## 2 Literature Review
They classified the methodologies in the existing literature into three categories, resource allocation, service  
Figure 4: A death star graph showing inter-relationships among microservices [34]. composition, and service scheduling, and discovered that service composition draws the most researchers. In general, scientists offer heuristic strategies for optimizing business operations in real time and still there is room for investigation at the resource allocation and service scheduling levels, which are covered in this article. Due to the high demand for cloud computing services, the cloud service providers must work together in a scenario known as an interconnected cloud computing environment [41]. Commonly known interconnected cloud environments are hybrid cloud, intercloud, federated cloud, and multicloud. The cloud service provider and the cloud service user agree upon a service level agreement (SLA). If the services are not provided according to the agreement, then the cloud provider has to pay certain plenty. The studies have been conducted to increase the overall efficiency of cloud services to decrease SLA violations. Moreover, different approaches have been defined to customize the SLA according to the present status of data centers. [PERSON] and [PERSON] [42] addressed the topic of SLA violation prediction in the cloud computing environment and used the machine learning approach for SLA violation prediction. The study compared the performance of the naive Bayes model and the random forest model. They found that the random forest model provided the best performance with an accuracy of 0.99%. The classical way of predefined SLA has certain issues. The predefined SLAs are not able to change according to the change in QoS parameters. SLA can be defined as a set of Service Level Objectives (SLO). These SLOs should be ensured by the cloud service provider according to the changing status of the data centers. The key findings of the literature are highlighted in Figure 5. The literature showed that since cloud computing maintains a range of virtualized resources, scheduling is an essential component. Inefficient scheduling strategies and nonoptimal resource management confront the issues of resource overutilization and underutilization, thus resulting in resource imbalance, which results in either impairment in cloud service performance, i.e., overutilization, or waste of cloud resources, i.e., underutilization. In addition, efficient SLA and computational monitoring infrastructures are still lacking which must be capable of monitoring and detecting SLA violations. Monitoring, detection, and pre-emption of SLA should be imposed to provide better resource availability, scalability, competitive price, and energy efficiency for cloud services. Notably, scientific research highlights two sorts of rating strategies, namely, profiling and ranking, for effective content distribution of interconnected services using the repairing genetic algorithm (RGA) [43], bubble-up and bubble-flux [44], the power-conscious model [45], service request routing optimization [46], resource balance ranking schema [47], ge-kube using Kubernetes for geographical distributions [48], energy usage in relation to processing intensive communication workloads [49], StressCloud to monitor cloud energy consumption [50], the positioning of microservice instances and resource distribution optimization [46], and CloudCost which used a UML profile to allow the modeling of user behavior and cloud architecture [51]. According to the literature investigation, most cloud services have certain limits, such as a maximum cost, task complexity, maximum completion time, less profit, and make span. Based on a review of the current literature, it is also discovered that numerous container orchestration solutions and provisioning techniques do not consider the unique properties of microservices such as high cohesion, independence, autonomy, and loose coupling.