Integrated Management Solution for
Various Cloud Environments

CloudMOA is designed for integrated management of large-scale IT infrastructure, PaaS and MSA services in various multi/hybrid
cloud environments. Anomaly detection using AI (Artificial Intelligence) and multi-dimensional service level performance
monitoring function are added to maximize the efficiency of corporate IT operations.

  • Integrated Monitoring of Real-time Kubernetes-based Multi-cloud Environments

  • Trace Call Relationship between
    MSA-based Application Services

  • Cloud Performance Management
    and Analysis using AI

  • Customizable Dashboard
    and Reporting Functionality


As a cloud-native architecture, CloudMOA provides installation convenience and scalability, and essential functions for large-scale cloud performance management.

  • Multi/Hybrid Cloud Support
    Including AWS, Azure, GCP,
  • 2D/3D Topology View
    Large-scale multi-cluster monitoring
  • Service Monitoring
    in MSA Environment
    Tracing and analyzing complex call relationships between services and
    detailed transaction flow
  • AI-based Anomaly Detection
    Real-time anomaly detection
    by learning from past data
  • AI-based Sparse Log Analysis
    Machine learning-based log analysis
    and sparse log detection
  • Securing Service Layer Visibility
    Real-time resource usage
    monitoring and alarm
  • Intuitive UI/UX
    Performance dashboard for quick recognition of failed containers
  • Container-based Agent Installation
    Automatic installation provides convenience for company-wide system management

Monitoring Views


Topology View

Topology screen provides integrated service monitoring. Users are able to monitor call relationships between services, real-time performance indicators and events of each service.

Infra Overview

Infra Overview

Infra Overview allows you to summarize and monitor key performance indicators for each cluster from the infrastructure point of view. It also provides the distribution status within the cluster, the status of nodes and containers, and of course, the status of top resource usage.

Anomaly Detection

Anomaly Detection

Confidence intervals can be created for various performance indicators through machine learning. Moreover, real-time anomaly detection can be quickly identified.

Resource Monitoring

Resource Monitoring

Provide real-time performance indicators, usage monitoring by resource (Node, Namespace, etc.) and insights for optimal resource allocation and operation management.

Pod / Container


This screen allows you to confirm information for each type of pod/container deployed and operated within the cluster, real-time performance indicators (CPU/Memory/Network Traffic/Disk Usage, etc.) trends and log monitoring.

Analysis Views

Event Summary

Event Summary

With the event and log analysis function by type and workload, it is possible to check and analyze the event occurrence status by entity/date and detailed messages about the event.

Anomaly Detection

Anomaly Detection

With the AI technology, this screen allows you to analyze the anomaly detection status and cause of failure by the time of failure and the desired period.

Service Trace

Service Trace

The service trace feature provides a detailed performance tracing function for application services. Users can analyze the performance trend and transaction details at a desired point in time.

Log Viewer

Log Viewer

Log Viewer allows you to collect and analyze log files of the desired server or pod/container without directly accessing the system in the cloud environment.

Alert History

Alert History

Provide easy confirmation on statistical analysis by fault alarm level and type. Moreover, you can also check and analyze detailed alarm messages with ease.


Customer Cases

  • Bank

    Private PaaS-based Kubernetes Integrated Operating System Monitoring Implementation

    Adoption Background

    With the first-time implementation of a cloud system, there is a growing necessity for integrated monitoring of IaaS, PaaS, and MSA to ensure optimal performance and availability.


    • Implementation of private PaaS-based Openshift operating system monitoring and failure detection.

    • Configuration of integrated failure alarm through health check function linkage including IaaS and PaaS areas.

    • Analyze pod lifetime log and cause analysis through Container Lifecycle function.

    • Securing infra operation efficiency with rapid change detection and insight for auto scale environment.

  • Financial

    Monitoring Multi-public Cloud Integrated Operating System Monitoring.

    Introduction Background

    Necessity for integrated management of various public cloud environments.


    • Provide an integrated dashboard for customer service performance and failures in a distributed cloud environment.

    • Securing operational convenience through integrated monitoring of multi and hybrid clusters.

    • Performance improvement effect through real-time active transaction monitoring of MSA-based applications.

    • Effect of IT operation cost reduction through efficient operation and management of cloud infrastructure.

  • Private Cloud
    (IDC Center)

    Establishing Integrated Monitoring

    Adoption Background

    Necessity of efficient integrated monitoring of cloud systems in an IDC environment that provides SaaS services.


    • Establishment of HCI (Hyper Converged Infrastructure) platform environment-linked monitoring for bare metal

    • Performance improvement effect with real-time detailed trace monitoring for MSA applications.

    • DevOps support by providing cluster integration and individual monitoring for each development and operation

    • Integrated operation and management efficiency of the IDC center via 3D integrated view.

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