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Monitor your hybrid standalone infrastructure through comprehensive performance dashboards that provide real-time visibility into server health, resource utilization, and system performance across your distributed runtime environment.
Runtime Manager provides comprehensive infrastructure monitoring dashboards for your hybrid standalone environment, so you can:
Monitor real-time server performance metrics and resource utilization
Analyze historical trends for capacity planning and optimization
Track JVM performance including memory usage, garbage collection, and thread activity
View aggregated metrics across server groups and clusters
Identify performance bottlenecks and system health issues
Make data-driven decisions for infrastructure scaling and maintenance
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To view infrastructure dashboards, you must have the Runtime Manager Read Servers permission for the target servers. Anypoint Platform Private Cloud Edition doesn’t support the Anypoint Monitoring dashboard feature. |
Runtime Manager provides these infrastructure monitoring dashboards:
Server Monitoring Dashboard
Individual server performance metrics including CPU, memory, JVM heap usage, garbage collection, and detailed memory breakdown
Server Group and Cluster Monitoring Dashboard
Aggregated performance metrics for servers organized in groups or clusters with comparative analysis capabilities
Monitor individual server performance through comprehensive dashboards that provide real-time visibility into system resources, JVM metrics, and operational health indicators.
The server monitoring dashboard provides detailed insights into your hybrid standalone server performance, including:
System resource utilization (CPU, memory, disk I/O)
JVM heap and non-heap memory allocation and usage
Garbage collection performance and frequency
Thread management and execution metrics
Memory breakdown by allocation pools (Eden, Survivor, Old Generation)
Class loading and code cache utilization
To access the server monitoring dashboard:
In Runtime Manager, click Servers.
Click the name of the server to monitor.
The server dashboard displays with comprehensive performance metrics:
You can also navigate to individual server dashboards from the server group or cluster dashboard by clicking individual server names.
The server monitoring dashboard provides two complementary tab views for comprehensive performance analysis:
Overview
High-level system metrics including CPU usage, heap memory, and overall server health
Breakdown
Detailed JVM memory analysis with individual allocation pools and garbage collection metrics
Monitor performance across different time periods using the time range selector:
Available time ranges include:
3 Hours: Real-time monitoring with minute-level granularity
24 Hours: Recent performance with 8-minute averaging
7 Days: Weekly trends with hourly data points
2 Weeks: Historical analysis with 2-hour averaging
Enhance your analysis with interactive dashboard features:
Display numerical values and timestamps by hovering over graph points
When multiple applications run on the server, filter performance data by specific applications using the application checkboxes
The memory usage graphs on the dashboards track these values:
Usage
Current memory consumption (MB) - indicates active memory allocation
Total
Maximum available memory (MB) - represents the upper limit for memory allocation
Committed
Memory guaranteed by the JVM (MB) - always greater than or equal to used memory
| If the JVM attempts to increase used memory beyond committed memory, allocation fails even when total memory is available. Monitor committed vs. used memory ratios for optimal performance. |
The Overview tab provides essential system health metrics for rapid assessment of server performance and resource utilization.
Heap Memory
Monitor JVM heap memory allocation and usage patterns to optimize application performance:
Heap Memory Usage: Current heap allocation in MB
Total Heap Memory: Maximum heap size configured for the JVM
Heap Committed Memory: Memory guaranteed by the JVM for heap operations
Use heap memory metrics to:
Identify memory leaks and excessive allocation patterns
Optimize JVM heap size configuration (-Xmx and -Xms parameters)
Plan capacity for application deployment and scaling
| If heap usage consistently approaches total memory, consider increasing heap size or analyzing application memory usage patterns. See the Breakdown Tab for detailed memory pool analysis. |
CPU
Track processor utilization and system load to ensure optimal server performance:
| CPU metrics are not available on Windows environments due to platform-specific monitoring constraints. |
CPU Usage
Percentage of processing capacity utilized
Available Processors
Number of CPU cores available to the JVM
Load Average
Average number of processes waiting for CPU execution
Load Average Interpretation:
Load < CPU Count: System has spare processing capacity
Load = CPU Count: System is optimally utilized
Load > CPU Count: System is experiencing processing pressure and potential performance degradation
The Breakdown tab provides detailed JVM memory analysis with granular metrics for each memory pool, enabling precise performance tuning and troubleshooting.
Understanding JVM memory organization is essential for effective performance monitoring:
Heap Memory Pools
Non-Heap Memory Areas
Monitor the primary object allocation area for young generation garbage collection optimization:
Eden space metrics help you:
Optimize object allocation patterns
Tune young generation garbage collection frequency
Identify excessive short-term object creation
Track intermediate object storage for garbage collection lifecycle analysis:
Survivor space monitoring enables:
Analysis of object promotion patterns to old generation
Optimization of survivor space sizing for GC efficiency
Detection of objects with intermediate lifespans
Monitor long-term object storage and major garbage collection patterns:
Old generation metrics provide insight into:
Long-lived object accumulation patterns
Major garbage collection frequency and impact
Memory leak detection through trend analysis
Monitor JIT compiler performance and native code storage:
Code cache metrics help optimize:
JIT compilation effectiveness
Native code storage allocation
Application startup and runtime performance
Track compressed ordinary object pointer (OOP) storage efficiency:
This metric is available only when using JDK 8+.
Monitor compressed class space for:
Memory efficiency of compressed OOPs
Class metadata storage optimization
JVM memory footprint reduction
Monitor class metadata storage using native memory allocation:
This metric is available only when using JDK 8+.
Key Considerations:
256 MB metaspace allocation
Uses system memory outside JVM heap
Grows automatically based on class loading requirements
Metaspace monitoring helps:
Prevent class metadata memory exhaustion
Optimize class loading and unloading patterns
Manage application deployment in memory-constrained environments
Monitor JVM runtime performance and system behavior through these key performance indicators:
Track JVM thread usage to identify concurrency issues and optimize thread pool configuration:
Monitor thread count to:
Detect thread leaks and excessive thread creation
Optimize application concurrency settings
Identify potential deadlock conditions
Monitor garbage collection efficiency across different heap generations:
Garbage Collection Areas:
Young Generation: Collection frequency for short-lived objects in Eden and Survivor spaces
Old Generation: Collection patterns for long-lived objects requiring more intensive cleanup
Use GC metrics to:
Optimize heap size allocation between generations
Identify memory allocation patterns causing excessive GC pressure
Tune garbage collection algorithms for application performance
Track dynamic class loading across all applications in the JVM:
Monitor class loading to:
Identify classloader memory leaks
Optimize application startup performance
Analyze dynamic code loading patterns
Monitor distributed infrastructure performance through aggregated dashboards that provide unified visibility across multiple servers organized in groups or clusters.
Server group and cluster monitoring enables centralized performance analysis across your distributed hybrid standalone infrastructure, providing:
Combined view of CPU, memory, and heap utilization across all servers
Side-by-side performance comparison between individual servers
Load distribution patterns across server infrastructure
Collective resource utilization for infrastructure planning
The group and cluster dashboard displays essential aggregated infrastructure metrics:
CPU Usage: Aggregate processing capacity utilization across all servers (percentage)
Memory Usage: Combined memory consumption across the infrastructure (MB)
Heap Total: Aggregate JVM heap allocation for all servers (MB)
Individual Server Breakdown: Per-server performance within the group context
Aggregated metrics appear as unified plot lines representing the collective performance of selected servers.
To monitor aggregated performance metrics for your server infrastructure:
In Runtime Manager, click Servers.
Click the server group or cluster name to display the aggregated dashboard.
Select specific servers from the list to display comparative metrics in the details pane.
Time Range Selection
Monitor performance trends across different time periods:
Choose from available time ranges: * 3 Hours: Real-time infrastructure monitoring * 24 Hours: Recent performance analysis * 7 Days: Weekly infrastructure trends * 2 Weeks: Historical capacity analysis
Server-Focused View
Access server-specific performance data within the group context:
From the group dashboard, navigate to individual server dashboards by clicking server names to access detailed performance metrics and troubleshooting information.
If monitoring metrics don’t appear immediately after configuring Anypoint Monitoring for servers in a server group, follow this systematic installation process:
In Anypoint Platform go to Runtime Manager > Servers > Add Server to create an Anypoint Runtime Manager token.
Execute the command provided in the Add Server screen.
In Anypoint Platform go to Runtime Manager > Servers > Create Group to create the server group.
Start Mule for all the servers to be added to the server group by using the command ./mule start from the $MULE_HOME/bin directory.
In Anypoint Platform go to Runtime Manager > Servers, click the server group name, and Add Server to add all the servers to the group.
Deploy an API to the server group.
Stop any running filebeat process in all the servers of the server group and run the ./install command from the $MULE_HOME/am/bin directory in all the servers of the server group.
In Anypoint Platform go to Runtime Manager > Servers, click the server group name, and restart all the servers in the server group via Actions > Restart.
Run ./setup from the $MULE_HOME/am/bin for all the servers in the server group.
If after this step the metrics don’t show up, repeat the last two steps.