In the wake of advancements in big data, cloud computing, and the Internet of things, software functionalities are constantly evolving to cater to a diverse and growing set of user needs. This rapid pace of data updates and the introduction of new modules can destabilize and imbalance traditional monolithic architectures. Consequently, microservices architecture (MSA), with its independent deployment service capabilities, has been proposed as a solution. MSA offers significant advantages in scalability and maintainability. However, a standard specific definition of MSA remains elusive due to its composition being contingent on specific business logic and varying business scenario requirements. These differing requirements inevitably lead to unique MSA patterns. This study aims to presents the cost-effective and effort-based prediction model for the most influential challenges of migration from monolithic to MSA using a nature-inspired optimization algorithm, i.e., genetic algorithm (GA). Moreover, future research directions are suggested in the realm of microservices architecture.

In the present digital era, malware defences and attacks are becoming more difficult, creating a progressing cyberthreat landscape. With the fast development in technology, cyberthreats have shown improved intricacy and potency that frequently exceed the abilities of conventional defence systems. The Internet of Things (IoT) is a technical development that allows machine-to-machine and human-to-human interaction for essential data exchange. The IoT provides numerous advantages but also builds several problems. Exposures in IoT methods are problematic and main to devices enduring various threats, with the danger of denial of service (DoS) and security challenges like privacy, confidentiality, and obtainability to assault. This manuscript proposes a cyberthreat defence mechanism using a Binary Ebola Optimization Search Algorithm and Ensemble Models (CDM-BEOSAEM) method. The main intention of the CDM-BEOSAEM method is to enhance the cyberattack detection method in an IoT environment. Initially, the min-max normalization is applied in the data normalization stage to convert input data into a beneficial format. Furthermore, the binary ebola optimization search algorithm (BEOSA) model recognizes the most appropriate features in the feature selection (FS) process. For the classification of cyberthreat defence, the proposed CDM-BEOSAEM model utilizes an ensemble of bidirectional gated recurrent unit (BiGRU), auto-encoders (AE), and graph convolutional network (GCN) techniques. Finally, the hyperparameter selection of ensemble models is performed by implementing the escape Coati Optimization Algorithm (eCOA) technique. The simulation of the CDM-BEOSAEM approach is accomplished under the ToN-IoT dataset, and the results are measured using various measures. The performance validation of the CDM-BEOSAEM approach portrayed a superior accuracy value of 99.29% over existing models.