Code instrumentation is vital for analysing software behaviour and facilitating cloud computing observability and monitoring, especially in microservices and containers. Despite its benefits, instrumentation introduces complexity and performance overhead, which may inadvertently slow down systems and cause unexpected or erratic behaviour. In this study, we examine the effect of automated code instrumentation on the performance of containerised microservices by comparing instrumented systems against a baseline without instrumentation. Our experimental framework is based on key performance metrics, including response time, latency, throughput, and error percentage. It is executed using a rigorous methodology with a warm-up strategy to mitigate cold-start effects. Over 5000 experiments were conducted on 70 microservice APIs drawn from two open-source applications hosted on AWS and Azure to compare the results with baseline data. The experimental analysis comprises three stages: a pilot study on AWS, a case study on AWS and Azure, and an outlier analysis of the experimental results. Overall throughput decreased by up to 8.40 %, with some individual cases experiencing up to a 30 % reduction compared to the baseline, and response time and latency dropped by 20–49 %. Moreover, the results show more outlier cases in instrumentation results than in the baseline. Additionally, the results reveal more outlier cases in the instrumentation results compared to the baseline. The instrumentation has led to unexpected or erratic behaviour, as indicated by higher variations in response time, latency, and throughput values, along with increased error rates and occasional outlier values that were not observed in the non-instrumented run. This indicates that the performance differences we observed are attributable to overhead introduced by instrumentation, rather than inherent inefficiencies within the APIs themselves. Furthermore, statistical analysis utilised the Wilcoxon Signed-Rank test and mean ratios, with multiple approaches validating significant performance differences between instrumented and baseline conditions for both cloud services. A significance analysis using Cohen’s d indicates that the throughput and response time reductions in both platforms are not only statistically significant but also suggest considerable operational impact. These findings offer insights into automated code instrumentation's performance and impact on containerised microservices. It highlights the need to develop better and less impactful instrumentation techniques, and possibly towards the development of a new approach for large-scale software development and deployment in cloud environments that facilitates efficient instrumentation by design.

: Unlike resilience, antifragility describes systems that get stronger rather than weaker under stress and chaos. Antifragile systems have the capacity to overcome stressors and come out stronger, whereas resilient systems are focused on their capacity to return to their previous state following a failure. As technology environments become increasingly complex, there is a great need for developing software systems that can benefit from failures while continuously improving. Most applications nowadays operate in cloud environments. Thus, with this increasing adoption of Cloud-Native Systems they require antifragility due to their distributed nature. : The paper proposes UNFRAGILE framework, which facilitates the transformation of existing systems into antifragile systems. The framework employs chaos engineering to introduce failures incrementally and assess the system's response under such perturbation and improves the quality of system response by removing fragilities and introducing adaptive fault tolerance strategies. : The UNFRAGILE framework's feasibility has been validated by applying it to a cloud-native using a real-world architecture to enhance its antifragility towards long outbound service latencies. The empirical investigation of fragility is undertaken, and the results show how chaos affects application performance metrics and causes disturbances in them. To deal with chaotic network latency, an adaptation phase is put into effect. : The findings indicate that the steady stage's behaviour is like the antifragile stage's behaviour. This suggests that the system could self-stabilise during the chaos without the need to define a static configuration after determining from the context of the environment that the dependent system was experiencing difficulties. : Overall, this paper contributes to ongoing efforts to develop antifragile software capable of adapting to the rapidly changing complex environment. Overall, the research provides an operational framework for engineering software systems that learn and improve through exposure to failures rather than just surviving them.

Measuring and testing the performance of cloud-based software services is critically important in the context of rapid growth of cloud computing. Scalability, elasticity and efficiency are interrelated aspects of performance of cloud-based software services. Here we present a work that is focused on measuring the scalability of cloud-based software services in technical terms. We introduce technical scalability metrics inspired by earlier technical metrics of elasticity.