Optimizing Microservice Performance: A Software Configuration Management Changes and Time Series Analysis Framework

Abstract

Modern microservice architectures demand agile configuration management to ensure optimal performance and reliability. However, the dynamic and distributed nature of these systems complicates the assessment of how configuration changes influence key performance indicators (KPIs) such as CPU utilization, memory consumption, response time, and error rates. In this study, we propose a time-series-driven approach for analyzing and optimizing the impact of configuration modifications in large-scale enterprise environments. Initially, we quantify the causal relationships between critical configuration parameters (cache size, thread pool size, and release complexity) and performance metrics using correlation and regression analyses. Subsequently, we employ time series modeling techniques (Prophet, ARIMA, and LSTM-based Autoencoder) to detect anomalies stemming from misconfigurations and traffic surges. To further enhance system performance, we integrate Bayesian Optimization and reinforcement learning methods for automated parameter tuning, demonstrating up to a 20–25% reduction in response times under realistic workloads. We also investigate deployment strategies—such as blue- green and canary releases and their interplay with rollback processes. Our findings underscore the significance of data-driven configuration management for microservices, offering actionable insights into achieving higher system stability, lower operational costs, and rapid recovery from performance anomalies.

Keywords

• Software Configuration Management (SCM)
• Time-Series Analysis
• Performance Optimization
• Anomaly Detection
• Deep Learning

Yazılım Konfigürasyon Yönetimi Değişiklikleri ve Mikro Servis Performansını Optimize Etmek: Zaman Serisi Analizi Çerçevesi

Abstract

Modern mikro servis mimarileri, optimum performans ve güvenilirlik sağlamak için çevik konfigürasyon yönetimi gerektirir. Ancak bu sistemlerin dinamik ve dağıtık yapısı, konfigürasyon değişikliklerinin CPU kullanımı, bellek tüketimi, yanıt süresi ve hata oranları gibi temel performans göstergelerini (KPI) nasıl etkilediğinin değerlendirilmesini zorlaştırmaktadır. Bu çalışmada, büyük ölçekli kurumsal ortamlarda konfigürasyon değişikliklerinin etkisini analiz etmek ve optimize etmek için zaman serisi odaklı bir yaklaşım öneriyoruz. İlk olarak, korelasyon ve regresyon analizlerini kullanarak kritik yapılandırma parametreleri (önbellek boyutu, iş parçacığı havuzu boyutu ve sürüm karmaşıklığı) ile performans ölçümleri arasındaki nedensel ilişkileri ölçüyoruz. Daha sonra, yanlış konfigürasyonlardan ve trafik dalgalanmalarından kaynaklanan anormallikleri tespit etmek için zaman serisi modelleme tekniklerini (Prophet, ARIMA ve LSTM tabanlı Autoencoder) kullanıyoruz. Sistem performansını daha da artırmak için, otomatik parametre ayarı için Bayesian Optimizasyonu ve takviyeli öğrenme yöntemlerini entegre ediyoruz ve gerçekçi iş yükleri altında yanıt sürelerinde %20-25'e varan bir azalma gösteriyoruz. Ayrıca mavi-yeşil ve kanarya sürüm dağıtımı gibi dağıtım stratejilerini ve bunların geri alma süreçleriyle etkileşimini de araştırıyoruz. Bulgularımız, mikro servisler için veri odaklı konfigürasyon yönetiminin önemini vurgulamakta ve daha yüksek sistem kararlılığı, daha düşük operasyonel maliyetler ve performans anormalliklerinden hızlı ve efektif bir şekilde kurtarmak için içgsörüler ve öneriler sunmaktadır.

Keywords

• Yazılım Konfigürasyon Yönetimi
• Zaman Serisi Analizi
• Performans Optimizasyonu
• Anomali Tespiti
• Derin Öğrenme

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