## 2 Literature Review
The results showed that dual-chromosome GA gives better performance when compared with single-chromosome GA and the best-fit descending algorithm. As most of the elasticity policies are based upon the threshold-based heuristics algorithm, the study by [33] explored and designed a more flexible solution using reinforcement learning (RL) to control the elasticity of container-based applications. Technically, the conventional service-oriented architecture (SOA) services and microservices are platform-agnostic and use standardized communication protocols such as HTTP. The microservice architecture divides the monolith (single-unit) software into dozens of small service components. The microservice is highly scalable because each service can be scaled easily when required. The individual nature of microservices means they are easily usable among the different systems. The number of microservices may grow to hundreds or even thousands in number. The death star graphic visualization [34] for a social media platform is shown in Figure 4. The complexity of many microservice interactions is evident. The increasing number of microservices brings complexity to the management of services. As the network of microservices grows, it also brings challenges for interservice communication. A microservices tracing and analytical tool (JCallGraph) to analyze the invocation relationship of microservices running on containers was designed by [PERSON] [35] for the Chinese e-commerce company JD.com (having approximately 34,000 microservices running on a cluster of 500,000 containers, supporting over 250 billion RPC-based microservices calls per day). The JCallGraph acted as a distributed tracing system to track the interactions and timing information of microservices. [PERSON] and [PERSON] [36] suggested an effective multivariate autoscaling system for cloud computing utilizing bidirectional long short-term memory (Bi-LSTM). The monitor-analyze-plan-execute loop served as the foundation for the system structure. In terms of prediction accuracy, the assessment results reveal that the proposed multivariate Bi-LSTM model outperforms not only the univariate Bi-LSTM model but also multivariate deep learning models such as LSTM and CNN-LSTM. [PERSON] et al. [37] addressed resource consumption prediction to avoid over-provisioning and under-provisioning problems. The purpose of this study was to anticipate multiresource consumption utilizing a functional-link neural network (FLNN) with a hybrid genetic algorithm (GA) and particle swarm optimization (PSO). The model trains network weights using a hybrid GA-PSO algorithm and for prediction uses FLNN. When compared to conventional methodologies, the hybrid model offered higher accuracy. [PERSON] et al. [38] addressed the issue of energy consumption and resource usage efficiency in virtualized cloud data centers. The workflow activities were preprocessed in the suggested approach to prevent bottlenecks by putting jobs with higher dependencies and lengthier execution durations in different queues. The tasks are then categorized based on the intensity of the needed resources. Finally, the optimum schedules are chosen using particle swarm optimization (PSO). The results of the tests demonstrated efficacy in terms of energy usage, makespan, and load balancing. [PERSON] et al. [39] evaluated the service-oriented architecture patterns and microservice architecture patterns for the deployment of applications in cloud computing, specifically the eHealth applications. It was determined that the microservice architecture performs better in terms of response time and performance than the service-oriented architecture variant; nevertheless, the microservice architecture consumes much more bandwidth than the service-oriented architecture variant. [PERSON] and [PERSON] [40] conducted a thorough evaluation of the literature on the optimization of business process execution in services architecture.