JUNO – INTELLIGENT CLOUD MANAGEMENT PLATFORM

Leverage innovation in artificial intelligence and automation to get the most out of cloud investments.

AI and Automation driven Multi- Cloud Management


In most organizations, it is difficult to collect and aggregate cloud management data into a single pane of glass. RTS Juno delivers an end-to-end integration of multiple data streams which enables RTS to leverage AI and ML. In addition, this allows us to automate and lower the overall time to resolution and provide a better experience.

RTS Juno leverages a combination of out-of-the-box toolsets and cloud native data aggregation to deliver a unified experience at the monitoring layer. This allows us to integrate data from the network layer, cloud infrastructure, application, and security layer into one data plane.


For performance and availability, we collect metrics, logs, and traces and use this for analyzing and predicting within our integrated platform.

In addition, security context is embedded in every aspect of our platform. We collect security audit logs into the analytics platform to analyze information as we review performance and availability.
Juno simplifies the process of multi-cloud management and enables traditional IT operations to transform into DevOps and Site Reliability Engineering (SRE)-based operating models.


Key transformation offerings:


  • Simplify end-to-end observability
  • Automate every task that is non ambiguous
  • Leverage AI to resolve ambiguity
  • Deliver transparent communication
  • Receive contextual end-to-end analytics
  • Simplify end-to-end observability

Juno simplifies observability across the entire stack in a multi-cloud environment. It currently supports AWS, Azure, Google Cloud, Oracle Cloud Infrastructure, and IBM Cloud. Juno brings all aspects of metrics, logs, traces, synthetic checks, and security information into one common unified data plane.

Automate every task that is non-ambiguous

Juno also provides an intelligent automation layer that enables ITOps to automate most repeatable tasks such as patching and vulnerability remediations. This increases overall availability and delivers a better experience.

Leverage AI to resolve ambiguity

Juno integrates a massively scalable data pipeline architecture that enables the deployment of ML models on cloud operations data. This allows the creation of automated decision-making on IT Ops data enabling quicker isolation and resolution of incidents.

Juno is an integral part of how RTS delivers multi-cloud management across the entire application stack

Platform Features


Metrics

Metrics information such as average CPU utilization, highest CPU utilization, memory utilization, and more, per host. This time series data can then be aggregated to provide insight for further action.

Logs

Cloud log activity displays log consumption and audit logs. Log information can be analyzed and monitored to derive alerts so further action can be taken manually or via automation.


Synthetic Monitoring

Synthetic monitoring is critical for service platforms because it provides:

  • Response times
  • Performance indicators
  • Availability
  • Latency
  • Continuous measurements to ensure defined service level objectives are met

Additional event monitors can also be configured to ensure the right teams are notified and automated processes can be triggered. RTS measures the performance of all connectivity, including for external databases or data sources.

Incident and Change Management

For service management, we use ServiceNow to obtain information such as:

  • Incidents that are open, resolved, and in progress
  • Tickets that have been opened within the last 24 hours
  • The number of incidents over a period of time

These metrics allow us to know precisely when there are critical issues. This allows for reduced response time, prompt resolution, and effective incident and change management.


 

AI Operations

With the use of metrics, logs, traces, and ticketing data, we utilize machine learning to analyze data that is collected and to predict outages or performance issues before they happen.