learning labs.

Module One: CLI automation: IOS-XR CLI automation.

Show commands, config-apply, config-replace, and more using on-box bash scripts or remote bash commands

Cisco IOS-XR offers a comprehensive portfolio of APIs at every layer of the network stack, allowing users to leverage automated techniques to provision and manage the lifecycle of a network device. In this module, we start with the basics: the Command Line Interface (CLI) has been the interaction point for expect-style scripters (TCL, expect, pexpect etc.) for ages.  But these techniques rely on send/receive buffers, thus are prone to errors and inefficient code. This is where the new onbox ZTP libraries come in handy. Use them for automated device bring-up automate Day1 and Day2 behavior of the device through deterministic APIs and return values in a rich Linux environment on the router.

◈ IOS-XR CLI automation – Bash
◈ IOS-XR CLI automation – Python

Setting up a Telemetry Client/Collector with “Pipeline” is a flexible, multi-function collection service that is written in Go.

Module Two: IOS-XR Streaming Telemetry changes networking monitoring for the better

SNMP is dead. It’s time to move away from slow, polling techniques employed by SNMP for monitoring that are unable to meet the cadence or scale requirements associated with modern networks. Further, Automation is often misunderstood to be a one-way street of imperative (or higher-layer declarative) commands that help bring a network to an intended state. However, a core aspect of automation is the ability to monitor real-time state of a system during and post the automation process to accomplish a feedback loop that helps make your automation framework more robust and accurate across varied circumstances. In this module, we learn how Streaming Telemetry capabilities in IOS-XR are all set to change network monitoring for the better – allowing tools to subscribe to structured data, contractually obliged to the YANG models representing operational state of the IOS-XR internal database (SYSDB) at a cadence and scale that are orders of magnitude higher than SNMP.

◈ IOS-XR Streaming Telemetry: Monitoring done the right way

◈ Creating your first python Telemetry Collector

◈ Creating your first c++ Telemetry Collector

◈ Deploying a Telemetry Collector on-box

On-Box agents and custom protocols that co-exist with standard protocols to influence routing. Facebook’s Open/R protocol that behaves like an IGP but runs as a third-party application on the router.

Module Three: IOS-XR Service-Layer APIs, programming is exposed through the service layer API

Cisco IOS-XR offers a comprehensive portfolio of APIs at every layer of the network stack. For most automation use cases, the manageability layer that provides the CLI, YANG models and Streaming Telemetry capabilities, is adequate. However, over the last few years, we have seen a growing reliance in web-scale and large-scale Service Provider networks on off-box Controllers or on-box agents.  These extract away the state machine of a traditional protocol, or feature and marry their operation to the requirements of a specific set of applications on the network. These agents/controllers require highly performant access to the lowest layer of the network stack called the Service Layer and the model-driven APIs built at this layer are called the Service-Layer APIs. With the ability to interact with RIB, the Label Switch Database (LSD), BFD events, and interface events. And with more capabilities coming in the future, now is the time to take your automation chops to the next level.

◈ Service-Layer APIs: Bring your own Protocol/Controller

◈ Your first python service-layer API client

◈ Your first c++ service-layer API client

◈ Deploying a Service-layer API client on-box

Continue reading