How to reduce CPU consumption caused by Garbage Collection?

All the modern programming languages such as Golang, nodejs, Java, .net, python,…. does automatic garbage collection to remove unreferenced objects from memory. While this automatic garbage collection provides convenience to developers, it can come at a cost: excessive CPU consumption. The constant cycles devoted to garbage collection causes couple of side effects:

a. Degradation in application’s performance: Since CPU cycles are constantly channeled to Garbage Collection, overall application’s performance will get impacted.

b. Increase Cloud hosting cost: It increases your cloud hosting cost. In efforts to reduce cloud hosting cost, recently Uber tuned their Garbage Collection to reduce CPU utilization.

In this article, we delve into five effective strategies that can help alleviate this concern, enabling developers to optimize application performance and mitigate the impact on hosting expenses. 

Before you learn the strategies to reduce CPU consumption caused by the Garbage Collector, you need to understand how much CPU is consumed by the Garbage Collector. Here is an article that highlights how to determine the amount of CPU consumed due to the Garbage Collector in your application.

5 strategies to reduce Garbage Collector’s CPU Consumption:

1. GC Log Analysis & Tuning:

One effective strategy to reduce CPU consumption caused by automatic garbage collection is to analyze the GC logs generated by the JVM and fine-tune the garbage collection process accordingly. Tools such as GCeasy, yCrash can analyze your GC logs and help you gain insights into the behavior of the garbage collector and identify optimization opportunities.

By studying the GC logs, you can identify patterns like frequent full garbage collection cycles or long pause times. This information serves as a guide for tuning the garbage collection parameters to improve CPU utilization. Additionally, many garbage collectors provide specific JVM arguments that allow you to control their behavior. These arguments include settings for heap size, the young-to-old generation ratio, garbage collection thread count, and more.

Experimenting with different combinations of these JVM arguments, enables you to find the optimal configuration for your application. Case studies highlighted here showcase the success of effective GC tuning in real-world scenarios.

2. Switch GC algorithms

Another valuable strategy to reduce CPU consumption caused by automatic garbage collection is to evaluate and switch between different garbage collection (GC) algorithms available in your programming language of choice. For example, in Java (openJDK), there are six major GC algorithms: Serial, Parallel, Concurrent Mark & Sweep, G1, Z GC, and Shenandoah.

Each GC algorithm has its own performance characteristics, including CPU consumption and pause times, depending on your application’s workload. By conducting a thorough analysis and understanding the specific requirements of your application, you can identify an alternative GC algorithm that may provide a significant reduction in CPU consumption.Here is a case study which talks about CPU consumption of each GC algorithm. By switching to a more suitable GC algorithm, you can potentially alleviate the burden on CPU resources and improve overall application performance.

3. Minimize Object Creation Rate:

A key aspect contributing to CPU consumption by automatic garbage collection is the creation of excessive objects in your application. Modern applications often generate a significant number of objects due to inefficient programming practices. To mitigate this, it is essential to optimize your code and reduce unnecessary object creation. Consider the following strategies:

Memory Profiling: Employ memory profiling tools such as HeapHero, yCrash, or  YourKit to identify areas in your code that generate excessive objects. By analyzing memory usage patterns, you can pinpoint code segments that require optimization.

Object Pooling: Implement object pooling techniques to reuse objects instead of creating new ones. This approach can significantly reduce the overhead of object allocation and deallocation.

Immutable Objects: Utilize immutable objects whenever possible. Immutable objects do not require modifications and can be safely shared, eliminating the need for frequent object creation.

StringBuilder Usage: In scenarios where string concatenation is required, utilize StringBuilder or similar efficient string manipulation mechanisms. This helps avoid the creation of multiple intermediate string objects.

Collection Capacity: Set appropriate initial capacities for collections to avoid frequent resizing, which can result in unnecessary object creations.

Remember, optimizing object creation should be done carefully to maintain code readability and maintainability. A balanced approach that targets critical sections of your codebase yields the best results in reducing CPU consumption caused by automatic garbage collection.

4. Change Heap size

Modifying the heap size can have a significant impact on CPU consumption caused by garbage collection. An inappropriate heap size can lead to frequent garbage collection cycles or longer collection times, resulting in increased CPU utilization.

Consider the following strategies to optimize the heap size:

Increase Heap Size: If your application frequently experiences garbage collection pauses or high CPU usage, increasing the heap size can help. A larger heap allows more objects to reside in memory before triggering garbage collection, reducing the frequency of collection cycles.

Decrease Heap Size: Conversely, if your application has a low memory footprint and experiences infrequent garbage collection cycles, reducing the heap size can be beneficial. A smaller heap requires less time for garbage collection, resulting in reduced CPU consumption.

Heap Ergonomics: Some JVMs offer automatic heap sizing algorithms, known as heap ergonomics. These algorithms dynamically adjust the heap size based on the application’s behavior. Experiment with enabling heap ergonomics to allow the JVM to optimize the heap size automatically.

Adjust the heap size incrementally and monitor the impact on garbage collection behavior and CPU utilization. Find the balance that best suits your application’s memory requirements and minimizes CPU consumption.

5. Scale Container/EC2 instances

Increasing the number of container instances or EC2 instances running your application can be a strategy to mitigate CPU consumption caused by garbage collection. By distributing the workload across multiple instances, you can reduce the impact of garbage collection on individual instances, thereby improving overall performance. Consider the following points when scaling instances:

Load Distribution: Allocate traffic evenly across multiple instances to prevent overburdening a single instance. Load balancing mechanisms, such as round-robin or weighted load balancing, can help achieve this distribution.

Horizontal Scaling: Instead of scaling vertically by adding more resources to a single instance, opt for horizontal scaling by adding more instances. This approach allows for better utilization of resources and reduces the strain on individual instances.

Monitoring and Autoscaling: Implement monitoring systems that track CPU utilization and other relevant metrics. Use auto scaling features provided by cloud platforms to automatically add or remove instances based on predefined thresholds. This ensures that the number of instances dynamically adjusts to the workload, optimizing resource allocation and minimizing CPU consumption.

It’s important to note that scaling instances incurs additional hosting costs. Consider the balance between performance improvements and associated expenses when determining the optimal number of instances.


Reducing CPU consumption caused by automatic garbage collection is crucial for improving application performance and minimizing hosting costs. However, it is important to experiment and monitor the implementation of the strategies discussed before deploying them in a production environment.

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