1. Service Mesh is a dedicated infrastructure layer for handling service-to-service communication in modern, cloud-native applications.
2. The adoption of microservices has led to the emergence of Service Mesh as a promising approach to mitigate operational complexity during service runtime.
3. Future research opportunities in Service Mesh include designing for high performance, adaptability, and high availability, as well as exploring data analytics on top of the service mesh layer.
The article provides a comprehensive review of the state of the art, challenges, and future research opportunities in service mesh technology. The authors highlight the benefits of microservices but also acknowledge the operational complexity associated with modern applications. They introduce service mesh as a promising approach to mitigate this situation by introducing a dedicated infrastructure layer over microservices without imposing modification on the service implementations.
The article presents three main challenges in achieving the vision of a service mesh: high performance, adaptability, and high availability. The authors also provide an overview of four service mesh platforms, including Istio, Linkerd, Amazon App Mesh, and Airbnb Synapse. They compare these platforms based on their fundamental features and native compatibility with cloud vendors.
One potential bias in the article is its focus on industry perspectives rather than academic research. The authors note that there are very few research works on service mesh from academia. This may limit the scope of the article and overlook important theoretical considerations.
Another potential bias is the promotional content for certain service mesh platforms. While the authors provide a comparison table for different platforms, they do not present both sides equally or explore counterarguments against their preferred solutions.
Overall, the article provides valuable insights into service mesh technology and its potential benefits for microservices applications. However, readers should be aware of possible biases and limitations in its coverage.