## 4 Results and Discussion  
The goal of the study is to conceive a conceptual framework that assists in the containerized microservices orchestration and provisioning in cloud computing environments. To associate the concepts of resilience and applicability, a part of the framework in a custom-built simulation environment has been simulated and the preliminary results of the limited framework evaluation have been presented. As previously stated, the proposed framework is comprised of four levels. To narrow the test, the evaluation of an intelligent partitioning module that uses the dependency agent has been made. The microservice architectural style organizes an integrated suite of tiny independent services that are designed for a business domain. To simulate the intelligent portioning, the microservices were grouped according to their design patterns [57]. The major categories of the design patterns for microservices are decomposition patterns, observability patterns, database patterns, integration patterns, and cross-cutting concern patterns. For implementation, the integration of domain-driven design (DDD) subdomain aggregates was intended. A domain (e.g., e-commerce business) is made up of many subdomains (inventory, orders, delivery, customers, etc.). Each subdomain represents a distinct aspect of the business. The aggregated items of the subdomain can be viewed as a single entity. This results in eliminating the possibility of object references exceeding service borders and satisfies the limits of the microservices transaction model since a transaction may only add or edit a single aggregate. In such a configuration, clustering and assigning individual containers to microservices is used when a service relies on the delivery of another service. These assemblies of containers are subsequently assigned to the appropriate cloud data centers. The performance efficiency of design-pattern-based intelligent portioning and distribution of microservices has been compared with the arbitrary distribution of microservices where the containers along with microservices were randomly assigned to available cloud data centers. As a result, for the distribution of microservices on cloud data centers, pattern-based clustering of microservices was not conducted in the arbitrary distribution design. A custom-built simulation program created microservice data sets of microservice consumption to verify the efficiency of intelligent partitioning. The simulation setup generated a data collection of microservices calls spread across 750 min, utilizing 150 microservices distributed across three data centers in three distinct geographical regions. Figure 12 depicts the average response time of a collection of microservices that are arbitrarily distributed. The average response time for microservice execution was 623.83 milliseconds; the minimum and highest response times were 237.28 and 1228.16 milliseconds, respectively. The intelligent partitioning employing a design-pattern-based distribution approach has been analyzed using the same simulation environment. The findings, as shown in Figure 13, indicate that response time is significantly reduced when compared to the arbitrary distribution method. The average response time for microservice requests is 457.45 milliseconds. The minimum response times for simulated service calls are 197.55 ms and the maximum value of response time is 1228.71 ms. The resource optimization part of the proposed framework was tested and evaluated in a separate study using custom-built simulation which is used for modeling and simulation of microservices in cloud computing environments. The details of which are out of scope for this study, as this study mainly defined the conceptual framework for cloud orchestration and provisioning based on container-level microservices.