Is your ServiceNow integration experiencing slowdowns? 🤔 Memory fragmentation could be a factor. But don’t worry! Our guide explores how understanding JVM Memory Fragmentation can aid your ServiceNow Integration. By leveraging the yCrash tool, we can optimize memory utilization and ensure smoother operations for enhanced performance.
How can analyzing JVM Memory Fragmentation help?
JVM memory fragmentation can have several impacts on the performance and stability of an application:
Increased Garbage Collection Overhead
Fragmentation can lead to inefficient memory management, causing the garbage collector to work harder to reclaim fragmented memory blocks. This increased overhead can result in longer garbage collection pauses, leading to degraded application performance and responsiveness.
Memory Allocation Failures
Fragmentation can also lead to memory allocation failures, where the JVM is unable to allocate contiguous blocks of memory for new objects or data structures. This can result in OutOfMemoryError or heap space exhaustion, causing the application to crash or become unstable.
Reduced Memory Utilization
Fragmentation can reduce the effective use of available memory, as fragmented memory blocks may not be efficiently utilized in an unformatted state.. This can result in reduced overall memory capacity for the application.
Increased Memory Footprint
In some cases, memory fragmentation can lead to an increased memory footprint for the application, as fragmented memory blocks may need to be retained for longer periods or may require additional memory overhead for management purposes. This can lead to higher memory usage and increased resource consumption.
Performance Degradation
Overall, memory fragmentation can contribute to performance degradation and instability in the application. It can lead to longer garbage collection pauses, increased memory allocation failures, and reduced memory utilization efficiency. Analyzing and addressing memory fragmentation issues is therefore important for maintaining the performance and stability of JVM-based applications.
Simulating JVM Memory Fragmentation in MID Server
The Java program given below simulates memory fragmentation on any machine/container in which it’s launched:
import java.util.ArrayList;
import java.util.List;
import java.util.Random;
public class MemoryFragmentationExample {
private static final Random random = new Random();
private static class BigObject {
private final byte[] data;
public BigObject() {
// Size varies from 1MB to 2MB.
data = new byte[1024 * 1024 + random.nextInt(1024 * 1024)];
}
}
public static void induceMemoryPressure() {
List<BigObject> objects = new ArrayList<>();
while (true) {
objects.add(new BigObject()); // Allocate a new BigObject that takes significant memory.
// To simulate inefficient memory turnover, occasionally remove and add objects.
if (objects.size() > 100) {
objects.subList(0, 50).clear();
}
try {
Thread.sleep(50); // Simulating work and giving time for GC to happen.
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
break;
}
}
}
public static void main(String[] args) {
induceMemoryPressure();
}
}
The above Memory Fragmentation Example Java program simulates high memory usage by creating and managing a list of BigObject instances, each containing a sizable byte array (1 to 2 MB). In the induceMemoryPressure() method, it continuously adds new BigObject instances to the list, increasing heap usage. To mimic inefficient memory turnover, once the list reaches more than 100 objects, it removes 50 objects from the start of the list to free up memory, which could lead to heap fragmentation as it creates gaps in the memory allocation. The program includes a brief pause of 50 milliseconds in each loop iteration to simulate processing time and provides the garbage collector with an opportunity to run. Running indefinitely, this process places constant pressure on the JVM’s garbage collector, which may result in increased GC pause times and potential simulation of fragmented memory conditions.
JVM Memory Fragmentation’s Impact In ServiceNow MID Server
Let’s create a JAR (Java Archive) file from this program, you can create the JAR file by issuing the following command:
javac MemoryFragmentationExample.java
jar cf MemoryFragmentationExample.jar MemoryFragmentationExample.classOnce a JAR file is created, let’s upload and run this program in the ServiceNow MID Server as documented in the MID Server setup guide. This guide provides a detailed walkthrough on how to run a custom Java application in the ServiceNow MID Server infrastructure. It walks through the following steps:
- Creating a ServiceNow application
- Installing MID Server on AWS EC2 instance
- Configuring MID Server
- Installing Java application with in the ServiceNow MID Server
- Running Java application from the ServiceNow MID server
We strongly encourage you to check out the guide if you are not sure on how to run custom Java applications in ServiceNow MID server infrastructure.
Analyzing Memory Fragmentation on the ServiceNow MID Server.
yCrash serves as a comprehensive monitoring tool tailored to pinpoint performance bottlenecks and offer actionable insights within the ServiceNow MID Server environment. ServiceNow organizations leverage yCrash extensively for diagnosing and addressing performance issues.
When analyzing the impact of memory fragmentation within ServiceNow’s MID Server, yCrash diligently monitors the micro-metrics of the environment. It adeptly identifies memory fragmentation issues and assesses their impact on system performance. Through detailed reports on the dashboard, yCrash provides valuable insights into the implications of memory fragmentation, facilitating effective optimization strategies.
The recommendations address a GC pause time issue caused by ‘G1GC Humongous Allocation’ events, which occur when allocations exceed 50% of the G1GC region size. These allocations can lead to performance problems due to unused space between humongous objects, potentially causing heap fragmentation. In older JVM versions, humongous regions were only reclaimed during full GC events, but newer JVMs handle this in the cleanup phase. To mitigate these issues, the solution suggests increasing the G1GC region size using the ‘-XX:G1HeapRegionSize’ property, although this change requires careful testing due to its sensitivity. Additionally, if using the G1GC algorithm on Java 8 update 20 or above, enabling string deduplication with ‘-XX:+UseStringDeduplication’ may improve application performance. These recommendations aim to optimize G1GC performance and manage memory fragmentation effectively.
This section provides key performance indicators (KPIs) related to the application’s throughput and latency, specifically focusing on garbage collection (GC) pause times.
Throughput
The throughput indicates the percentage of time that the application spends executing useful work compared to total time, with a reported value of 99.779%. A high throughput percentage indicates efficient utilization of resources and minimal time spent on non-productive tasks.
Latency
Average Pause GC Time: This metric measures the average duration of GC pauses, with a reported value of 2.33 milliseconds. Lower average pause times are desirable as they indicate shorter interruptions to application execution.
Maximum Pause GC Time
This metric represents the longest duration of GC pauses, with a reported value of 7.58 milliseconds. It signifies the maximum delay experienced by the application during GC activity.
GC Pause Duration Time Range
This section breaks down the distribution of GC pause times into different duration ranges (in milliseconds).
For each duration range, the table provides the number of GC events and their percentage relative to the total number of GC events. For example, there were 31 GC events (30.39% of total) with a pause duration between 0 and 1 millisecond, indicating relatively short pauses.
The distribution helps identify patterns in GC pause times and assess the impact of different durations on application performance.
Overall, these KPIs offer insights into the application’s efficiency in terms of throughput and latency, particularly regarding GC pause times, which are crucial for understanding and optimizing performance.
This section provides statistics on the different phases of the G1GC garbage collection (GC) algorithm:
- Young GC: Represents the collection of garbage from the young generation.
- Total Time: 416 ms
- Avg Time: 3.59 ms (average duration of each Young GC operation)
- Std Dev Time: 3.47 ms (standard deviation of Young GC operation durations)
- Min Time: 0.0860 ms (shortest observed duration)
- Max Time: 15.4 ms (longest observed duration)
- Interval Time: 916 ms (time between successive Young GC operations)
- Count: 116 (total number of Young GC operations)
- Concurrent Marking, Remark, Cleanup: These phases occur concurrently with application execution.
Similar metrics provided for each phase, including total time, average time, standard deviation, minimum time, maximum time, interval time, and count. You can see the full report here.
Conclusion
In summary, yCrash’s analysis of JVM memory fragmentation provides valuable recommendations and insights to optimize memory usage. The integration with the ServiceNow MID server streamlines incident reporting and resolution processes, enhancing IT service management efficiency. This integration is particularly beneficial for addressing memory fragmentation challenges within large applications integrated with the ServiceNow MID Server, ultimately contributing to a more resilient and efficient IT environment. If you want to diagnose performance problems in your ServiceNow deployment using yCrash you may register here.
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