Performance Evaluation and Testing Optimization Techniques for Cloud-Native Systems in Edge-Cloud Continuum
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I2P114Keywords:
Cloud-Native Systems, Edge Computing, Performance Evaluation, Testing Optimization, Microservices, Kubernetes, Edge-Cloud Continuum, Distributed SystemsAbstract
The concept of cloud-native systems has transformed the manner in which application is deployed today utilizing microservices, containerization, and orchestration frameworks to provide scalability, resilience and agility. As the edge-cloud continuum rapidly emerges, with the computational resources being spread across centralized cloud data centers and decentralized edge nodes, new issues emerge in performance evaluation and testing optimization. The paper provides a detailed analysis of the performance assessment strategies and testing optimization tricks that are designed to suit cloud-native systems working in a heterogeneous edge-cloud setup. The edge-cloud continuum presents latency, bandwidth, resource availability, and workload distribution variability dynamically, thus requiring conventional performance testing methodologies to be inadequate. The study examines the innovative testing systems that integrate real-time telemetry, distributed tracing, and predictive analytics powered by AI in order to improve performance visibility. The paper also highlights the significance of workload modelling, chaos engineering and adaptive testing pipelines in the reliability of systems in different operational contexts. The performance metrics, including response time, throughput, resource utilization, and fault tolerance are analyzed in a comprehensive way. The paper offers a hybrid testing architecture that brings continuous testing practices and edge-aware optimization strategies together. Experimental evidence shows that optimized testing pipelines can enhance the efficiency of system performance up to 35 percent and reduce the latency by 20 percent in edge-intensive workloads. Moreover, this paper identifies how container orchestration platforms facilitate effective resource distribution and scaling processes at edge and cloud layers. The suggested methodology includes automated test case generation, dynamic provisioning of resources and optimization loops run by feedback. The results imply that the combination of performance testing and intelligent optimization methods can greatly boost the resilience and performance of cloud-native systems in the distributed context. This study is also an addition to the emerging research in the area of edge computing since it offers a systematic approach to assessing and optimizing performance in the next generation of distributed systems.
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