What is WebSocket?

WebSocket is a web communication protocol standardized as RFC 6455 by the IETF in December 2011, enabling persistent, full-duplex communication over a single TCP connection (docs.spring.io). Unlike HTTP’s half-duplex, request-response model, WebSocket begins with an HTTP handshake that is “upgraded” to a custom framing protocol, allowing both client and server to send messages at any time, bypassing the need for repeated HTTP connections (pages.ably.com). Due to its compatibility with ports 80 and 443 and seamless operation through proxies, it’s well suited for modern web environments and supports secure communications via wss:// (developer.mozilla.org).

One of WebSocket’s core strengths lies in its low latency and minimal overhead. Once the handshake is complete, the connection remains alive, eliminating repetitive TCP handshakes, SSL negotiations, and HTTP headers in subsequent messages. This efficiency makes it ideal for interactive, real-time user experiences.

Major Use Cases of WebSocket

WebSocket powers a wide array of real-time, bidirectional applications, fundamentally transforming digital interactions:

Chat and Messaging Apps

Modern chat applications—such as Slack, WhatsApp Web, and in-game messaging—rely on WebSocket to immediately transmit messages between clients and servers without delay (linkedin.com).

Collaborative Editing Tools

Platforms enabling real-time collaboration, like Figma or Google Docs, use WebSocket to synchronize edits, cursor positions, and selections among multiple users simultaneously (developer.mozilla.org).

Live Dashboards and Analytics

Financial dashboards streaming constant market data, IoT sensor networks, and monitoring systems frequently use WebSocket to push updates instantly, supporting timely decision-making .

Online Gaming and Immersive Experiences

Real-time multiplayer games demand sub-100ms latency to maintain game state consistency, a use case where WebSocket excels .

IoT Device Control and Monitoring

For devices that stream sensor data or require immediate remote control, WebSocket enables efficient bidirectional communication where every message counts.

Notifications and Streaming Updates

Real-time updates—such as stock tickers, sports scores, and social alerts—are delivered seamlessly over WebSocket, eliminating the need for inefficient polling mechanisms .

How WebSocket Works: Architecture & Protocol

The elegance of WebSocket lies in its structured yet flexible architecture:

a. Handshake and Protocol Upgrade

Communication starts with a standard HTTP GET request containing headers like Connection: Upgrade, Upgrade: websocket, and Sec‑WebSocket‑Key. The server responds with a 101 Switching Protocols, validating the request, returning a Sec‑WebSocket‑Accept key, and finalizing the switch to WebSocket (datatracker.ietf.org, pubnub.com).

b. Persistent TCP Connection and Full‑Duplex Communication

Post-handshake, a single TCP connection remains open. Either party can send framed messages independently, enabling true real-time interaction without the overhead of new connection setups (linkedin.com).

c. Frame Structure, Masking & Control Frames

WebSocket messages are structured in frames, each with fields for a FIN bit, opcode (text, binary, ping, pong, close), payload length (7–64 bits), and optional masking. Clients must mask payloads with a 4-byte key, ensuring intermediary proxies cannot misinterpret the data (en.wikipedia.org). Control frames include ping and pong for heartbeating, and close codes like 1000 (normal) and 1006 (abnormal closure) to manage connection health (medium.com).

d. API and Browser Support

The WebSocket API—exposed via new WebSocket(url) in browsers—supports event-based interaction using onopen, onmessage, onerror, and onclose. Advanced stream control is possible with the WebSocketStream API, while emerging standards like WebTransport add features like unordered delivery and datagrams that WebSocket lacks (developer.mozilla.org).

Basic Workflow of WebSocket

1. Connection Initiation:
   Client sends HTTP handshake.
2. Handshake Completion:
   Server responds with 101, switches protocol.
3. Connection Open:
   readyState transitions to OPEN, enabling bidirectional message exchange.
4. Message Exchange:
   Data is sent using ws.send() and received via onmessage, in frames that may carry text, binary, or control signals.
5. Keepalive & Health Checks:
   ping/pong control frames are used to maintain the connection, especially through firewalls or proxies (medium.com).
6. Graceful Closure:
   Either client or server sends a close frame. Upon reply, the connection is terminated cleanly, ensuring no message loss.

Step-by-Step Getting Started Guide

✅ Step 1: Initialize Node.js Project

npm init -y
npm install ws

✅ Step 2: Implement WebSocket Server

server.js

const WebSocket = require('ws');
const wss = new WebSocket.Server({ port: 8080 });

wss.on('connection', ws => {
  console.log('Client connected');

  ws.on('message', msg => {
    console.log('Received:', msg);
    ws.send(`Echo: ${msg}`);
  });

  ws.on('close', () => console.log('Client disconnected'));
});

Run: node server.js

✅ Step 3: Build Web Client (index.html)

<!DOCTYPE html>
<html>
<head><title>WebSocket Demo</title></head>
<body>
  <input id="msg" placeholder="Type message" />
  <button onclick="send()">Send</button>
  <div id="log"></div>

  <script>
    const ws = new WebSocket('ws://localhost:8080');
    ws.onopen = () => log('Connected');
    ws.onmessage = e => log('Server: ' + e.data);
    ws.onclose = () => log('Connection closed');
    function send() {
      const msg = document.getElementById('msg').value;
      ws.send(msg);
      log('You: ' + msg);
    }
    function log(text) {
      const div = document.getElementById('log');
      div.innerHTML += `<p>${text}</p>`;
    }
  </script>
</body>
</html>

Open in a browser and test sending messages in real time.

✅ Step 4: Add Enhancements

• Secure with wss:// and SSL/TLS
• Automate reconnections with exponential backoff
• Monitor and manage heartbeats, buffer sizes, and idle sessions
• Scale using load balancers like NGINX or HAProxy with sticky sessions, horizontal clustering, and Pub/Sub mechanisms such as Redis (linkedin.com).

Best Practices & Production Considerations

WebSocket deployments require thoughtful architecture to ensure efficiency, security, and scalability:

• Security: Validate Origin headers, use TLS encryption, and implement token or cookie authentication to prevent CSWSH attacks (linkedin.com, en.wikipedia.org).
• Scaling: Use sticky sessions, load-balanced deployment, and Pub/Sub for synchronization across instances (medium.com).
• Resource Management: Regularly clean up idle connections, monitor heartbeat frequency, implement backpressure, and avoid buffer saturation (dev.to).
• Data Efficiency: Use the permessage-deflate extension for large payload compression.
• Fallback Mechanisms: Integrate libraries like Socket.IO to support automatic fallback to HTTP polling or SSE where WebSocket isn’t available (en.wikipedia.org, interactiveimmersive.io).

What the Research Found

The study was a collaboration of five scientists from Northwestern University, Lawrence Berkeley National Laboratory, and an independent research firm, funded by the U.S. Department of Energy. Rather than extrapolating from demand curves, the researchers took a bottom-up approach—compiling detailed data on processors, storage hardware, networking equipment, software, and cooling systems across global data centers to estimate actual electricity consumption.

Their conclusion directly challenges frequently cited projections that data center energy use could triple or more over a decade. Lead author Eric Masanet described those projections as “simplistic extrapolations” that focus almost entirely on rising computing demand while ignoring substantial efficiency gains on the supply side.

The Cloud Migration Effect

The single biggest driver of improved efficiency is the shift from on-premises corporate data centers to large-scale cloud infrastructure. In 2010, the researchers estimated that 79% of data center computing was performed in smaller, traditional facilities—largely owned and operated by non-technology companies. By 2018, that picture had reversed: 89% of data center computing took place in larger, utility-style cloud data centers.

This migration matters because hyperscale cloud facilities operated by companies such as Google, Microsoft, Amazon, and Meta (then Facebook) represent the most energy-efficient computing environments on the planet. Their scale gives them both the economic incentive and the engineering resources to squeeze maximum performance from every kilowatt-hour spent.

How Hyperscalers Achieve Efficiency

Large cloud providers invest heavily across the full stack to minimize energy use per unit of computation:

• Custom silicon: Purpose-built chips optimized for specific workloads reduce wasted compute cycles and idle power draw.
• High-density storage: Modern NVMe and tiered storage architectures deliver more IOPS per watt than legacy spinning-disk setups.
• Virtualization and containerization: Virtual machines and containers raise server utilization rates dramatically compared to single-tenant bare-metal deployments.
• Ultrafast networking: Low-latency, high-bandwidth fabrics reduce the time—and therefore the energy—needed to move data between compute nodes.
• Precision cooling: Customized airflow management, hot-aisle/cold-aisle containment, free-air cooling in cooler climates, and proximity to large hydroelectric sources all reduce the energy overhead of keeping hardware within thermal limits.

Google stated that its data centers on average generated seven times more computing power in 2018 than they did five years prior using no additional electricity—a claim attributed to Urs Hölzle, a senior vice president overseeing Google’s data center technology at the time of the study’s publication.

A Brief History of Data Center Energy Forecasting

The study places its findings in historical context. From 2000 to 2005, data center energy use doubled. In 2007, the U.S. Environmental Protection Agency forecast another doubling between 2005 and 2010. In reality, researcher Jonathan Koomey estimated the actual global increase at around 56% for that period—already far below the EPA projection—and the new study with additional data revised that estimate downward further.

Koomey, a former Lawrence Berkeley scientist and independent researcher at the time of the Science publication, co-authored the new work. “We’re hopeful that this research will reset people’s intuitions about data centers and energy use,” he said.

Current Energy Footprint

As of 2018, data centers consumed approximately 1% of global electricity output—equivalent, by the researchers’ estimate, to roughly 17 million U.S. households. That is a meaningful figure, but the key point is that it has remained essentially flat even as the volume of computing has grown dramatically.

Looking Ahead: The Next Transition Phase

The researchers caution that the efficiency gains offsetting demand growth are expected to hold for only roughly three to four more years from the time of publication (i.e., through the early-to-mid 2020s). Beyond that window, the outlook is uncertain. Several emerging pressures could change the trajectory:

• AI and machine learning workloads: Training large models is computationally intensive and power-hungry at a scale not fully captured in 2018 data.
• Edge computing expansion: Distributing compute to thousands of smaller edge nodes may reduce per-task efficiency compared to centralized hyperscale facilities.
• Slowing hardware gains: As Moore’s Law matures, the cadence of transistor-level efficiency improvements is slowing.

To navigate this transition, the Science article recommends increased investment in energy-saving research, better measurement practices, and more transparent information sharing by data center operators worldwide. The authors describe the coming years as “a critical transition phase to ensure a low-carbon and energy-efficient future.”

Key Takeaways for DevOps and Cloud Engineers

• Migrating workloads from private on-premises data centers to public cloud infrastructure is not just a cost and agility decision—it generally reduces per-workload energy consumption.
• Right-sizing instances, improving utilization rates through containerization, and decommissioning idle resources all contribute directly to the efficiency gains the study highlights.
• Sustainability metrics such as Power Usage Effectiveness (PUE) and Carbon Usage Effectiveness (CUE) are increasingly published by major cloud providers and can inform vendor selection.
• The efficiency story may not hold indefinitely. Teams adopting GPU-heavy AI/ML pipelines should actively monitor and optimize energy-intensive training jobs.

Original reporting sourced from The Indian Express, February 2020, based on a study published in the journal Science. The study was led by Eric Masanet (Northwestern University / UC Santa Barbara) and included Jonathan Koomey and collaborators from Lawrence Berkeley National Laboratory.

What Is Managed Cloud Networking?

Cloud networking describes an approach in which all core networking resources — routing, switching, security, and management — are centralized and delivered over the internet rather than maintained on dedicated on-premises hardware. A managed cloud networking platform extends this further: a third-party provider operates, monitors, and optimizes the entire network fabric on behalf of the enterprise, typically under a service-level agreement (SLA).

Common workloads handled across these platforms include network management, virtual desktop infrastructure (VDI), Voice over IP (VoIP), and unified communications. By shifting network operations to the cloud, organizations can reduce the capital expenditure associated with owning and refreshing physical networking equipment while gaining elastic capacity and centralized visibility.

Key Market Drivers

Several converging forces are accelerating enterprise adoption of managed cloud networking platforms:

• Demand for operational simplicity: Distributed branch offices and remote workforces create complex networking environments. Managed cloud platforms reduce that complexity by centralizing policy, configuration, and monitoring.
• Cost pressure: Moving from capital expenditure (CapEx) network infrastructure to an operational expenditure (OpEx) subscription model appeals to finance and IT leadership alike.
• Adoption of IoT and AI workloads: As organizations deploy Internet of Things sensors and machine-learning pipelines, they require network infrastructure that can scale dynamically and handle unpredictable traffic patterns — a natural fit for cloud-native networking.
• Mobile and application hosting growth: The proliferation of mobile workforce tools and SaaS applications puts pressure on traditional WAN architectures, pushing organizations toward SD-WAN and cloud-managed alternatives.
• Hybrid cloud strategies: Enterprises operating across both private and public clouds need consistent networking policies, driving demand for platforms that can manage connectivity across heterogeneous environments.

Key Market Restraints

Despite strong tailwinds, managed cloud networking adoption faces meaningful headwinds:

• Security and privacy concerns: Routing all organizational traffic through a third-party managed platform raises questions about data sovereignty, confidentiality, and regulatory compliance — particularly in healthcare, financial services, and government sectors.
• Vendor lock-in risk: Migrating network configurations and policies from one managed platform to another is non-trivial, making enterprises cautious about long-term commitments.
• Integration complexity: Organizations with legacy on-premises networking equipment may face significant integration effort before they can fully leverage a managed cloud platform.

Market Segmentation

The managed cloud networking platforms market can be analyzed across several dimensions:

By Service Type

• Business services
• Security services
• Mobility services
• Network services
• Data center services

By Cloud Deployment Model

• Public cloud: Networking infrastructure shared across multiple tenants and operated by a hyperscale or specialist provider.
• Private cloud: Dedicated networking resources, either on-premises or hosted, offering greater control and isolation for regulated industries.

By Enterprise Size

• Small and medium-sized enterprises (SMEs)
• Large enterprises

By End-User Industry

• Telecom and IT-enabled services (ITES)
• Banking, financial services, and insurance (BFSI)
• Healthcare
• Government and education
• Retail and consumer goods
• Manufacturing and automotive
• Media and entertainment

Regional Outlook

North America

North America has historically held a leading position in this market, driven by the concentration of major technology vendors, a mature enterprise IT landscape, and early adoption of cloud-first networking strategies. The region’s strong regulatory environment around data management has also pushed vendors to develop robust compliance tooling within their managed platforms.

Asia Pacific

Asia Pacific represents a high-growth opportunity, particularly among small and medium-sized enterprises that lack the internal IT resources to manage complex network infrastructure. Significant cloud infrastructure investment by global hyperscalers in the region has expanded the available service footprint, making managed cloud networking more accessible to regional businesses.

Europe, Middle East & Africa, and Latin America

European adoption is shaped by strict data protection regulations (including GDPR), which influence both architecture choices and vendor selection. Middle East & Africa and Latin America markets are at earlier stages of adoption but are expected to grow as cloud infrastructure matures in those regions.

Notable Vendors in the Space

The managed cloud networking platforms landscape includes a mix of networking specialists, system integrators, and hyperscale cloud providers. Vendors frequently cited in this market segment (as of the original 2019 analysis) include:

• Cisco Systems
• Huawei Technologies
• IBM (International Business Machines)
• Ericsson
• Fujitsu
• NEC Corporation
• NTT Data Corporation
• Accenture
• Alcatel-Lucent (now Nokia)
• Aerohive Networks (acquired by Extreme Networks in 2019)
• Interoute Communications (acquired by GTT Communications in 2018)
• NaviSite

The competitive landscape has continued to evolve since 2019, with major public cloud providers (AWS, Microsoft Azure, Google Cloud) expanding their managed networking service portfolios and SD-WAN vendors entering the managed services space.

Platform Capabilities to Evaluate

When assessing a managed cloud networking platform, engineering and infrastructure teams should evaluate the following capability areas:

• Elastic compute and hosting: Can the platform scale network resources automatically in response to traffic demand?
• Security services: Does the platform include firewall-as-a-service, intrusion detection, and zero-trust network access (ZTNA)?
• Relational database and storage integration: How tightly does the networking layer integrate with managed database (SQL) and object storage services?
• Observability and analytics: What network telemetry, logging, and AI-driven anomaly detection does the platform provide?
• Multi-cloud and hybrid support: Can the platform manage connectivity across multiple public clouds and on-premises environments from a single control plane?
• Compliance and data residency: Does the platform support regional data residency requirements relevant to your industry?

Summary

Managed cloud networking platforms sit at the intersection of several major enterprise IT trends: cloud migration, distributed workforce enablement, IoT expansion, and operational cost reduction. While security concerns and integration complexity remain real barriers, the market continues to mature as vendors build out more comprehensive, compliance-aware offerings. For DevOps and platform engineering teams, understanding this landscape is increasingly relevant as network configuration becomes code and infrastructure-as-a-service models blur the boundary between application delivery and network management.

Companies are turning to open source network automation tools as less expensive, good-enough alternatives to Cisco ACI and VMware NSX.

Companies trying to avoid or lessen the use of expensive network automation software from Cisco and VMware are turning to open source tools that are often good enough for many tasks associated with managing complex modern networks.

Cisco’s application-centric infrastructure (ACI) and VMware’s NSX are powerful technologies for operating networks built on the vendors’ respective products. But many large enterprises have data centers filled with perfectly good multivendor hardware and software that very few organizations are willing to swap for an all Cisco or VMware alternative.

Therefore, companies are turning to open source networking products, such as Ansible, Chef, Puppet and SaltStack, for automating many network-related chores across as much of the data center as possible, while relegating ACI and NSX to Cisco- or VMware-only portions of the network.

“I’m seeing a lot more appetite — quite honestly — for more of a roll-your-own solution,” said Teren Bryson, a network consultant for Fortune 500 companies. “Neither one of those solutions [ACI, NSX] is by any means necessary, and a lot of companies are actually finding them fairly onerous, expensive, complicated and not able to do some of the things they can do using other tools.”

In July, Gartner reported in its Data Center Networking Magic Quadrant that companies have difficulty justifying the cost of NSX. “We believe this is a primary reason that the adoption of NSX data center remains less than 10% of the VMware ESX installed base,” the research firm said. ESX is VMware’s platform for server virtualization.

Gartner found similar resistance to Cisco ACI.

“Based on client feedback, Cisco ACI is complex for a combination of financial, technical and cultural reasons,” the report said. “We believe this has limited ACI’s usage and adoption.”

Using open source network automation

Companies striving for vendor independence are building network orchestration systems comprised of Ansible, Jenkins and GitLab, which is a repository for code version control, Bryson said. In place of Ansible, companies might use Chef, SaltStack or Puppet.

The list of network tasks engineers can automate using the open source tools is long. “It’s a big tapestry of automation that people are looking at,” said Shamus McGillicuddy, an analyst at Enterprise Management Associates (EMA), based in Boulder, Colo.

Engineers who once used open source networking tools primarily to configure network devices find they can do much more. Other routine tasks the software can handle include ensuring a switch booted up for the first time has the correct version of the operating system and the latest security patches. Automated chores could also include verifying the network is operating as intended after engineers use a tool like Ansible to make configuration changes to switches, load balancers and firewalls.

Engineers write reusable scripts — typically in Python — that execute the automation. Programmers store the scripts in a repository, such as GitLab, while using other data storage software, such as NetBox, for critical network data. Examples include the locations, and IP or network addresses of all hardware, and the default settings of devices and the versions of programs installed on them.

An EMA survey of 250 IT pros — scheduled for release in a few weeks — found that they had automated roughly a third of the tasks performed on network devices, McGillicuddy said. More than 90% of the survey respondents used more than one network automation tool, and almost 30% of those engineers had homegrown software often built with open source components.

“Almost nobody is hiring network engineers anymore that don’t have some ability to program at least at a rudimentary level,” Bryson said. “It’s a big change from when I first got into the industry. It’s a big change even from five or six years ago. It’s definitely where the industry is moving.”

Advanced features in ACI, NSX

Configuration management tools are not comparable to ACI or NSX in functionality. Both can do much more when managing networks built with Cisco and VMware, respectively.

NSX is a hardware-agnostic software layer that delivers switching, routing and distributed firewall features to applications running on VMware’s virtualization platform. Cisco has designed ACI as a software alternative to the manual task of managing the company’s switches through their command-line interfaces.

Nevertheless, companies are finding that the best strategy is to tap the technologies only when needed.

“The cost of ICT [information and communications technology] needs to come down,” said Chris Antlitz, an analyst at Technology Business Research Inc. “There [are] CIOs out there saying, ‘we can’t keep spending this kind of money. It’s just not sustainable.'”

What Is the DevOps Master Class Lifetime Bundle?

The DevOps Master Class Lifetime Bundle is a curated collection of ten online courses spanning approximately 86 hours of content. It targets engineers who want to build practical, job-ready skills across the core tools and platforms that define modern DevOps workflows — including infrastructure automation, containerisation, cloud architecture, big data, and scripting.

The bundle was promoted at a heavily discounted price through the Neowin Deals store. Whether you encountered this deal in 2019 or are researching it now, the course list itself serves as a useful reference for structuring a self-directed DevOps learning path.

Courses Included in the Bundle

The bundle contains the following ten courses:

• Ansible Automation For Beginners to Advanced — Covers Ansible fundamentals through to advanced use cases including AWS automation, Ansible Tower, and network automation.
• Introduction to Kubernetes Using Docker — Focuses on container orchestration concepts and practical deployment patterns using Kubernetes alongside Docker.
• Fundamentals of Unix & Linux System Administration — A grounding in Linux administration skills that underpin almost every server-side DevOps role.
• Become an AWS Certified Solutions Architect – Associate — Prepares learners for the AWS Solutions Architect Associate certification, one of the most recognised cloud credentials in the industry.
• Projects in Hadoop and Big Data: Learn by Building Apps — A project-based approach to learning Hadoop and related big data technologies.
• AWS Certified Solutions Architect Professional Exam Guide — An advanced AWS course aimed at engineers seeking the professional-level Solutions Architect credential.
• Docker for Professionals: The Practical Guide — A hands-on Docker course covering containerisation from basics to production-grade usage.
• The Python Mega Course: Build 10 Real World Applications — Python scripting and application development, directly applicable to automation and tooling work in DevOps pipelines.
• DevOps Tutorial: Complete Beginner Training — An overview of DevOps culture, practices, and toolchains for those new to the field.
• Elasticsearch & the Elastic Stack — Covers search and observability using Elasticsearch, Logstash, Beats, and Kibana (the ELK/Elastic Stack).

Why This Course Combination Makes Sense for DevOps Engineers

The bundle reflects a realistic cross-section of the skills hiring managers look for in mid-level DevOps and Site Reliability Engineering (SRE) roles:

• Infrastructure as Code: Ansible remains one of the most widely adopted configuration management and automation tools in enterprise environments.
• Containers and Orchestration: Docker and Kubernetes together cover the container lifecycle from image build through to scaled cluster deployment — a combination that is now effectively a baseline expectation in DevOps job descriptions.
• Cloud Platforms: Both the Associate and Professional AWS Solutions Architect certifications are among the highest-value cloud credentials available. Having both tracks in a single bundle creates a logical progression path.
• Observability: The Elastic Stack course addresses log aggregation, search, and visualisation — skills that feed directly into monitoring and incident response workflows.
• Scripting: Python is the dominant scripting language in DevOps automation, infrastructure tooling, and cloud SDK usage.
• Foundations: Linux system administration and a general DevOps overview course ensure the bundle is accessible to career-changers and those transitioning from other IT disciplines.

Bundle Access Details

At the time of the original promotion, the bundle included the following terms:

• Access duration: Lifetime access to all courses.
• Certification: Certificates of completion included for each course.
• Redemption window: Course codes needed to be redeemed within 30 days of purchase.
• Advertised price: $39 (reduced from a stated list price of $990, representing a 96% discount).

Building Your Own DevOps Learning Path

If this specific bundle is no longer available at the discounted price, the course list above still maps to a coherent self-directed learning path. The logical sequence for most learners would be:

1. Linux fundamentals — everything else builds on this.
2. DevOps overview — understand the culture and workflow before diving into tooling.
3. Python scripting — enables automation work across every other discipline.
4. Docker — learn containers before the orchestration layer.
5. Kubernetes — once Docker is solid, move to orchestration.
6. Ansible — infrastructure automation to complement container workflows.
7. AWS Associate — cloud skills are now inseparable from DevOps practice.
8. AWS Professional — deepen cloud architecture knowledge.
9. Elastic Stack — add observability and log management skills.
10. Hadoop/Big Data — useful for data-adjacent platform engineering roles.

Equivalent courses for each of these topics are available on platforms including Udemy, Pluralsight, Linux Foundation, and A Cloud Guru. AWS certification preparation is also supported through AWS’s own training portal.

Original deal source: Neowin.net Deals store, published July 2019.

Why the Human Side of DevOps Matters

DevOps conversations are often dominated by toolchains, automation pipelines, and infrastructure-as-code. But the DevOps Institute argues that technology alone is not the limiting factor in most DevOps transformations — people are. To address this, the Institute introduced the SKIL Framework, a structured model designed to guide individuals and organizations through the process of upskilling for DevOps.

“We really feel very strongly that while automation is obviously a critical success factor for DevOps, it really is the human factor that’s going to make a difference,” said Jayne Groll, CEO of the DevOps Institute, in an interview with SD Times.

What Is the SKIL Framework?

SKIL is an acronym standing for Skill, Knowledge, Ideas, and Learning. It is a holistic model created by the DevOps Institute to give practitioners and organizations a coherent path to developing DevOps competencies — without prescribing allegiance to any single methodology such as ITIL or Agile.

“We recognize that we needed to create a framework that helps people understand how to transform without being specific to things like ITIL or Agile,” Groll said. “DevOps is really about continuous learning and mastery.”

The Skills Gap Driving the Framework

According to the Upskilling: Enterprise DevOps Skills Report 2019 published by the DevOps Institute, organizations identified the top three skills they needed as:

1. Automation skills — scripting, CI/CD tooling, infrastructure automation
2. Process skills — workflow design, release management, value stream mapping
3. Soft skills — collaboration, communication, empathy, and adaptability

The inclusion of soft skills in the top three was described as unexpected by the report’s authors, but it reinforces the DevOps Institute’s position that technical proficiency alone is insufficient. Groll noted that a shortage of these critical skills is behind many of the challenges organizations face when attempting to adopt or scale DevOps practices.

The Four Elements of SKIL

Each element of the framework targets a different dimension of professional development:

1. Skill

Focuses on measurable, demonstrable career growth. This includes certifications, skill assessments, and defined learning paths that individuals can point to on a CV or résumé. The goal is to give practitioners tangible credentials that validate their expertise to employers and peers.

2. Knowledge

Covers rich, substantive content that supplements day-to-day practice. This includes research reports, case studies, whitepapers, and books. Knowledge resources are meant to provide deeper context and insight beyond what a certification or course syllabus can cover.

3. Ideas

Emphasizes peer-to-peer exchange and community engagement. This element encompasses Skillup events, Slack community groups, networking opportunities, and forums where practitioners can share experiences, debate approaches, and learn from one another’s successes and failures.

4. Learning

Supports continuous, lifelong learning rather than point-in-time training. This includes listening to thought leaders, following emerging research, and pursuing structured learning paths that lead toward genuine mastery rather than checkbox compliance.

Context for Individuals and Organizations

One of the practical problems the SKIL Framework aims to solve is the lack of direction that many practitioners face when they are told to “upskill” without being given guidance on how. There is no shortage of available resources — bootcamps, MOOCs, university programs, conference talks, and freely available video content all exist — but without a structured framework, it can be difficult to know where to start or how to sequence learning effectively.

Groll stated that SKIL is intended to provide that context both for individual contributors trying to advance their careers and for engineering managers or HR leaders trying to build upskilling programs for their teams.

Where SKIL Fits in the Broader DevOps Landscape

Unlike prescriptive frameworks that mandate specific tools or processes, SKIL is deliberately methodology-agnostic. It does not compete with Agile, SAFe, ITIL 4, or any other framework — instead, it acts as a meta-layer that helps practitioners navigate whichever methodologies are relevant to their context.

For DevOps engineers and platform teams, the practical takeaway is straightforward: technical skills matter, but they are not sufficient on their own. Building knowledge, staying connected to the broader community, and committing to continuous learning are equally important factors in a sustainable DevOps career or a successful organizational transformation.

More information about the DevOps Institute and its programs is available at devopsinstitute.com.

Why Kubernetes Networking Is Hard

The fundamental challenge is that containers are ephemeral and can be scheduled on any node in a cluster. Maintaining direct, routable connectivity between all pods — regardless of which physical or virtual host they land on — requires a networking layer that goes well beyond what a default Linux network stack provides out of the box. Add multi-tenancy, network policy enforcement, and the need for observability, and the problem becomes genuinely complex.

How CNI Plugins Solve the Problem

Kubernetes delegates the networking problem to the Container Network Interface (CNI) — an open standard for plugin-based networking in Linux containers. CNI plugins are responsible for:

• Allocating unique IP addresses to every pod.
• Creating and managing network interfaces inside container namespaces.
• Setting up routing rules so pods on different hosts can reach each other.
• Optionally enforcing network policies that control traffic flow.

CNI is not Kubernetes-specific. The same plugins also work with OpenShift, Amazon ECS, and other container runtimes. Most plugins achieve cross-host connectivity by building an overlay network on top of the existing physical or cloud network infrastructure. This overlay assigns each container a unique IP from a shared address space and transparently routes traffic between containers without requiring changes to the underlying network.

The overlay approach offers two practical benefits: cluster administrators gain a single, consistent network layer that is far easier to monitor and secure, and individual hosts no longer need hand-crafted iptables rules for every container-to-container communication path.

Tool 1: Flannel — Simple Overlay Networking

Flannel, originally developed by CoreOS, is one of the oldest and most widely adopted CNI plugins. Its design philosophy is simplicity: it provides a straightforward overlay network that satisfies Kubernetes networking requirements without exposing operators to unnecessary complexity.

How Flannel Works

• Flannel assigns a subnet to each node. Every pod on that node receives an IP from that subnet, giving every pod a unique, cluster-wide address.
• Subnet-to-host mappings are stored in etcd, the same key-value store Kubernetes uses for cluster state.
• A daemon called flanneld runs on every node. It watches etcd for subnet assignments and programs route rules in the Linux kernel’s routing table accordingly.
• For multi-host traffic, the recommended default backend is VXLAN. The flanneld daemon collects packets destined for remote hosts, wraps them in UDP/VXLAN encapsulation (an L3 wrapper), and sends them across the physical network. The receiving node unwraps the packet and delivers it to the correct pod.
• A software bridge (e.g., the Linux bridge) handles local switching; the VXLAN device handles forwarding at Layer 2 for off-host traffic.

When to Use Flannel

Flannel is an excellent choice when you need basic pod-to-pod connectivity and want minimal operational overhead. It does not natively support Kubernetes NetworkPolicy, so if fine-grained traffic control is a requirement, you will need to pair it with a separate policy engine or choose a different plugin.

Tool 2: Calico — High-Performance L3 Networking with Policy

Project Calico takes a fundamentally different architectural approach. Rather than building an overlay, Calico configures a pure Layer 3 network using BGP (Border Gateway Protocol) to exchange routing information directly between nodes. Each node acts as a BGP router, advertising the pod CIDRs it owns to the rest of the cluster.

How Calico Works

• Packets travel between pods without any encapsulation wrapper, which reduces overhead and makes packet captures with standard tools (tcpdump, Wireshark) straightforward.
• BGP route distribution is handled by BIRD, a widely used open-source BGP daemon.
• In environments where BGP is not supported by the underlying network fabric, Calico can fall back to VXLAN or IP-in-IP encapsulation modes.
• Calico enforces network policy by programming iptables (or eBPF) rules on each node, giving fine-grained control over ingress and egress traffic.

Advanced Policy Capabilities

The standard Kubernetes NetworkPolicy API covers ingress rules based on pod labels and ports, but has historically lacked support for egress policies and CIDR-based rules. Calico extends this with its own GlobalNetworkPolicy and NetworkPolicy custom resources, which add:

• Egress policy enforcement to control outgoing connections from pods.
• CIDR-based rules for integrating with external networks.
• Namespace-scoped policies that prevent cross-namespace communication by default.
• Integration with Istio service mesh, allowing Calico to enforce policy at both the network infrastructure layer and the service mesh layer simultaneously.

When to Use Calico

Calico is a strong choice when performance and advanced network policy are priorities. The L3 routing approach scales well to large clusters. Commercial support is available through Tigera, the company that maintains the project.

Tool 3: Weave Net — Ease of Use with Strong Performance

Weave Net, from Weaveworks, occupies the middle ground: it aims to combine operational simplicity with solid performance and built-in security features.

How Weave Net Works

• Weave Net creates a mesh overlay network that connects every node in the cluster to every other node.
• Like Flannel, it uses VXLAN encapsulation for cross-host traffic, but it performs the encapsulation directly in the kernel via a fast datapath. This avoids the overhead of packets traveling through userspace, which provides a meaningful performance advantage over purely userspace implementations.
• When the underlying network topology does not support the fast datapath, Weave automatically falls back to a userspace mode called sleeve mode, ensuring connectivity is maintained even if performance is reduced.
• A built-in DNS component called weaveDNS provides automatic service discovery, name resolution, load balancing, and fault tolerance within the cluster without additional configuration.

Security Features

Weave Net ships with traffic encryption enabled between hosts. It uses NaCl encryption for sleeve mode traffic and IPsec ESP for fast datapath traffic. Network policies are automatically configured at installation time; operators only need to define their rules rather than setting up the policy infrastructure from scratch.

When to Use Weave Net

Weave Net is well-suited to teams that want good performance and security without the operational complexity of managing a BGP-based network like Calico. Its automatic configuration and built-in service discovery make it accessible to operators who are newer to Kubernetes networking.

Comparison Summary

Choosing the Right CNI Plugin

There is no universally correct answer. The right plugin depends on your team’s operational maturity, the size and complexity of your cluster, and your network policy requirements:

• Start with Flannel if you need something working quickly and your cluster does not require network policy enforcement.
• Choose Calico if performance, scalability, and fine-grained egress/ingress policy are priorities, and your team is comfortable with BGP concepts or willing to learn them.
• Choose Weave Net if you want a balance of performance, built-in encryption, and service discovery without significant configuration effort.

It is also worth noting that this list is not exhaustive. Other CNI plugins — such as Cilium (eBPF-based), Canal (Flannel + Calico policy), and Multus (multi-network support) — address specialized use cases and are worth evaluating as your requirements evolve. Modern Kubernetes deployments often combine multiple tools rather than relying on a single solution for all networking needs.

Original source referenced: techgenix.com (article from June 2019). Some product capabilities described here may have evolved since the original publication date. Always consult current official documentation before making architectural decisions.

The Convergence of DevOps and Networking

For years, networking was treated as a discipline apart from software development and systems operations. Network changes were manual, infrequent, and governed by change-management processes that stood in sharp contrast to the rapid iteration cycles championed by DevOps teams. That gap is closing. Practices from the DevOps world — automation, version control, continuous delivery, and observability — are being systematically adopted into the management of networking services, driven by better tooling, more programmable hardware, and the rise of software-defined networking.

Key DevOps Practices Being Adopted in Networking

Several DevOps disciplines translate directly into networking contexts:

• Networks-as-Code — Declaring desired network topology and behavior in version-controlled configuration files, then applying that state programmatically to physical or virtual devices.
• CI/CD Pipeline Integration — Treating network configuration changes as deployable artifacts, validated and promoted through automated pipelines before reaching production.
• Container-scale Network Functions — Decomposing network services into smaller, independently deployable units that can run in containers rather than on dedicated appliances.
• Chaos Engineering for Networks — Deliberately injecting network faults to validate resilience and expose hidden dependencies before they cause unplanned outages.
• Unified Monitoring and Observability — Extending the same monitoring stacks used for applications and infrastructure to cover network-specific metrics and events.

Networks-as-Code: Declaring Intent, Not Commands

The infrastructure-as-code (IaC) pattern — where the desired state of infrastructure is expressed in declarative files and enforced by automation tools — maps naturally to networking. With networks-as-code, engineers describe how a network should look and behave: which VLANs exist, how routing tables should be configured, what firewall rules apply. An automation tool then reconciles the live network against that declared state.

Historically, network device configuration has been standardized through protocols such as NETCONF (Network Configuration Protocol, RFC 6241), but adoption in automation toolchains was slow. That changed as mainstream DevOps tools added native networking support:

• Ansible introduced dedicated modules for networking device automation, covering hardware from multiple vendors including Cisco, Juniper, Arista, and others. These modules allow network configuration tasks to be expressed as Ansible playbooks alongside server and cloud automation.
• SaltStack provides similar networking modules, enabling event-driven and scheduled configuration management of network devices.
• NAPALM (Network Automation and Programmability Abstraction Layer with Multivendor support) is an open-source library that provides a vendor-neutral Python API for interacting with network devices. It integrates with both Ansible and Salt, allowing engineers to write automation that works across different vendors’ hardware without rewriting logic per platform.

Virtual Network Functions and Containers as Experimentation Platforms

One barrier to adopting automation in networking has been the risk of experimenting directly on production hardware. Virtual Network Functions (VNFs) — network capabilities such as firewalls, load balancers, or routers running as software on virtual machines rather than on dedicated physical appliances — lower that barrier considerably. Teams can test automation workflows against virtual instances before applying them to production hardware.

VNF adoption has been particularly pronounced in the telecommunications industry, where operators are under pressure to provision and reconfigure network services rapidly. Beyond virtual machines, some network functions are being containerized, enabling faster startup times and denser packing on commodity hardware. Not all network functions are well-suited to containerization — those with high throughput requirements or tight hardware integration may still require VMs or dedicated appliances — but the trend toward containerized network functions is growing.

Software-Defined Networking as an Automation Enabler

Software-Defined Networking (SDN) separates the control plane (decisions about where traffic goes) from the data plane (the actual forwarding of packets). By exposing the control plane through APIs and programmable interfaces, SDN makes network devices far more amenable to scripted and automated configuration. As more networking services are delivered through SDN architectures, the surface area available for DevOps-style automation expands correspondingly.

Chaos Engineering Applied to Network Reliability

Chaos engineering — the practice of deliberately introducing failures into a system to uncover weaknesses — is well established for application and infrastructure layers. Its application to networking is less mature. Network chaos experiments might include simulating link failures, injecting packet loss or latency, or partitioning network segments to observe how applications and failover mechanisms respond.

At the time of the original reporting, chaos engineering in the networking domain was still in early stages. The principles are sound: if you do not proactively test how your network behaves under failure conditions, you will discover those behaviors during an incident instead.

Monitoring and Observability for Networks

Networks are increasingly monitored using the same tooling that observes the rest of the stack — time-series databases, dashboards, alerting systems, and distributed tracing platforms. Some of these tools include network-specific extensions or integrations, such as SNMP polling, flow data collection (NetFlow, sFlow, IPFIX), and BGP/routing state monitoring. Unifying network observability with application and infrastructure observability gives teams a single plane of glass for diagnosing cross-layer issues, rather than requiring separate NOC and operations workflows.

The Human Factor

Automation does not eliminate the human element in network reliability — it changes it. Studies on network reliability have identified human error as a leading cause of outages, which motivates investment in automation to reduce manual, error-prone steps. At the same time, effective automation requires skilled engineers who understand both the networking domain and the software engineering practices needed to build and maintain reliable automation pipelines. Bridging this skill gap is one of the central challenges in the NetDevOps or Network Reliability Engineering space.

Summary

The application of DevOps principles to networking is not a distant aspiration — it is actively happening, driven by programmable hardware, open-source tooling like Ansible, SaltStack, and NAPALM, and architectural shifts toward SDN and VNFs. Teams looking to move in this direction can start by treating network configuration as code, integrating network change validation into CI/CD pipelines, and extending existing monitoring practices to cover the network layer.

Why Networking Needs DevOps

DevOps culture has transformed software development and operations — but networking has traditionally lagged behind. Manual switch configuration, siloed network teams, and change-averse processes are still common in many organizations. The question Armstrong addresses in his book is whether incorporating networking into a DevOps culture can genuinely boost organizational agility and growth. His answer is a clear yes — and the tooling is rapidly catching up to make it practical.

The Future of DevOps in Networking

Intent-Based Networking

According to Armstrong, the most significant shift on the horizon is intent-based networking (IBN). Rather than configuring each network device individually, intent-based systems allow network architects to define a high-level blueprint of what they want the network to do — and then automatically push that intent to all target devices.

Armstrong cites Apstra as an early example of this model. With an intent-based approach, a network architect can express a desired topology — for instance, a leaf-spine data center architecture — and the system handles the per-device configuration automatically. If the design changes later, the network converges to the new intent without engineers touching each switch manually.

This model has clear parallels with infrastructure-as-code in the software world: you declare desired state, and the platform handles reconciliation. For private cloud and data center environments, IBN promises to abstract away the day-to-day management burden of individual network devices.

Container Networking

Container networking has become a first-class concern as Kubernetes adoption has grown. Armstrong highlights open source projects such as Calico, which provides container networking for both private and public cloud environments alongside Kubernetes. Calico favors network-native principles — using standard IP routing rather than complex overlay networks — making it more approachable for network engineers familiar with traditional routing and switching concepts.

The broader trend is a move away from opaque overlay abstractions toward container network architectures that network teams can reason about using existing skills.

Application-Centric Load Balancing

On the load balancing side, Armstrong points to application-centric software load balancers — such as Avi Networks (now VMware NSX Advanced Load Balancer) — that bring built-in analytics and the ability to scale based on real-time throughput. These platforms expose developer-friendly APIs, making them well-suited for integration into automated deployment pipelines.

The unifying theme across all three areas — intent-based networking, container networking, and modern load balancers — is that networks are being built for automation from the ground up, with APIs that DevOps toolchains can consume directly.

Key Tools and Languages for NetDevOps Engineers

Bridging the gap between traditional networking and DevOps requires network engineers to develop software skills — and software engineers to understand networking fundamentals. Armstrong’s recommended skill set covers four broad areas:

1. Programming and Templating

• Python — The dominant scripting language in network automation. Used for everything from parsing device output to calling network APIs.
• Jinja2 templates — Widely used to render device configurations dynamically from structured data.
• YAML — The standard data format for defining variables, inventories, and playbooks in most network automation frameworks.

2. Configuration Management

• Ansible — The most widely adopted configuration management tool for network automation. Its agentless architecture suits network devices well, and it has extensive modules for vendors such as Cisco, Juniper, and Arista.

3. Version Control

• Git — Treating network configurations as code means storing them in version control. Git is the industry standard, enabling change tracking, peer review, and rollback for network changes — just as it does for application code.

4. CI/CD Pipelines

• Jenkins — A mature, self-hosted CI/CD server with broad plugin support.
• Travis CI — A cloud-based CI service, commonly used for open source projects.
• Concourse CI — A pipeline-centric CI/CD tool favored in environments that want a declarative, container-native approach to build and deployment workflows.

Together, these tools allow network changes to follow the same review, test, and deploy lifecycle as application code — reducing the risk of manual configuration errors and enabling faster, more auditable change management.

Takeaways for DevOps and Network Engineers

• Networks are becoming programmable. Vendor APIs and intent-based platforms mean that manual CLI configuration is no longer the only — or best — option for managing network infrastructure at scale.
• The skills gap is real, but closeable. Network engineers who invest in Python, Ansible, and Git will be significantly better positioned as automation becomes standard practice.
• DevOps principles translate directly. Infrastructure-as-code, version control, CI/CD, and peer review are as applicable to network configuration as they are to application deployment.
• Container networking is not optional. As Kubernetes becomes the default runtime for many organizations, understanding how container networking works — and how to operate it — is increasingly a core networking skill.

For engineers looking to go deeper, Armstrong’s book DevOps for Networking (Packt Publishing) provides a structured introduction to applying DevOps workflows specifically to network engineering challenges.