@phdthesis{phd_mikael,

title = {Strategies For Building Performant Containerized Applications},

author = {Mikael Sabuhi},

year  = {2023},

date = {2023-09-25},

urldate = {2023-09-25},

abstract = {The evolution of cloud computing in the last decade has offered unprecedented access to sizable, configurable computing resources with minimal management effort. Containerization of applications, particularly through Docker, has been pivotal in this progression. As modern software increasingly relies on various cloud services, designing performant cloud applications has emerged as a critical concern. Key attributes of such applications include reliability, scalability, efficiency, fault tolerance, and responsiveness. This thesis seeks to address the challenges intrinsic to creating performant cloud applications by developing strategies aimed at achieving these characteristics through: 1) the application of autoscaling techniques to enhance scalability, efficiency, and responsiveness; 2) the introduction of a methodology for assessing the impact of Docker image upgrades on containerized applications to prevent performance degradation; and 3) the utilization of microservices architecture to develop scalable, reliable, and fault-tolerant cloud applications. In our initial research, we propose a pioneering approach to optimize the performance and resource usage of containerized cloud applications using adaptive controllers grounded in control theory. Our methodology harnesses the capacity of neural networks to capture the intrinsic non-linearity of these applications, and adapts the parameters of a proportional-integral-derivative (PID) controller to accommodate environmental changes. The outcomes demonstrate significant enhancements in resource utilization and a reduction in service level agreement violations, surpassing the performance of other examined autoscaling techniques. In the subsequent study, we present a method to evaluate the performance implications of Docker image upgrades on cloud software systems and their correlation with application dependencies. Our case study of 90 official WordPress images underscores the need for comprehensive performance testing before upgrades, the importance of maintaining a performance repository for reporting test results, and the potential benefits of extending semantic versioning to encompass performance modifications. This investigation encourages an enlightened approach to Docker image management, promoting enhanced cloud application performance. Lastly, we introduce Micro-FL, a fault-tolerant federated learning framework crafted to enhance the reliability and scalability of cloud-based machine learning platforms. By incorporating a microservices-based architecture within Docker containers, Micro-FL overcomes challenges typically associated with federated learning, such as resource constraints, scalability, and system faults. Performance assessments demonstrate Micro-FL’s capability to efficiently manage faults and streamline federated learning processes, offering a more robust and scalable solution for federated learning. The research work presented in this thesis provides deep insights, actionable recommendations, and effective and thoroughly evaluated approaches for building performant cloud applications.

},

keywords = {Docker, Docker Hub, Microservices, Performance, Performance analysis, Performance engineering},

pubstate = {published},

tppubtype = {phdthesis}

}