Products for AI Ops for Middleware.
AIOps provides an understanding of the importance of the relationships between different flows of data.
meshIQ AI Ops for Middleware.
AIOps is an area of technology that is developing rapidly and is generally accepted to mean “using machine learning to contextualize large amounts of data”. We make use of the inherent knowledge built into the configuration of messaging middleware systems to deliver AIOps. The meshIQ platform delivers AIOps today for many large enterprises, and it is being used for both operational management and the delivery of regulatory compliance.
Ingestion.
Source:
- Messaging Middleware Platforms
- Log Files
- Data Streams
- Any form of Machine Data
Solution.
meshIQ provides a broad and deep range of methods for ingesting data.
We maintain a GitHub repository of open-sourced, supported connectors that use many methods of connecting to machine data sources. We make use of both agents and agentless methods to fit all needs.
Infrastructure, platforms and applications across datacenters and clouds can be ingested.
Topology.
Presenting a model of how the data flows through the entire system, how it all connects.
Each form of messaging middleware provides a map of the relationship between the different components of the application stack. By following the message pathway each “transaction” takes through the environment, meshIQ AIOps can present the user’s experience as a topology.
Correlation.
Mapping the data against time to see what happened, when.
meshIQ AIOps then presents the performance data that is available through each system that a user’s request passes through. Showing you the time it took for a request to be serviced.
Machine learning.
Identifying signals in the data and understanding what they mean in the context of the business.
meshIQ AIOps makes use of Machine Learning Artificial Intelligence (ML AI) to compare the topology of a user’s experience to the historical record of similar requests, to identify the subtle, early indicators of a performance anomaly.
ML AI.
We make use of the inherent knowledge built into the configuration of messaging middleware systems to deliver AIOps. Instead of trying to recreate complex algorithms to describe the business’s topology, we use the very systems that you have already spent untold blood and gold configuring. By reading the configuration information from across your many messaging middleware environments, and even the contents of the messages themselves, we are able to visualize the entire topology of your enterprise application stack, exactly as your users’ transactions see it. And overlay onto this topology all the data you are already collecting to describe the flow of your business.
Then we can compare the historical record of transactions to each new transaction that takes place, allowing any deviance to be recognized before any impact is felt by the user. Using machine learning artificial intelligence (ML AI), we can alert operations staff early enough (predictively or proactively) that remediation can be performed before an event becomes an issue, and using ML AI, we create automation to perform these tasks based on alerts or user requests.
Remediation.
Understanding what actions must be taken to fix issues, and how these can be automated.
Using a mix of various machine learning artificial intelligence (ML AI) algorithms, meshIQ AIOps can identify the signals associated with complex interactions that indicate a deviation from normal behavior, while also identifying and ignoring false positives. These signals can then be used to alert operators or initiate automated processes to mitigate the potential impact of an event.
Actions taken once to remediate a predicted issue can then be included in future automations driven by ML AI, as the system learns and improves.
