# Challenges and Solution Directions of Microservice Architectures: A Systematic Literature Review
## 5 Identified Challenges and Solution Directions
### RQ2. What Are the Identified Solution Directions?
#### 5.2.6 Service Orchestration
With the conducted experiments, the proposed algorithm has been validated that proposed algorithm improves the success ratio and cost. Study [BX] points out that remote registry-based images could cause increased pulling traffics and startup time latency. To solve these issues, the authors come up with an idea of layer sharing deployment for microservices. Since containers are generally implemented as multi-layered structures, they claim that common layers can be shared between microservices. For this purpose, they propose an accelerated distributed augmented lagrangian (ADAL) based algorithm to be used by servers and registries. Experiment results show that it reduces the microservice startup time by 2.20 times on average. Study [BZ] addresses the issue of performance degradation when traffic increases. The authors claim that existing approaches of autoscaling do not pay enough attention to the microservice chain and performance degradation issues. This study proposes an autoscaling framework for microservice chains. It includes two modules. The first is responsible for collecting samples from microservices and training a latency model using the GNN. The second is responsible for identifying the number of microservice instancesthrough the GNN model. Their evaluation results demonstrate pHPA effectiveness with reduced latency and improved resource usage. Study [CA] notes that the flow control rules are generally adjusted and applied manually. In addition, it is also noted that availability is really critical for MSA-based systems and it should be handled with some concepts such as fault tolerance and flow limiting. To improve the availability of a system, the authors claim that flow control rules should be handled dynamically. To this end, they propose a dynamic flow control algorithm. The algorithm works on monitoring data and current flow and determines the flow-limiting thresholds. Evaluation results show that automatic flow control mechanisms obtain better results in terms of performance compared to traditional static methods. Study [CB] proposes a microservice rescheduling framework to address performance degradation and response time challenges. The authors point out that response time is one of the most important keys to quality of service. Hence, runtime adaptations and rescheduling should be handled carefully. They stated that existing works lack handling the effect of configuration parameters of container-based microservices. The proposed solution makes some periodic monitoring and then rescheduling activities are triggered based on threshold-based rules. Experiment results demonstrate that with the proposed framework, a significant reduction of up to 13.97% in the average response rate was achieved. Study [CD] focuses on the availability issues of MSA-based systems. It points out that availability is still a problem while migrating a legacy application to MSA even if microservices will be running on Kubernetes, which is a popular service orchestration platform. The authors stated that repair actions of Kubernetes cannot satisfy the high availability (HA) requirements. Hence, they propose an approach in which automatic service redirection to healthy microservices and application state replication can be achieved by adding service recovery to the repair actions of Kubernetes. Their experimental results show that their solution brings an improvement in terms of response time. To sum up, there are many kinds of studies addressing almost all the concerns about deployment, scheduling, auto-scalability, load-balancing, and orchestration. These are the most important areas in service orchestration. Additionally, these studies use some best practices and technologies implemented by some big vendors. Thus, it allows these studies to be used in a wide application area.