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1.
Mikael Sabuhi; Petr Musilek; Cor-Paul Bezemer
Micro-FL: A Fault-Tolerant Scalable Microservice Based Platform for Federated Learning Journal Article
Future Internet, 16 (3), pp. 1-19, 2024.
Files: 
Abstract | BibTeX | Tags: Federated learning, Machine learning, Microservices
@article{Sabuhi_MicroFL,
title = {Micro-FL: A Fault-Tolerant Scalable Microservice Based Platform for Federated Learning},
author = {Mikael Sabuhi and Petr Musilek and Cor-Paul Bezemer },
year = {2024},
date = {2024-02-19},
journal = {Future Internet},
volume = {16},
number = {3},
pages = {1-19},
abstract = {As the number of machine learning applications increases, growing concerns about data privacy expose the limitations of traditional cloud-based machine learning methods that rely on centralized data collection and processing. Federated learning emerges as a promising alternative, offering a novel approach to training machine learning models that safeguards data privacy. Federated learning facilitates collaborative model training across various entities. In this approach, each user trains models locally and shares only the local model parameters with a central server, which then generates a global model based on these individual updates. This approach ensures data privacy since the training data itself is never directly shared with a central entity. However, existing federated machine learning frameworks are not without challenges. In terms of server design, these frameworks exhibit limited scalability with an increasing number of clients and are highly vulnerable to system faults, particularly as the central server becomes a single point of failure. This paper introduces Micro-FL, a federated learning framework that uses a microservices architecture to implement the federated learning system. It demonstrates that the framework is fault-tolerant and scalable, showing its ability to handle an increasing number of clients. A comprehensive performance evaluation confirms that Micro-FL proficiently handles component faults, enabling a smooth and uninterrupted operation.},
keywords = {Federated learning, Machine learning, Microservices},
pubstate = {published},
tppubtype = {article}
}
As the number of machine learning applications increases, growing concerns about data privacy expose the limitations of traditional cloud-based machine learning methods that rely on centralized data collection and processing. Federated learning emerges as a promising alternative, offering a novel approach to training machine learning models that safeguards data privacy. Federated learning facilitates collaborative model training across various entities. In this approach, each user trains models locally and shares only the local model parameters with a central server, which then generates a global model based on these individual updates. This approach ensures data privacy since the training data itself is never directly shared with a central entity. However, existing federated machine learning frameworks are not without challenges. In terms of server design, these frameworks exhibit limited scalability with an increasing number of clients and are highly vulnerable to system faults, particularly as the central server becomes a single point of failure. This paper introduces Micro-FL, a federated learning framework that uses a microservices architecture to implement the federated learning system. It demonstrates that the framework is fault-tolerant and scalable, showing its ability to handle an increasing number of clients. A comprehensive performance evaluation confirms that Micro-FL proficiently handles component faults, enabling a smooth and uninterrupted operation.
2.
Mikael Sabuhi
Strategies For Building Performant Containerized Applications PhD Thesis
2023.
Files:
Abstract | BibTeX | Tags: Docker, Docker Hub, Microservices, Performance, Performance analysis, Performance engineering
@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}
}
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.
3.
Simon Eismann; Cor-Paul Bezemer; Weiyi Shang; Dušan Okanović; André van Hoorn
Microservices: A Performance Tester's Dream or Nightmare? Inproceedings
ACM/SPEC International Conference on Performance Engineering (ICPE), pp. 1–12, 2020.
Files:
Abstract | BibTeX | Tags: DevOps, Microservices, Performance, Regression testing
@inproceedings{Simon20,
title = {Microservices: A Performance Tester's Dream or Nightmare?},
author = {Simon Eismann and Cor-Paul Bezemer and Weiyi Shang and Dušan Okanović and André van Hoorn },
year = {2020},
date = {2020-01-24},
urldate = {2020-01-24},
booktitle = {ACM/SPEC International Conference on Performance Engineering (ICPE)},
pages = {1--12},
abstract = {In recent years, there has been a shift in software development towards microservice-based architectures, which consist of small services that focus on one particular functionality. Many companies are migrating their applications to such architectures to reap the benefits of microservices, such as increased flexibility, scalability and a smaller granularity of the offered functionality by a service.
On the one hand, the benefits of microservices for functional testing are often praised, as the focus on one functionality and their smaller granularity allow for more targeted and more convenient testing. On the other hand, using microservices has their consequences (both positive and negative) on other types of testing, such as performance testing. Performance testing is traditionally done by establishing the baseline performance of a software version, which is then used to compare the performance testing results of later software versions. However, as we show in this paper, establishing such a baseline performance is challenging in microservice applications.
In this paper, we discuss the benefits and challenges of microservices from a performance tester’s point of view. Through a series of experiments on the TeaStore application, we demonstrate how microservices affect the performance testing process, and we demonstrate that it is not straightforward to achieve reliable performance testing results for a microservice application.},
keywords = {DevOps, Microservices, Performance, Regression testing},
pubstate = {published},
tppubtype = {inproceedings}
}
In recent years, there has been a shift in software development towards microservice-based architectures, which consist of small services that focus on one particular functionality. Many companies are migrating their applications to such architectures to reap the benefits of microservices, such as increased flexibility, scalability and a smaller granularity of the offered functionality by a service.
On the one hand, the benefits of microservices for functional testing are often praised, as the focus on one functionality and their smaller granularity allow for more targeted and more convenient testing. On the other hand, using microservices has their consequences (both positive and negative) on other types of testing, such as performance testing. Performance testing is traditionally done by establishing the baseline performance of a software version, which is then used to compare the performance testing results of later software versions. However, as we show in this paper, establishing such a baseline performance is challenging in microservice applications.
In this paper, we discuss the benefits and challenges of microservices from a performance tester’s point of view. Through a series of experiments on the TeaStore application, we demonstrate how microservices affect the performance testing process, and we demonstrate that it is not straightforward to achieve reliable performance testing results for a microservice application.
On the one hand, the benefits of microservices for functional testing are often praised, as the focus on one functionality and their smaller granularity allow for more targeted and more convenient testing. On the other hand, using microservices has their consequences (both positive and negative) on other types of testing, such as performance testing. Performance testing is traditionally done by establishing the baseline performance of a software version, which is then used to compare the performance testing results of later software versions. However, as we show in this paper, establishing such a baseline performance is challenging in microservice applications.
In this paper, we discuss the benefits and challenges of microservices from a performance tester’s point of view. Through a series of experiments on the TeaStore application, we demonstrate how microservices affect the performance testing process, and we demonstrate that it is not straightforward to achieve reliable performance testing results for a microservice application.