Effective network monitoring should identify performance issues and bottleneck aids in the early detection of problems. Proactive monitoring can help avoid network downtime or failures.

The Most Important Advantages Of Network Monitoring

Complete Network Visibility

Network monitoring provides managers with a detailed overview of all the linked devices on a network. It shows how data flows across them and allows to rapidly find and resolve critical incidents and prevent them in the future.

Improved Use of IT Resources

Network monitoring systems’ hardware and software technologies decrease manual effort for IT staff. This means that valued IT employees can dedicate more time to key organizational tasks.

The Capacity To Detect Security Risks More Quickly

Network monitoring assists companies in understanding what “typical” network performance looks like. As a result, when odd behavior happens, such as an inexplicable surge in network traffic levels, administrators may rapidly identify the problem—and evaluate whether it is serious.

Early Detection Of Future Infrastructure Requirements

Network monitoring systems can generate information on how network components behaved over time. Network administrators can predict when the firm may need to consider updating or adopting new IT infrastructure by examining these reports.

Who Is Responsible For Network Monitoring

The network administrator is responsible for the proper functioning of the network infrastructure and must perform the following daily:

• Checking the health of servers, Internet connection, e-mail, other applications, as well as office equipment;
• Side connection to servers;
• Checking free space on hard drives, RAM and other capacities;
• Verification of data backup implementation.

The ongoing activity is aimed at the timely detection of errors in the operation of software and hardware, which minimizes the risk of critical problems.

An alternative to a network administrator is automatic monitoring systems that can check workstations, servers, site availability, client and server applications and services, printers, scanners and other network equipment.

Network monitoring systems are indispensable assistants to network administrators. They allow not only collecting but also visualizing, structuring and sorting information about infrastructure components and events, as well as independently notifying administrators about incidents, reducing the time to eliminate them.

The Main Functions Of Network Monitoring Systems

Discovering

Every network monitoring begins with the process of discovering. Obviously, you can’t monitor a network until you know what’s on it.

Network monitoring tools like Acure.io find every device on the network, including routers, switches, firewalls, servers, printers, and more. A network monitoring system automatically assigns the proper device role to each identified device when the discovery procedure is complete.

But discovering the components is only the start. You must understand the connections between the elements in order to monitor the network effectively. Because an issue with one device’s performance may affect another’s.

For instance, when a switch malfunctions, the whole network of devices attached to that switch cannot interact. If this is the switch linking the servers hosting an organization’s CRM system, there is a critical problem.

In the future, understanding the relationships between the components of the system will help to perform not only the root cause but also the impact analysis.

Network Vizualization

Their eyes are a network administrator’s most important diagnostic tool. They can save spending hours debugging network issues.

Network monitoring systems should provide network maps to assist network administrators in visualizing their networks. Network maps give users a clear and organized visual overview of the devices in a network, their connections, and the most recent status.

Some programs just offer a sketching tool, leaving network mapping to the administrator’s expertise. Other platforms, like Acure.io, can perform auto-discovering of all system elements and their connections and transform them into a full map in the form of topology. You can monitor the health of both the entire system and each configuration item separately and customize the map in a way that suits you the most.

Proper visualization helps reduce incident resolution time. With auto-discovered relationships, you can quickly identify the root cause, saving time on troubleshooting rather than searching.

Constant Monitoring

Turn-key device responsibilities that specify what needs to be monitored are provided by network monitoring software. Device roles can be changed by network administrators, or new ones can be created entirely. Monitoring tools give network administrators access to a wide range of monitors. Network administrators should first keep an eye on CPU, memory, disk and interface usage as well as Ping availability and latency.

The majority of network monitoring software also tracks temperature in wire closets and other hardware elements like fans and power supply in switches. Additionally, they may keep an eye on network services like HTTP, TCP/IP, and FTP.

Alerts and Notifications

Network administrators may respond to issues before they have an impact on users, apps, or the company. That’s why network monitoring systems should immediately alert network administrators when a problem occurs. They send out notifications by text, email, and logging.

CPU, memory, and interface usage are performance measures that change during the day. During times of peak usage, they may go beyond thresholds for a few seconds or minutes. Network administrators don’t want to deal with these small hiccups. Notifications from monitoring systems are set up with a time element to prevent this.

Network admins also need sleeping mode. But unfortunately, a lot of them should be ready for alerts they are stormed with 24/7. Some systems allow administrators to set blackout times during which notifications are disabled. Often alerts are duplicated, so many engineers do the same thing, which leads to additional time and money costs. To avoid storm alerts, monitoring systems use functions of noise reduction. For example, Acure.io allows sorting events by priority and shows only important notifications.

SLA Reports

Network administrators are actively involved in the ongoing planning, analysis, and redesign of the network. The network monitoring systems offer both historical and real-time monitoring data to support this life cycle.

Network administrators can use this information to:

• Verify that network designs are producing the desired results
• Show that SLA commitments are being met
• Expose trends to deliver the performance required by users, applications, and the business
• Rapidly identify and resolve performance issues

Network monitoring programs are indispensable assistants for every system administrator. They allow you to quickly respond to anomalous activity within the local network, keep abreast of all network processes and, thus, automate some of the administrator’s routine activities: primarily those related to network security. Let’s take a look at which network monitoring programs are relevant in 2022.

Popular Network Monitoring Tools (2022)

1. Zabbix

Zabbix is a proper solution for networks and applications which uses a fascinating robust tool for monitoring and event correlation.

Zabbix allows you to customize your dashboards and web interface to focus on the most important network components. Notifications and problem escalations can be based on custom actions that are applied to hosts or groups of hosts. Actions can even be configured to run remote commands, so some script of yours can run on a controlled host if certain event criteria are observed.

The program graphs shows data such as network bandwidth and CPU usage and collects it for custom display systems. In addition, Zabbix supports customizable maps, screens, and even slideshows showing the current status of monitored devices.

Basically, Zabbix works with software agents running on monitored systems. But this solution can also work without agents, using the SNMP protocol or other monitoring capabilities. Zabbix supports VMware and other virtualization hypervisors by providing detailed hypervisor performance and activity data. Particular attention is also paid to the monitoring of Java application servers, web services and databases.

Hosts can be added manually or through an automatic discovery process. A wide range of default templates apply to the most common use cases such as Linux, FreeBSD and Windows servers; widely used services such as SMTP and HTTP as well as ICMP and IPMI for detailed network hardware monitoring. In addition, custom checks written in Perl, Python or almost any other language can be integrated into Zabbix.

Zabbix is free since it was released under the GPL license. There are no limitations on the number of monitored devices, you can use Zabbix to monitor many thousands of devices absolutely free.

2. Acure

Acure is a newly released cloud-based AIOps observability and automation patform that offers a single, interactive ecosystem for monitoring and resolving common cases encountered by modern IT environments.

Acure has a simple and user-fiendly interface that allows the creation of a workspace in seconds. You can also connect it to popular IT monitoring systems, such as Zabbix or Prometheus, using templates or manually, add data from virtualization systems, such as VMware, add Azure or AWS cloud providers, collect raw logs from your services.

As it was mentioned in examples before, Acure automatically discovers configuration items and connections between them to place them on automatic monitoring. IT infrastructure is visualized as a topology graph which shows connections, subordinations and the health of configurations units. IT allows you to quickly and efficiently perform root cause and impact analysis.

Acure automates not only discovering but also rules and actions. You can create scripts and use automation to make changes smoothly to the whole system without extra efforts. The Plugins system allows the administrator to create and add automated notification methods via e-mail, sms or popular messengers.

Acure also has automated testing feature to set up checks that can imitate user actions and report any anomalies as they run, even when infrastructure monitoring does not signal a problem.

Pricing of this software is $0 which offers: unlimited events, unlimited users, unlimited automation rules, up to 5GB of daily incoming data and up to 100 tests daily. 

3. Nagios

Nagios is an established network monitoring software system that has been in active development for many years. It can be difficult for beginners, but the rather complex configuration is also an advantage of this tool, as it can be adapted to almost any monitoring task.

Nagios monitors all mission-critical infrastructure components, including applications, services, operating systems, network protocols, system metrics, and network infrastructure. Multiple APIs allow for easy interaction with both internal and external applications.

The platform offers a custom email, SMS and instant messaging notification mechanism for most popular messengers.

In addition, the display feature shows all monitored devices in a logical, color-coded representation of their location on the network, allowing problems to be shown as they occur.

Nagios is completely free.

4. Icinga

Icinga started as a branch of the Nagios monitoring system, but has recently been rewritten into a standalone solution known as Icinga 2. However, Icinga 1 is compatible with a large number of Nagios plugins and configuration. Icinga 2 was designed to be less bulky, performance-oriented, and more user-friendly. It offers a modular architecture and multi-threaded design that neither Nagios nor Icinga 1 has.

There are several variations of the web interface for Icinga, but the main difference between this monitoring software solution and Nagios is the configuration that can be done through the web interface rather than through configuration files.

Icinga integrates with a variety of monitoring and graphical display software packages such as PNP4Nagios, inGraph and Graphite to provide a robust visualization of your network. In addition, Icinga has advanced reporting capabilities.

Also free like Nagios.

5. Datadog Network Monitoring

Datadog Network Monitoring helps to monitor the performance of network devices.The technology, which is cloud-based, can scan a network to find every connected device. The network monitor will compile an asset list and a network topology diagram using the data from this study. By using this method, the system runs its own setup processes.

Any modifications you make to your network are immediately picked up by the continuous autodiscovery process, which also refreshes the inventory and topology map. Additionally, the program can recognize virtual systems and expand connection monitoring to include cloud resources.

End-to-end visibility of all connections is provided by Datadog Network Monitoring, which is also associated with performance problems shown in log messages.

The system’s dashboard is available through any common browser and is stored in the cloud. This gathers data on network performance from many sources, centralizes it, and covers the complete network, end to end and link by link.

Datadog has a free version for up to five hosts.

6. Cacti

Cacti is a program included in the LAMP suite of server software that provides a standardized software platform for plotting virtually any statistical data. There are templates for monitoring a wide range of equipment, from Linux and Windows servers to Cisco routers and switches, basically anything that talks on SNMP (Simple Network Management Protocol). There are also collections of third-party templates that further expand the already huge list of Cacti-compatible hardware and software.

Using Network Weathermap, a PHP plugin for Cacti, you can effortlessly create real-time maps of your network showing the congestion of communication channels between network devices, implemented using graphs that appear when you hover over the network channel image

Cacti is a powerful graphing and trending network performance toolkit that can be used to monitor virtually any monitored metric in a graph. The solution also supports almost limitless customization options, which can make it overly complex for certain applications.

Cacti is absolutley free.

7. Auvik

Auvik is a variety of system management tools included in the cloud-based network monitoring solution.

A network discovery procedure is the first step in the service offered by Auvik. This automatically fills in all of the fundamental data required for the monitor to function.

The usage of third-party products for which the network monitor has connectors allows Auvik to be expanded.

Auvik is built for remote system monitoring. A set of thresholds is used to monitor networks. A threshold for resource utilization or system performance is applied to each of the network activity metrics that the service monitors. A warning will be generated by the Auvik service if that threshold level is exceeded. This implies that unless a problem manifests, your team of technicians need not pay attention to the network.

Auvik offers 14-day free trial version.

8. SolarWinds

SolarWinds Network Performance Monitor is the network performance monitoring tool that can track device status via SNMP. It has the ability to automatically find network devices linked to your network. Utilize the dashboard to keep a close eye on the performance and availability of all connected network devices.

A network topology map, which shows how your infrastructure is connected, may also be used to see any found devices, programs, or services. The NetPath function enables you to track packet transfers hop-by-hop, which can aid in more accurately identifying the cause of performance network problems.

You can specify alert trigger circumstances using the custom alerts system. The program will notify you through email or SMS when the trigger requirements are satisfied to let you know that an event has occurred.

SolarWinds provides you with 30-days free trial.

***

Network monitoring is important to understand how well the network is operating. The network administrator can control various aspects of the network, such as bandwidth usage, server performance, and security.

Monitoring can be done manually, but it is better to use network monitoring tools that monitor various aspects of the network and help administrators keep an eye on the health of the network and save time thrugh automation and clear system visualization.

Therefore, no matter which of these above systems you use, it will provide you with at least the functionality of another system administrator.

Сообщение What Is Network Monitoring? +8 Popular Tools in 2023 (Updated) появились сначала на Acure AIOps Platform.

What is Log Monitoring?

Collecting logs for future observation is called log monitoring. It is the process of categorizing logs and searching for abnormalities that might cause system problems.

The Steps of Logs Processing

In the processing logs, at least four stages can be distinguished:

1. Collecting logs from servers and containers using agents and sending them via network protocols to a centralized repository either directly or through aggregators with pre-processing;

2. Log messages are parsed inside the aggregators, enriched with additional information (time stamp, source ID, location tag, etc.), converted to a single format and sent to storage ​​ready for indexing;

3. Direct storage of logs and storage management;

4. Analysis and visualization of data obtained from logs.

Log Monitoring Tools You Can Use for Free

There are about a hundred software solutions on the market with the required propensity to collect and analyze logs. In this article, we will look at 4 popular FREE log monitoring tools and compare their functionality as aggregators, a storage system and instruments of visualization.

• ELK Stack
• Graylog Open
• Acure
• Grafana Loki

1. ElasticSearch + Logstash + Kibana

ElasticSearch, Logstash and Kibana were originally developed as open source products and developed separately from each other, but in 2015 they merged under the Elastic brand and began to be a single product – the ELK stack.

Logstash is a server-side data processing pipeline that simultaneously receives data from multiple sources, transforms it, and then sends the processed data to Elasticsearch storage. The main functionality of Logstash as a log aggregator is the following:

• The ability to accept data in different formats and different sizes from many sources using all sorts of input plugins;
• Processing of input data using separate plugins;
• Output plugins are used to send processed structured logs to various data storage systems (most often Elasticsearch), message brokers (kafka, redis), etc.
• Logstash has the ability to use different codecs to decode and encode input and output data: the main ones are plain for working with Plain text messages and JSON for working with events in JSON format.
• The processing pipeline configuration is set in a simple text file.

The centerpiece of the ELK stack is Elasticsearch, a high-performance full-text search engine based on the Apache Lucene open-source library. It combines the functions of a database, as well as a search and analytics engine, with the following main features:

• Elasticsearch is non-relational document-oriented data storage (NoSQL) in JSON format without strict structure;
• All work with the database is based on JSON requests using the REST API, which allows you to add, view, modify and delete data, issue documents by index, calculate various statistics;
• The absence of a schema allows you to upload any text documents to the storage and index them automatically;
• The indexing procedure consists in processing the text of the document by the analyzer and adding the output set of tokens to the search index;
• Elasticsearch provides fast and flexible full-text search across all fields in all data warehouse documents (words from a query are searched by index);
• Support of several different fuzzy search methods,
• Work with texts of oriental languages CJK (Chinese, Japanese, Korean) is supported;
• Elasticsearch has high scale-out and replication capabilities for high-load projects with large volumes of data, as well as relatively easy management and good fault tolerance.

Kibana, as part of the ELK stack, is the tool responsible for presenting search query results to Elasticsearch in a human-readable form. In essence, Kibana is a web interface for searching, viewing and interacting with documents in the data warehouse, allowing you to:

• send search queries to Elasticsearch (using special KQL syntax, Kibana Query Language) and carry out all kinds of filtering of the results obtained,
• analyze data and visualize the results in the form of various charts, histograms, tables, graphs, maps, etc.,
• create, modify, save and load custom dashboards that combine several visualizations on one screen, and third-party dashboards can also be loaded,
• administrate Elasticsearch data warehouse, manage indexes and processing pipelines,
• manage the rights and levels of user access to different elements of the system.

It should be noted that in the latest versions (starting from 7.14) Elastic actually switched to collecting logs using their own agent programs (Elastic Agents) installed on servers and containers instead of using input plugins for Logstash.

Elastic Agent is a single, unified way to add monitoring of logs, metrics, and other types of data per node, making it easier and faster to deploy monitoring in a large system.

The Kibana web interface has an additional Fleet panel that can be used to add and manage agents, and you can also install ready-made integrations for popular services and platforms from it. The integrations provide a fairly simple and fast way to connect standard data sources, plus they come with customized elements such as dashboards, visualizations, and pipelines for extracting structured fields from log messages.

2. Graylog Open

One of the systems competing with the ELK stack for collecting and processing logs is Graylog Open, which also uses Elasticsearch as a log storage and search engine. Graylog itself directly performs the functions of a log aggregator and visualization tool in the form of a client single-page browser application.

In terms of log aggregation, the Graylog functionality is very similar to the Logstash functionality:

• the ability to receive data in different formats from various sources using various input plugins,
• processing input data using so-called extractors, in which the rules for extracting the values of the desired named fields from message strings can be specified using regular expressions and grok patterns,
• sending processed structured logs to the Elasticsearch data storage system or, using special output plugins, to other systems,
• the ability to work with both simple text messages and events in JSON format .

Since Graylog uses Elasticsearch, it is similar to the ELK stack in terms of search queries and working with the data warehouse and index, and in terms of processing and visualizing data from log messages, the Graylog web interface provides functionality that is similar to Kibana.

Despite the significant similarity in the principles of operation and user functions between the ELK stack and Graylog, the latter has a number of features:

• additional mongoDB database to store configurations and settings,
• ability to create and combine multiple search queries into one action and view the results on a dashboard screen.

But the most important difference and advantage of Graylog Open over the ELK stack is the presence of a built-in notification system (alerts) in case of any specific situations or events in the process of collecting logs (there is a notification system in ELK, but it is paid, although there are also free third-party plugins with similar functionality).

Graylog Alerts are periodically self-triggered search queries that can send notifications if certain conditions are met as a result of the query. Graylog allows you to set a variety of alert conditions based on the data it collects, the following are available by default (for others, you need to install plugins):

• the filter condition is triggered when at least one message arrives in the specified stream, in which a specific field contains the specified value,
• the aggregation condition is triggered when the result of the aggregation (usually a counter of the values of some fields) exceeds a certain threshold value.

Notifications can be sent via email or http to any configured recipient.

Graylog, as well as the latest versions of Elastic, provides the ability to install its agents on each controlled system (Graylog Collector Sidecars), which collect the necessary information and send it to the server.

Using a separate Graylog panel Sidecars in the web interface, you can centrally manage and maintain a consistent configuration of various logging agents on all nodes. For this, a system of tags is used, which are created through the web console and contain configurations for collecting a certain type of logs (for example, Apache logs, DNS logs, etc.), and Sidecar agents on specific machines can “self-configure” by the specified tag and start sending data.

3. Acure

Acure platform is a freemium solution for log monitoring and AIOps. There are no restrictions on the number of users, events or automation rules and up to 5 GB of daily incoming data. Although Acure is still a very young product, in terms of functionality it can compete with well-established log monitoring tools like the ELK stack, since it has all the necessary elements to organize a data collection and processing system.

As a log message aggregator, Acure can perform the following functions:

• receiving data in JSON format from various sources via http and many connection templates (Zabbix , Prometheus , Nagios, Ntopng , SCOM, etc.);
• its own low-code engine for processing of input data: extracting named field values from message text using regular expressions, transformation and change of field values, adding new fields and their values (enriching data with new labels);
• storage of logs in ClickHouse database;
• management of user access rights to configurations and settings of data flows.

Acure uses the ClickHouse DBMS as a data store, as well as search and analytical engines, which gives it the following functionality:

• sending search queries (with a syntax similar to Lucene) and all kinds of filtering of the results,
• column type of the ClickHouse database provides very fast processing of search and analytical queries (lack of indexing),
• the model for the database is generated automatically based on the JSON model of a particular stream, the necessary fields are added if it’s required.

In terms of data visualization, Acure has the following functionality:

• displaying the total number of processed events as a time series histogram;
• representation of the values of various fields in the form of a histogram of frequency (for numeric fields, the minimum, maximum and average value are also calculated);
• support for Markdown markup in displaying the text of log messages (you can write your own handler that will add Markdown markup to the required fields);
• visual filter constructor with the ability to include and exclude specific field values in the filter by clicking on them.

In addition to the standard presentation of raw log and event data, there are also more specific visual components:

• timeline for events and work planning (this is convenient when you need to share a schedule of important events, routine maintenance or marketing activities with colleagues, and also on the timeline you can investigate the root cause of the incident and correlate by the time of the event);
• heat map of the state of the selected set of services and monitoring objects;
• topology graph with the states of individual objects and the transfer of health statuses with the formation of the health of these objects;
• other screens for more specific tasks, such as SLA calculation and business impact analysis.

Acure also has a notification system that works on the same principles as Graylog or Splunk, there is a synthetic trigger mechanism for generating events, and a “Rules and Actions” mechanism for triggering the necessary actions to trigger complex rules.

4. Grafana Loki

Grafana Loki is a relatively recent addition to the list of log monitoring tools and analysis software solutions – the project was launched in 2018. Grafana Loki works in the same way as the ELK stack and Graylog, but with its own specifics.

Loki itself is nothing more than an indexer of the structured logs it receives and indexing is not carried out according to the entire text of log messages, only according to log metadata (tags or labels), while the logs themselves are compressed side by side into separate files and stored either locally, or in cloud storage like Amazon S3 or GCS. Full-text search by index is not possible in Loki.

The data is searched first by indexed fields and then the text of the selected logs is scanned with regular expressions. This approach avoids problems with RAM requirements (a full-text index of logs is often comparable in size to the logs themselves, and for a quick search it must be loaded into memory), but it significantly increases the search time in case of a large volume of logs.

To speed up the search, Loki can split the request into several parts and execute them in parallel, so that the processing speed depends on the allocated resources.

Secondly, Loki delegates all the main work (parsing, finding named fields in the text of log messages, their transformation and reduction to a common format) to collector agents. Loki ‘s native agent is Promtail, although it is possible to use Fluentd, Logstash and some others.

Currently, Promtail can only read log messages from local files and from the systemd service, but it borrows Prometheus ‘s service discovery mechanism, which allows it to automatically integrate with Kubernetes and collect logs from nodes, services, or pods, immediately putting tags on based on metadata from Kubernetes .

The mechanism for processing log messages and bringing them to a structured form in Promtail is similar to the mechanism of extractors in Graylog (although there are some nuances everywhere), but Promtail does not have a graphical interface and the entire configuration of processing pipelines must be specified separately in a text file, which is not always convenient.

A tool for visualizing data from logs in the Grafana system Loki performs, of course, Grafana . Search queries in Loki can be sent in a special Grafana interface Explore, queries use LogQL very similar to Prometheus PromQL . Like Kibana, Grafana provides a wide range of data visualization options:

• search query results can be displayed in the form of various graphs, histograms, tables, heat maps, etc.,
• it is possible to combine several visualizations on one dashboard, which can be saved, loaded and modified, as well as exported and imported from Grafana.com.

Grafana has a highly developed built-in alert system, which, similar to Graylog, are periodically self- launched search queries that generate notifications when certain conditions are met. At the same time, the options for configuring and setting up alerts provided in the Grafana graphical interface are very wide, and notifications can be sent through many channels: email, slack, telegram, discord, etc.

Instead of a conclusion…

For all the considered log monitoring tools in the latest versions, it should be noted a strong trend towards delegating the functionality of the primary processing of logs to their local agents, which can lead to some simplification of the functionality of log aggregators. It is also worth noting the trend of a gradual reduction in free functionality and the emergence of more and more paid ones.

This applies to ELK and Graylog. ELK stack is one of the most complete log monitoring tools for collecting and processing data but alerting only in paid versions significantly spoils the picture. Graylog is also more of a log analysis tool. Grafana Loki is one of the simplest and lightest log monitoring tools, and it is suitable for solving narrow problems when full observability of systems and services is not needed.

Acure, despite being a young platform, is significantly superior in functionality to the presented log monitoring tools and provides full observability and powerful functionality absolutely for free.

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Сообщение 4 Best Free Log Monitoring Tools in 2023 (Updated) появились сначала на Acure AIOps Platform.

With AIOps, Ops teams can control their modern IT infrastructures’ enormous complexity and volume of data, preventing outages, maintaining uptime, and achieving continuous service assurance.

With IT at the center of digital transformation efforts, AIOps enables enterprises to function faster than modern business demands while providing an exceptional user experience.

What Is an AIOps Platform?

AIOps platforms use artificial intelligence and machine learning algorithms to automate and improve IT operations. The goal of an AIOps platform is to streamline IT operations, reduce downtime, and proactively identify and resolve IT incidents before they impact end-users.

A typical AIOps platform integrates multiple data sources, such as log files, performance metrics, network traffic data, and other data sources, to build a comprehensive view of IT infrastructure and application performance. The platform then uses AI and machine learning algorithms to analyze this data and identify patterns, anomalies, and potential problems.

The platform can also automate IT operations tasks such as incident management, problem management, and change management. By automating these tasks, an AIOps platform can reduce the amount of manual work required by IT teams, improve incident response times, and reduce the risk of human error.

AIOps platforms are typically used by large enterprises that have complex IT infrastructures, but they are becoming more common as more organizations adopt digital technologies and seek to improve their IT operations.

Here Is How It Works:

To fully automate and monitor IT processes, AIOps uses five different types of algorithms. The algorithms are as follows:

1. Data Selection

AIOps initially filters away up to 99% of the data to find the problematic ones because enormous amounts of redundant data are collected and stored.

2. Pattern Discovery

For more sophisticated analytics, use correlation to organize the selected, significant data items and discover links between them.

3. Root Cause Analysis

The initial sources of the problems are uncovered in this step. RCA helps not only to detect a problem, but also shows its cause, that helps to prevent its occurrence in the future.

4. Collaboration

The relevant IT teams are brought together to discuss remedial actions when the underlying causes of the problems have been determined. Data on the incidents are also preserved to help with future problem diagnostics.

5. Automation

Automating response and correction as much as feasible improves the accuracy and speed of solutions.

Why AIOps is a Future?

AIOps gathers and examines data to make sophisticated automated judgments. This data is used to forecast potential future events that could impact performance and availability before they happen. AIOps accelerate problem solving and deployment.

AIOps adoption is skyrocketing, which is a reflection of a pragmatic change that is transforming IT operations.

Businesses can benefit from the following by implementing AIOps:

Improved Collaboration

Within IT groups and between IT and other business divisions, AIOps enhance collaboration and workflow processes. Teams may immediately comprehend their responsibilities and requirements using customizable reports and dashboards.

Improved Business ROI IT Productivity

Businesses benefit from a reduction in the mean time to repair, outages prevented through issue prediction, and automation of repetitive manual processes—AIOps aid in maximizing your team’s overall capacity while lowering costs and increasing output.

Digital Transformation Success

AIOps delivers commercial value for firms adopting a digital-centric strategy by reducing time and effort so your employees can concentrate on innovation instead. AIOps provides end-to-end visibility into the infrastructure and apps.

Improved Performance Monitoring and Service Delivery

AIOps anticipates resource usage and performance problems. By using probable cause analytics, it concentrates on the most likely cause of an issue—clustering and anomaly detection aid in locating the underlying problems that cause events.

By offering a technology foundation for managing the machine learning lifecycle through automation and scalability, Machine Learning Operations (MLOps) enables enterprises to ease many problems on the way to AI with ROI.

Data scientists and operations specialists can work together and communicate using MLOps, a set of techniques. Implementing Machine Learning and Deep Learning models in expansive production environments can be automated while also improving quality and streamlining the management process. Aligning models with both business demands and regulatory standards is simpler.

Top 20 AIOps Tools & Platforms (2022)

Now that we have discussed what AIOps is and how it benefits organizations in handling operations without encountering any troublesome flaws let’s look at the list of the top AIOps tools you can use for your firm.

1. Acure

Acure is a topology-based AIOps Observability & Automation Platform that offers a single, interactive ecosystem for monitoring and resolving common cases encountered by modern IT environments. By identifying potential flaws and providing insights earlier, Acure helps to eliminate failures that could disrupt your businesses.

It offers a role-based paradigm and system entity ownership to construct a functional shared data space for information sharing and secure collaboration. It is built for many contexts, including multi-cloud, SaaS, on-premises, and hybrid IT.

Key Features:

• Integrating all control and AIOps monitoring systems into a solitary user interface.
• High data security standards, such as Extended SSO authentication, SSL protocol, Complex password, user management policies, and many more, will prevent attackers from accessing the system.
• A multirole paradigm makes it simple and rapid to configure user access to system features.

2. Zenoss

Zenoss enables IT, professionals, to obtain total visibility in today’s most challenging, dynamic, and contemporary multi-cloud IT settings. The AIOps blend full-stack monitoring with machine learning analytics to ensure you get the most out of big data

Zenoss uses full-stack monitoring and machine learning to process all your data sources, including metrics, dependency data, events, logs, and streaming data, to give an unheard-of level of AIOps power. The platform provides the necessary context for machine learning algorithms to automatically identify the fundamental causes of issues by feeding them live topology data.

Key Features:

• A unified, contextualized view is created using data from logs, events, metrics, model data, performance monitoring, and other sources.
• Before problems result in service interruptions or degradation, predictive analytics and anomaly detection can find them.
• By removing noise, mean time to resolution (MTTR) is significantly shortened using machine learning, visualization, retrospective analytics, and dashboards.

3. MicroFocus OpsBridge

OpsBridge by MicroFocus is an AIOps-powered automated event correlation, analysis, and performance monitoring platform intended for use in various contexts, including multi-cloud, SaaS, on-premises, and hybrid IT.

OpsBridge can use more than 200 technologies and tools to gather and combine data from monitoring (metrics, logs, and events). It discovers the topology to give monitoring capabilities and event correlation to identify the source of issues. Big Panda offers ML and AIOps-based big data analytics and centralizes the data in a single access point.

Key Features:

• Automated discovery of dependencies across services, topologies, and applications.
• Data consolidation and analysis across multiple vendors and domains (real-time and historical access to metric and event data).
• Subject matter experts, IT and non-IT executives, and others can use role-based stakeholder dashboards.

4. Mosaic AIOps

Larsen & Toubro’s mosaic AIOps Infotech is an AI-driven platform for business IT operations. It provides greater visibility, streamlined processes, automated detection, remediation, and improved asset monitoring.

Through the deployment of AI-led IT Operations, Mosaic AIOps allows Enterprise IT transformation. Fostering collaborative support practices across the operations teams entails improved asset monitoring, automated problem identification & remediation, and more innovative service desk actions.

Key Features:

• Providing end-to-end visibility into the health and performance of assets throughout the hybrid IT landscape
• A consistent support experience allows the integration of all IT Operations functions through a single platform.
• To simplify, monitoring eliminates clutter and surfaces only the most essential actionable.

5. Watson AIOps

Watson AIOps integrates data from multiple sources to provide real-time insights and recommendations. It enables you to address complicated IT issues rapidly to minimize service disruptions and avoid outages.

Watson AIOps provides insights via a ChatOps experience, so warnings, recommendations, and actions are provided to the collaboration platforms and tools that IT teams now use. Applying AI throughout IT operations may predict problems and handle them more effectively. This IBM AIOps technology may detect anomalies, automate workflows, resolve incidents quickly, and manage events.

Key Features:

• Can provide comprehensive insight and awareness as complex problems emerge, helping teams diagnose and handle mission-critical issues more quickly.
• Employs traceable AI to assist teams and stakeholders in putting their trust in AI-powered recommendations and insights for mission-critical workloads.
• Connects signals from structured and unstructured data sources to deliver a clear picture of abnormalities, with links to sources for faster inquiry and resolution.

6. BigPanda

BigPanda’s SaaS platform for Event Correlation and Automation, driven by AIOps, assists enterprises in preventing and resolving IT disruptions. BigPanda automatically aggregates warnings from Datadog and any third-party tool and correlates them into context-rich incidents that help prevent outages and reduce incident management agony.

BigPanda integrates with all of Datadog’s monitoring products, including Infrastructure, Log Management, and APM, out of the box. It automates incident response manual tasks and correlates data from monitoring, topology, and change tools into actionable insights.

Key Features: 

• Enhance the speed with which incidents and outages are resolved by automatically finding the most likely root cause of problems. BigPanda detects root cause modifications as well as infrastructure-related root causes.
• Data from all observability, monitoring, change, and topology tools should be combined. BigPanda’s Open Box Machine Learning will correlate the data into a small number of actionable insights, allowing incidents to be recognized in real-time before they grow into outages.
• Integrating BigPanda with enterprise runbook automation technologies speeds up remediation.

7. AppDynamics

AppDynamics is a prominent APM solution for managing application performance and availability in cloud computing environments. It provides end-to-end visibility and real-time monitoring, allowing you to prioritize what’s critical and take rapid decisions and actions.

AppDynamics employs AI/ML to provide total visibility into the whole business domain while reducing the overhead of IT operations responsible for running the business. It promotes a more proactive approach to performance management. AIOps systems connect performance insights to business outcomes by including all application environment data.

Key Features:

• AIOps combines all data and builds causality/relationships, giving IT a high-level picture of the problem and allowing it to slice and dice the data as needed for a better understanding of the scenario.
• AIOps filters and correlates important data into issues by absorbing data from any component of the IT environment.
• AIOps platforms reduce MTTR and expenses associated with performance concerns by providing faster answers to outages and other problems.

8. Netreo

Netreo is full-stack monitoring software that enables customers to automate and monitor everything in the company from a single dashboard. Network administrators, system administrators, IT directors, and managers can use Netreo to gain total access to their IT ecosystems.

Network and system administrators can use Netreo to spend less time configuring their NMS platforms and more time assisting end-users, engineering, and satisfying service-level agreements (SLAs).

Key Features:

• Thresholds are automatically baselined against past readings and exceptions to reduce false positives and alert noise.
• To ensure there are no blind spots, compare all measurements to best-practice key performance indicators (KPIs).
• Change the monitoring infrastructure to automatically adapt as you become aware of the system and environmental changes.

9. Moogsoft

Moogsoft is a well-known AIOps platform that provides services to help streamline IT operations. Moogsoft is renowned for its monitoring solutions, which enable teams to prioritize problems, assure uptime, and address issues rapidly, resulting in higher agility and lower risks.

By identifying issues before they become essential, determining who should respond, and comprehending trends to avoid reoccurring problems, Moogsoft offers an AIOps solution that enables continuous availability. Additionally, it integrates quickly across your tool stack and collects all of your observability data in one location for the duration of an incident’s lifecycle.

Key Features:

• To discover anomalies early in the lifecycle, use metrics and events as a data source.
• Context is essential when an outage occurs. Advanced correlation technology from Moogsoft automatically identifies abnormalities and connects the dots between all warnings to help you find the root of the problem more quickly.
• By automating the issue management procedure, Moogsoft offers continual improvement and frees up your time for other significant and pleasant duties.

10. Instana

Instana‘s AIOps automatically detect and map all applications, services, infrastructures, events, and interdependencies. Instana employs stream processing to collect and analyze all data in real-time, making incoming data immediately actionable so that issues may be resolved without delay.

Instana continually finds and maps every service, automatically profiles every process, ingests observability metrics, tracks every request, and maps every application dependency.

Key Features:

• All application dependencies automatically are found, mapped, and kept track of.
• Instant contextual knowledge of the caliber of each service will let applications run more efficiently.
• Quickly fix problems by taking wise action; stop searching for difficulties and start looking for solutions.

11. Dynatrace

Dynatrace is a leading provider of cloud monitoring services. It is an American technology business that provides artificial intelligence-based solutions for monitoring and optimizing application performance, operations, infrastructure, and user experience.

Dynatrace continuously processes billions of dependencies in milliseconds, discovers errors instantly, and provides precise root cause analysis. There is no guesswork or time-consuming model training compared to machine learning methodologies. With the root cause identified, you may address issues before they negatively affect the customer experience and have more time to innovate.

Key Features:

• Dynatrace continually and instantly recognizes your changing environment.
• Open APIs make it simple to import other data sources from your CI/CD workflow, cloud platforms, and service management tools for even more comprehensive AI processing.
• Without any manual configuration, Davis recognizes entity relationships at startup.

12. Datadog

Datadog uses machine learning to automatically analyze the performance of infrastructure and applications so that engineering teams can be alerted to problems without manually setting up alerts for every potential failure mode.

The Datadog anomaly detection engine detects anomalous error rates in any application or service, high latency for every database or query, network difficulties with cloud providers, and more.

Key Features:

• Whether dashboards and alerts have been set up for a specific application statistic or not, Datadog will nevertheless keep track of it and inform you as soon as a potential problem is found.
• Discover anomalies and outliers that are impossible to find manually.
• Datadog will forecast future metric growth and behavior, accounting for seasonality, and warn engineers of prospective capacity issues or measures that have started to trend strangely compared to past performance.

13. BMC

BMC provides a broad array of tools for mapping, logging, and managing IT infrastructure. It has formed alliances with most major networking and cloud players. For historical and current data, BMC’s open data access strategy employs many data clusters.

BMC’s AIOps solutions integrate machine learning and predictive capabilities into IT operations and DevOps systems for real-time, enterprise-wide observability, insights, and automated remediation.

Key Features:

• Streamline enterprise-wide data sources into a single, actionable view. Utilize predictive analytics powered by artificial intelligence (AI) and machine learning (ML) to swiftly identify operational issues and decrease event noise by up to 90%.
• Events and notifications are based on measurements and are triggered by built-in anomaly detection. 
• Advanced data analysis across infrastructure and applications allows you to cut MTTR by up to 75%.

14. Splunk

Splunk is a premier AIOps platform that provides total visibility of the cloud platform, end-to-end service management, powerful analytics, and predictive management.

Splunk AIOps brings together data from numerous sources and simplifies data analysis as IT operations management becomes more complicated. AI can automatically scan vast volumes of network and machine data to detect patterns, allowing it to identify and prevent current problems.

Key Features: 

• To prevent problems before they impact your customers, use predictive analytics powered by machine learning.
• Employ event correlation to organize warnings into groups and swiftly determine their likely fundamental causes.
• Automated incident response can increase efficiency and provide full-stack service visibility.

15. LogicMonitor

LogicMonitor’s AIOps platform allows businesses to see what’s coming before it happens and utilizes AI and machine learning to provide context, meaningful alerts, reveal patterns, and enable foresight and automation.

With over 2000 pre-configured connectors, it provides comprehensive visibility of on-premise servers, cloud, and network monitoring in a unified platform. It has AI-powered features and an automatic alert mechanism, making it easier to reduce disruptions while fostering innovation and agility with AIOps.

Key Features:

• Learn about upcoming trends to proactively stop problems before they happen.
• To boost MTTR and uncover problem sources more quickly, automatically find correlations between resources.
• Only alerts for problems that develop outside a resource’s operational range using robust anomaly detection.

16. OpsRamp

OpsRamp‘s artificial intelligence for IT operations (AIOps) solution was designed to detect, monitor, manage, and automate the world’s complex hybrid IT settings. 

It gives your team a holistic image of your hybrid infrastructure, allowing them to manage incidents, automate processes, and streamline IT operations.

Key Features: 

• Assist you in condensing and compressing raw alarms into relevant events.
• Simplifies modern IT operations by providing richer and deeper insights across your tool stack.
• With IT process automation, OpsRamp can help you avoid costly service disruptions and handle recurrent events at scale.

17. PagerDuty

PagerDuty is a cloud computing startup based in the United States that is well-known for its SaaS incident response platform. It helps businesses prevent downtime and detect issues and opportunities in real-time by utilizing machine learning and automation. 

We connect data from all your tools to give insights into your IT infrastructure, with 650+ native integrations and the opportunity to develop and adapt workflows using the extensible PagerDuty APIs. All inbound events are automatically normalized into standard fields by our improved Events API v2.

Key Features: 

• Substantially reduces system noise and alert fatigue. PagerDuty automatically groups alerts and eliminates disruptions using a combination of data science approaches and machine learning to filter out up to 98% of noise.
• Enhance situational awareness when determining the core cause of a problem, pertinent surface episodes, and recent developments.
• You may eliminate tedious manual labor by employing nested rules and custom logic to process events in real-time.

18. StackState

StackState speeds up your IT operations by removing barriers between teams and tools. StackState can find, map, and monitor your complete IT ecosystem across teams and tools. StackState can help you uncover a problem’s underlying cause in seconds and prevent issues from affecting your business.

StackState captures and correlates a wide range of information, offering a full-stack view of the operating landscape, with support for a wide range of products and integration into other monitoring or APM systems.

Key Features:

• Synthesize segregated data from several sources, including Kubernetes, observability, and infrastructure monitoring tools, virtualization and cloud platforms, data lakes, applications, and incident management systems.
• Get rid of alert storms and learn where to concentrate repair efforts immediately. Stay away from duplicate instances, phony alarms, and pointless warnings.
• A component’s health state is determined by StackState’s health checks, based on the telemetry and log streams defined for that component.

19. ScienceLogic

ScienceLogic AIOps leverage big data and machine learning to produce predictive results that aid in speedier root-cause analysis (RCA) and shorter mean time to repair (MTTR).

Your ITOps may continuously improve by offering intelligent, actionable insights that promote a higher level of automation and cooperation, saving your organization time and resources.

Key Features:

• Automatically update your incidents with diagnostics data to help you quickly find the root cause.
• Eliminate recurring issues using collected diagnostic data to identify typical faults and automate forensic remediation and repair procedures.
• Using data-driven dashboards and automated workflows, you can quickly assess the impact, hone in on the root cause, and fix events.

20. New Relic

New Relic is a leading provider of AIOps products. It focuses on applied intelligence, which tries to recognize, comprehend, focus, and resolve issues as quickly as possible by reducing noise, reducing pattern discrepancies, and gaining deeper insights.

Get a real-time and detailed look at your network, infrastructure, applications, end-user experience, machine learning models, and more. With robust whole stack analysis tools, teams can analyze all of their telemetries in one spot.

Key Features:

• Automatic alerts based on golden signals like throughput, failures, and latency detect odd changes across all apps, services, and log data—no configuration required.
• Reduce distracting and redundant alerts by up to 80% by automatically grouping alerts and events from any source.
• Intuitive insights into the fundamental cause of every problem help you solve problems faster.

Conclusion

AIOps is a relatively new technology getting recognition within organizations for its propensity for early anomaly detection and solutions. In this post, we’ve compiled a list of the best AIOps tools that may assist you in immediately spotting abnormalities and providing guidance on how to avoid them. 

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Сообщение What Is AIOps? +20 Best Tools in 2023 (Updated) появились сначала на Acure AIOps Platform.