# 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
The authors propose an overload control scheme designed for MSA, called DAGOR. It monitors the load status of each microservice in real-time and distributes the load between the related services when overload is detected. DAGOR has been used in the messaging application for five years. According to experience and experiment results, DAGOR achieved high success. Study [AW] focuses on the latency because of long-chain microservices. Generally, a request is processed by many microservices called chains, and these microservice chainsare in a competition to use resources. The authors noticed that there is not enough study to handle the competition between microservices chains. In this study, a queue-based and chain-oriented load balancing method is proposed. With this method, it is claimed that this method decreases the latency of the long chain. Their evaluations also show that it could decrease the latency of long chains. Study [BE] addresses the load balancing issue. The authors stated that load balancing is the most important mechanism for availability and scalability and there are some techniques such as client-side and server-side to implement load balancing in a system. However, in order to benefit from each method's advantages, they consider combining them. To this end, they propose a hybrid model to leverage the advantages of both sides. Study [O] points out the scheduling problem of microservices, especially in multiple clouds. The authors believe that there is an alternative way showing decreased overall turnaround time in contrast to the standard biased greedy scheduling algorithm. For this purpose, they propose an affinity-based scheduling approach and compare it with the standard biased greedy algorithm. The proposed approach achieves a big improvement. Study [AY] addresses total network delay and network price issues. In addition, the authors noted that increasing energy efficiency is an important task in an edge platform. They aim to minimize network delay and price and improve energy efficiency by designing a novel approach. To this end, they propose a dynamic microservice scheduling algorithm for mobile edge computing (MEC) and evaluate the computational complexity of the scheduling algorithm. According to simulation results, it has been observed that the microservice scheduling framework improves the performance metrics based on total network delay, energy consumption rate (ECR), failure rate, average price, and satisfaction level. Study [AO] points to increase energy consumption and low service performance with MSA. The authors stated that since resource allocation should be handled efficiently, unlike the current studies not focusing on optimization issues for such chain-oriented service provisioning, they focus on the resource allocation optimization problem. They aim to optimize end-to-end response time and resource usage. To this end, the three-stage scheme is proposed to improve the metrics mentioned above. According to the evaluation, their approach provides a better result than benchmarking algorithms on load balancing and energy consumption. Study [AQ] works on a novel scheduling framework for Kubernetes. The authors aim to introduce a solution providing improvement for locally main tasks. For this purpose, they propose a hybrid-state scheduler for unscheduled jobs. To validate this approach, they carried out an analysis of their approach's capabilities and evaluation results show that it will overcome problems of their existing solution in their clusters such as collocation interference, priority preemption, high-availability, and baseline scheduling problems. Study [BD] handles scheduling issues in terms of some concerns such as availability, reliability, resource utilization, scalability, and power consumption. It is noted that current scheduler solutions do not cover all of these concerns. However, the authors claimed that these concerns should be handled together in a scheduling approach to take better results.