[Datanet] To efficiently monitor a network, it is necessary to be able to respond quickly and automatically to problems that occur on any network, regardless of the complexity of the architecture or the speed at which the network configuration changes. As a result, existing SNMP-based monitoring tools are not suitable for analyzing the latest network flow, and 'Observability' that combines artificial intelligence and machine learning technologies is needed. 

With the transition to hybrid cloud environments such as digital innovation, cloud migration, and SaaS, the IT infrastructure environment is undergoing more complex and diverse changes. Existing network monitoring tools do not have visibility into the cloud environment, so increasing cloud adoption increases network blind spots, which can lead to serious problems.

In particular, the SNMP-based monitoring tool is not a proactive problem-solving method, but a post-processing method designed to handle only known problems, which is not suitable for state-of-the-art network flow analysis. Therefore, it is difficult to provide comprehensive and integrated visibility into today's hybrid network environments.

These constraints pose serious problems for IT departments that need to work in cloud and distributed infrastructure environments. Cloud Native Architecture and the transition to a system that operates everything to the cloud have dramatically increased the number of different types of equipment and network structures, all of which have been managed by IT as part of network monitoring.

At the same time, the cloud environment should be able to understand how various resources (such as cloud provider networks, ISPs, CDN infrastructure, and SaaS applications) and their dependencies interact to form a complex network environment.

However, isolated vendor-dependent platforms with narrow views on network performance have spread, and IT organizations' control over the network has become weaker than before. Instead, the pressure to ensure high availability, latency, and deliver superior customer experience is increasing more than ever.

Network Observability Appears

Over the past decade, data science and analysis tools have become more advanced, and many isolated point solutions have become outdated. Now, a new solution that combines data sources and can be implemented by combining machine learning (ML) and artificial intelligence (AI) technologies is being provided.

The solution of this modern approach is called an observability solution. Observability is an evolved concept of monitoring and means securing visibility into the entire system used by companies.

Network Observability uses a variety of data sources to understand what happens inside the network and the impact of the network's internal state on its business objectives and user experiences. This is an approach that goes far beyond network monitoring, which is limited to just finding out, rather than analyzing and interpreting predictable problems on the network.

Through network observability, IT organizations can identify problems such as network performance and availability, as well as accurately analyze the underlying causes. In fact, network performance monitoring has become more complex as companies move more workloads to the cloud, which provides visibility into these environments and helps them find and solve problems effectively.

In this article, we will look at 10 tips for securing network observerability in the era of digital transformation (DX) to help IT organizations understand the concept of network observerability.

■ Step 1: Accept SaaS-based network observability

Traditional network monitoring tools are appliance-based solutions that are built in an on-premise environment. This means that assets that IT organizations must maintain and manage will be added. These solutions are difficult to scale and must be preceded by large investments in infrastructure.

Today's network management appliances are becoming outdated. Modernizing IT management strategies requires real-time monitoring of all networking assets with minimal effort and migration to a flexibly scalable SaaS-based network observation solution.

SaaS-based solutions for network observability can be paid as much as they are used, and flexible expansion is possible. It is easy to connect even the equipment in the cloud environment, which was difficult to monitor with a physical appliance, and it can expand smoothly as the scope and complexity of the network increase.

■ Step 2: Centralize networking data management

Observations of the latest network infrastructure require the ability to collect and analyze data from various sources, including external services (SaaS application and cloud services), on-premise-based infrastructure, virtual infrastructure, private and public networks.

To analyze all of these data efficiently, real-time analysis must be possible and stored in a central log collection database (DB) that executes the x-x-alert. If data centralization and integration are not properly implemented, the efficiency of automated analysis tasks will be reduced, and it will be difficult to understand network issues in depth. Eventually, network observability tools must be able to collect data from different sources and store it in a central unified repository that can perform as many analysis tasks as necessary.

Kentik helps to combine network performance data with general observability tools. For example, Kentic data can be useful on New Relic's observability platform.

■ Step 3: Utilize metadata and tags

Analysts recommend applying metadata and tags to network observability data to provide context for indexing searches. Typically, tagging or contextualizing other metadata is performed in the process of network observability tools transforming data to prepare for analysis.

Of course, you can manually apply tags to your data. However, large-scale, efficient operations require network observability tools that automatically tag data based on factors such as sources, data types, or endpoints associated with the data.

Kentic automatically applies the ▲ Application ▲ Device ▲ Interface ▲ Autonomous System Number (ASNs) ▲ Country ▲ AWS Region ▲ BGP Path ▲ City ▲ Connection Type ▲ Nexthop ASNs ▲ Protocols (Profiles) profiles to networking data.

■ Step 4: Build a vendor-neutral system

Network observability tools should be designed to observe networks in a vendor-friendly manner. This is the only way to ensure that the IT organization's observerability strategy continues to evolve in the course of various tasks, such as modifying the network architecture and making new cloud services and provider choices. Tuning multiple vendor-independent monitoring solutions does not work.

Vendor-neutral means collecting networking data based on standardized open protocols instead of proprietary data collection or monitoring frameworks. It also means the ability to integrate with all cloud providers, ISPs, and application types. Regardless of format, type, or source, a consistent approach should be taken to analyze and manage networking data.

■ Step 5: Monitor SaaS Application Network Performance

Usually, it is easy to overlook SaaS applications when managing networks. In general, SaaS manufacturers manage SaaS applications instead.

However, from a network performance monitoring perspective, SaaS problems can seriously degrade the user experience. It should be possible to immediately check whether problems such as connection errors, response speeds, DNS performance, and so on of SaaS applications are SaaS application problems, ISP networks, or internal network problems.

There is a problem that SaaS network performance monitoring does not allow direct access to SaaS manufacturers' environments, in other words, data about the network cannot be collected directly. Kentic operates a monitoring agent that tracks the health of dozens of key SaaS applications in real time and can address this problem by deploying it on a global network.

The SaaS APPs performance item on the Kentic Portal allows internal users of the organization to immediately see if performance problems occur with the SaaS applications and platforms they are using. This allows us to determine whether the root cause of the user experience problem is inside or outside the network. 

■ Step 6: Monitor Your Cloud Network

Networking problems in cloud provider environments can be a serious problem for users, as can SaaS networking performance problems. To avoid this problem, public cloud networks and traffic flows should be monitored by applying the same depth and granularity used to monitor their own network infrastructure.

Kentik Map, which tracks the relationship between cloud provider's network, Internet, and on-premise network resources, provides this visibility. Kentik Map illustrates the topology of all these networking components and allows you to understand how packets travel across public cloud areas, VPCs, subnets, and gateways, and how these flows affect the network performance and user experience of your environment.

■ Step 7: External Network Path Analysis

Detecting network problems from external resources such as SaaS applications or public cloud environments can optimize the user experience, but sensing alone cannot solve the remaining problems.
 

Path Analytics provides this visibility. Route analysis can use trace paths to analyze traffic flows in uncontrolled networks, such as the public Internet, and identify various service providers for each path in the network.

Kentik Synetics' Path View menu provides this visibility. The Path View displays data on the hop and physical distance of the external network, and analyzes the external network path from various perspectives to inform how the path changes in chronological order.

■ Step 8: Proactive Network Testing

Modern network management means more than taking action after a problem occurs. If you don't know what the problem is or under the assumption that you're wrong, you'll already have a hard time in itself.

Network management should be able to continuously test the network to take preemptive action, and to identify problems before they affect end users. The most desirable way to do this is to generate synthetic traffic for test scenario emulation.

These tests should be able to analyze and accurately analyze various aspects of network performance, such as page load time, jitter, packet loss, DNS response speed, and API request performance. In particular, it should be possible to evaluate network performance based on specific conditions and factors such as the CDN traffic used or the geographical location where the request was initiated.

Kentik Synetics provides comprehensive and granular testing capabilities through page load testing, making it easy to preemptively test network infrastructure. With Kentic, various comprehensive preemptive testing capabilities that monitor SaaS application performance, major cloud networks, BGP paths, and DNS performance are available immediately at no additional cost. It also provides a user-defined test library for its own network.

■ Step 9: Accept NetOps

Currently, network management organizations cannot proceed independently. Network operations teams, server operations teams, developers, DevOps teams, as well as other stakeholders should continue to communicate and collaborate. Collaborative strategies are essential to ensure that different teams collaborate and that networks and other IT tasks operate complementarily.

NetOps is a network management approach that enables this collaboration. Network engineers and other technology stakeholders should use the same language and work toward common goals, leveraging shared methodologies that are important to both network operations teams, developers, and DevOps teams, such as observerability.

Kentic supports NetOps operating environments through tools such as the Kentik Firehose, which sends network observability data from Kentic to Debops observability and warning tools such as Neurelic, Splunk, and Prometheus. Working with the same data for all stakeholders can optimize their ability to understand how networking fits broader IT goals.

For collaboration between NetOps and DevOps, Kentik has established a developer community called Kentik Labs to share information on open source networking. Kentic Labs provides a series of open-source projects, such as the Kentic Fire Horse, to help break the boundaries existing in today's network monitoring technology. These open-source projects are enabling communities to share the information needed to make it observable for all problems occurring in the network.

■ Step 10: Embrace AIOps for Network Management

AI Ops refers to the ability to automate complex IT operations using AI and machine learning, and has become a hot topic in the IT field in recent years. However, AIOps is not just for general IT operational tasks, but it can still be effective in network management.

Performing AI-based analysis on complex networking datasets with tools such as Kentick can identify problems that engineers cannot detect on their own. It is also possible to find out the cause of performance problems more quickly within complex multi-tier networks.

Securing real-time network visibility and control rights

Today's networks are changing dramatically differently from just two to three years ago. The transition to virtual network infrastructure environments and the transition to the cloud means that existing network technologies and tools are no longer sufficient to quickly detect and mitigate network performance problems in the current IT infrastructure environment.

The only solution is to switch from existing network monitoring technology to the latest network observerability technology. Platforms such as Kentic allow real-time visibility of all networking resources and assets, including those owned by engineers and those provided by external manufacturers.

This allows IT organizations to gain control over network resources. You can also gain confidence that your organization's network management strategy can continue to evolve and expand as your business progresses.

Source: DataNet (http://www.datanet.co.kr)