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    Handling Rollbacks with Separated Response Control Service for Microservice Architecture
    (Institute of Electrical and Electronics Engineers Inc., 2022) Gordesli, M.; Nasab, A.; Varol, A.
    Working with large scale microservice based applications may be hard to maintain and control. The services in those applications must be isolated from each other and they must work in harmony. There must be an intelligent transaction management system to implement this kind of collaborative work of microservices. There are two main methodologies for transaction management, namely Two Phase Commit (2PC) and Saga pattern. There are two phases in 2PC that prepare phase and commit phase. In Saga pattern, there are two ways of implementing Saga pattern; choreography based, and orchestration based. When there is a problem in the whole transaction, controlling the rollback scenario may be hard because each microservice has its own rollout and rollback solutions in both ways of Saga pattern. If we separate the rollback system from the microservices, we might have simpler microservices and microservices that can run in parallel. So whole system works safer and faster. In this work we focus on parallel execution of microservices with separate rollback system that controls the whole application in error condition. © 2022 IEEE.
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    Importance of Machine Learning and Deep Learning Algorithms in Earthquake Prediction: A Review
    (Institute of Electrical and Electronics Engineers Inc., 2023) Gursoy, G.; Varol, A.; Nasab, A.
    Earthquakes are the leading natural disasters that have caused loss of life and property since the formation of the world. Machine learning and deep learning are frequently used in studies for earthquake prediction. This article consists of a compilation of studies using machine learning and deep learning algorithms. In the article, studies on topics such as earthquake magnitude estimation, signal discrimination, electron density estimations in the ionosphere, examination of radon gas anomalies using machine learning and deep learning algorithms are included. The studies in this paper show that Deep Learning algorithms are used more frequently in earthquake forecasting. It is expected that Deep Learning will provide more successful results in future studies due to its ability to work with larger data sets compared to Machine Learning and its ability to improve itself from errors. © 2023 IEEE.