Unchaining Microservice Chains : Machine Learning Driven Optimization in Cloud Native Systems

Sammanfattning: As the cloud native landscape flourishes, microservices emerge as a central pillar for contemporary software development, enabling agility, resilience, and scalability in modern computing environments. While these modular services promise opportunities, particularly in the transformative ecosystem of 5G and beyond, they also introduce a myriad of complexities. Notably, the migration from hardware-centric to software-defined environments, culminating in Virtual Network Functions (VNF), has facilitated dynamic deployments across cloud data centers. In this transition, VNFs are often deployed within cloud native environments as independent services, mirroring the microservices model. However, the advantage of flexibility in cloud native systems is shadowed by bottlenecks in computational resource allocation, sub-optimal service chain placements, and the perpetual quest for performance enhancement. Addressing these concerns is not just pivotal but indispensable for harnessing the true potential of microservice chains.In this thesis, the inherent challenges presented by cloud native microservice chains are addressed through the development and application of various tools and methodologies. The NFV-Inspector is introduced as a foundational tool, employing a systematic approach to profile and analyze Virtual Network Functions, subsequently extracting essential system KPIs essential for further modeling. Subsequent research introduced a Machine Learning (ML) based SLA-Aware resource recommendation system for cloud native functions. This system leveraged regression modeling techniques to correlate key performance metrics. Following this, PerfSim is proposed as a performance simulation tool designed specifically for cloud native computing environments, aiming to improve the accuracy of microservice chain simulations. Further research is conducted on Service Function Chain (SFC) Placement, emphasizing the equilibrium between cost-efficiency and latency optimization. The thesis concludes by integrating Deep Learning (DL) techniques for service chain optimization, employing both Graph Attention Networks (GAT) and Deep Q-Learning (DQN), highlighting the intersection of DL techniques and SFC performance optimization.

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