One the world’s largest automobile manufacturers has optimized their middleware platform’s response time by several folds. This world renowned enterprise optimized their garbage collection performance using our GCeasy tool and was able to reduce their overall application’s response time by a stunning 49.46%
When you optimize Garbage collection performance, you are not only improving the Garbage collection pause time, but you are also improving the overall application’s response time. Just by modifying the garbage collection settings without refactoring a single line of code, this team improved their overall application’s response time significantly. The table below summarizes the overall response time improvement they achieved with each Garbage Collection setting they made:
| Avg Response Time (secs) | Transactions > 25 sec (%) | |
| Baseline | 1.88 | 0.7 |
| GC settings iteration #2 | 1.36 | 0.12 |
| GC settings iteration #3 | 1.7 | 0.11 |
| GC settings iteration #4 | 1.48 | 0.08 |
| GC settings iteration #5 | 2.045 | 0.14 |
| GC settings iteration #6 | 1.087 | 0.24 |
| GC settings iteration #7 | 1.03 | 0.14 |
| GC settings iteration #8 | 0.95 | 0.31 |
When they started the GC tuning exercise, this automobile application’s overall response time was 1.88 seconds. As they optimized Garbage Collection performance with different settings, on iteration #8, the automobile manufacturer was able to improve the overall response time to 0.95 seconds. i.e., 49.46% improvement in the response time. Similarly, percentages of transactions taking more than 25 seconds dropped from 0.7% to 0.31%, i.e., 55% improvement. This is a significant improvement to achieve without modifying a single line of code.
All other forms of response time improvement will require an infrastructure, architectural or code-level change. All those changes are expensive. Even if you embark on making those costly changes, there is no guarantee of the application’s response time improvement.
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