Traditionally, network operations (NetOps) teams used performance monitoring tools to manage the health and performance of corporate networks. However, as network usage has increased and network deployments have become more disaggregated, many people are looking for alternative performance monitoring methods, such as Artificial Intelligence for IT operations or AIOps.
This blog discusses NetOps and AIOps in detail and analyses how NetOps can be a bridge to AIOps, which will revolutionise the world of tech & business.
Artificial intelligence for IT operations refers to applying AI and related technologies such as ML and NLP to traditional IT businesses and activities (AIOps).
AIOps assist IT Ops, DevOps, and SRE teams (Site reliability engineers) in working smarter and faster. This significantly helps by using algorithmic analysis of IT data and observability telemetry to detect and resolve problems with digital services before operations and customers are impacted.
With AIOps, operations teams can tame the immense complexity and amount of data generated by their modern IT environments. This helps significantly to avoid downtime, maintain uptime, and get a continuous service guarantee.
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As a result of putting IT at the heart of digital transformation efforts, Now AIOps enables businesses to deliver an exceptional user experience at the speed they need.
AIOps enables operations teams to manage the complexity and volume of data generated by their modern IT environments, eliminating outages, maintaining availability, and ensuring continuous service assurance. It also helps them resolve problems as soon as possible before business operations and customers are jeopardised. We can see that almost every AIOps implementation is a success story.
AIOps, which places IT at the heart of digital transformation efforts, enables organisations to operate at speed demanded by modern businesses while providing a superior user experience.
AIOps solutions provide greater visibility into IT environments that are becoming increasingly volatile, heterogeneous, distributed and hybrid. They collect data from multiple tools and systems and aggregate it to provide focus and context when problems arise. Some key business benefits include:
DevOps, SRE, and IT Ops teams can detect incidents early with AIOps solutions and resolve them before they affect customers.
IT operations costs can quickly escalate if the system isn’t adequately optimised and automated. Companies that invest in proper IT automation with AI can easily reduce overall costs because there will be less human intervention or manual tasks.
AIOps solutions help DevOps and SRE teams to identify issues to keep cloud adoption and migration projects on track. Less time troubleshooting means more time to innovate. AIOps can act as a bridge during cloud adoption/migration phases, allowing downstream teams to continue with readiness tools. Existing ones without the need for additional configuration.
Pager fatigue and constant firefighting strain employees. This distracts their attention from what drives the business and puts them in prolonged periods of stress. AIOps automates many time-consuming and repetitive tasks, allowing them to focus on what’s important and interesting and increasing employee satisfaction.
NetOps is derived from DevOps – a set of IT software development principles that leverage operational feedback to accelerate application development and deployment. DevOps aims to provide end users with more useful, reliable, and secure digital services.
The NetOps principles take the agile nature of DevOps and apply it to managing, growing, and scaling network management practices. By using this methodology, NetOps tools automatically collect network health data. NetOps employees can analyse the data to make informed decisions about customising network components and achieving the best possible network performance.
NetOps relies heavily on using automation to simplify network management and troubleshooting tasks. This automation speed up the time it takes to identify parts of the network that can be changed to improve performance in critical areas. NetOps administrators manually analyse this collected and organised data to make informed decisions.
The NetOps 2.0 transformation is happening because digital businesses need networks to deliver applications and services faster. The goal is to remove the limitations of legacy networks by making them more responsive and flexible.
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Using network automation, orchestration, and virtualisation, NetOps can help an organisation respond quickly and consistently to new requests and events while minimising manual intervention. According to network expert Andrew Froehlich, president of West Gate Networks, moving to an agile network approach has clear business benefits, from faster service delivery to improved data security. Network teams are challenged to deliver more functionality to distributed users faster, often without increasing network staff. To handle the growing workload, NetOps teams can create a proactive and programmable network that better supports digital transformation while managing hybrid environments with public and private cloud connections. At NetOps, network teams anticipate frequent changes and try to minimise the potential side effects of these changes rather than avoiding them altogether.
The first distinction between NetOps and DevOP is the way they are defined. NetOps refers to network operations, which means that it focuses on network engineers and their businesses. DevOps, or Development Operations, encompasses a broader view of IT operations, often involving application developers and other IT professionals.
NetOps and DevOps differ significantly in that NetOps focuses on automating daily network operations, whereas DevOps focuses on automating every aspect of application delivery. DevOps incorporates operations into the development of applications.
While DevOps uses Puppet, an open-source configuration management tool that enables configuration management centralisation, or Chef, a configuration management tool that automates tests and deploys infrastructure by writing code instead of using a manual method. DevOps uses Chef as an open-source automation tool for managing configurations and deploying applications.
Another difference between NetOps and DevOps is the operational views. NetOps has a smaller scope but can affect any part of it at any time because no one else is touching it. While DevOps can plan releases across multiple applications, their scope is limited because no group owns everything from start to finish.
There is an important difference between the DevOps and NetOps delivery approaches. NetOps is moving towards micro-deployments that require less configuration management than traditional monolithic environments.
NetOps is responsible for configuring and managing network hardware. You manage switches, routers, firewalls, etc. On the other hand, DevOps engineers focus on collaborating with development teams to ensure development environments are deployed quickly.
Just as DevOps creates a continuous software development framework, a NetOps model takes the same approach for rapid application deployments, Froehlich said. He added that the three pillars of NetOps 2.0 – network virtualisation, network automation, and AI-assisted network monitoring tools – help network teams manage modern networks, moving from local networks to virtual networks in public and private clouds.
Some advantages of NetOps are the following:
Network operations teams traditionally use performance monitoring tools to manage corporate network performance. However, as network usage and deployments have increased, many organisations are turning to other performance monitoring methods.
According to West Gate Network’s Froehlich, the advent of AIOps could be the future of network health monitoring. The growth of public and private clouds and edge computing has increased overall network complexity and created bottlenecks in network analysis.
NetOps teams realise that the amount of data collected on network health and performance will increase to such an extent that it will overwhelm teams’ ability to keep up. Relying on AIOps tools that automatically analyse data and provide solutions to network performance problems can provide a valid answer.
The evolution of NetOps makes it increasingly important for network professionals to have basic skills in automation and programming and the soft skills needed to collaborate with other teams and users proactively. NetOps 2.0 requires significant cultural change as corporate network teams learn to embrace change and manage risk rather than avoid it.
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As IT infrastructure becomes more complex, NetOps teams must find ways to keep their networks up and running at peak performance. One option is AIOps.
Traditionally, network operations (NetOps) teams used performance monitoring tools to manage the health and performance of corporate networks. However, network usage has increased, and network deployments have become more disaggregated. Many seek alternative performance monitoring methods, such as AI for IT operations or AIOps.
While NetOps principles accentuate advances in network performance agility, expansion into public and private clouds and edge computing has increased overall complexity. The increasing complexity of the network can cause bottlenecks during the network analysis phase of the NetOps process.
Until recently, the only option IT decision makers had to overcome this bottleneck was to strengthen the NetOps team workforce. With larger numbers, more people could view network analytics data which would translate into actionable activities.
AIOps has now emerged as a second option to rebalance money spent and achieve performance gains. AI can analyse network health data using tools with built-in data analysis capability. It can provide detailed, granular advice on network changes. Using these teams can improve overall network performance or identify other critical applications.
Now more applications, services, and devices are added to the network. Now, NetOps practitioners expect that the amount of health and performance data collected will become so large soon. They also believe that their teams will become overwhelmed – and networks will continue to grow into different edges and clouds. As a result, AIOps tools that can automatically analyse data and provide solutions to network performance issues are highly sought after.
AIOps seamlessly integrates with various tools and processes. It allows leveraging the many datasets created by various apps and infrastructures.
AIOps offers numerous benefits to DevOps teams, developers, and IT operations. AIOps’ self-healing functionality, expert bot integration capabilities, and end-to-end user monitoring have proved useful. This helps save developers time, improving efficiency and reducing response time to requests.
The essence of AIOps is that it fosters an atmosphere of continuous code release. DevOps systems increasingly incorporate AIOps for logging, analysis, and risk assessment. AIOps will go beyond pre-production metrics to incorporate production indicators. Such as user engagement, quality, and business relevance to the DevOps framework. This suggests that by using AIOps platforms to monitor and maintain apps, DevOps teams can reduce development time and costs.
These new technologies and users are pushing traditional performance and service management techniques and tools to their limits. So, the ideal technique for IT teams to address these digital transformation issues is AIOps. Because AIOps has proven to help to increase IT operations’ potential significantly. So, automated, AI-powered analytics will help teams focus on operational excellence and transform the company into a self-sustaining digital business.
Answer: Most definitions of NetOps (the abbreviation for “network operations”) refer to a network operations strategy that places a strong emphasis on maximizing agility, velocity, and automation.
Answer: AIOps combines big data and machine learning to automate IT operational processes, including event correlation, anomaly detection, and causality determination.
Answer: NetOps.ai is an intelligent automation and managed services platform based on cloud-native principles that help network operators achieve business results by reducing time to market and enabling new sources of income with 5G.
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