Service Meshes and Their Relationship to Kubernetes Networking

Roman Glushach
7 min readAug 29, 2023
Service Mesh

A service mesh is a dedicated infrastructure layer that provides reliable, secure, and observable communication between microservices. It consists of two components: a data plane and a control plane. The data plane is composed of proxies that intercept and manage the network traffic between the microservices. The control plane is responsible for configuring and monitoring the data plane.

Kubernetes is a popular platform for deploying and managing containerized applications. It provides a set of abstractions and APIs for orchestrating the containers, such as pods, services, deployments, and ingresses. However, Kubernetes does not offer a native solution for service-to-service communication. Instead, it relies on the underlying network layer to provide connectivity and routing between the pods.

This is where a service mesh can complement Kubernetes networking. By deploying a service mesh on top of Kubernetes, you can leverage the benefits of both technologies. A service mesh can provide advanced features such as load balancing, service discovery, encryption, authentication, authorization, observability, and resilience for the microservices running on Kubernetes. A service mesh can also simplify the configuration and management of the network policies and rules by using a declarative approach.

Service Meshes vs Traditional Load Balancers and Reverse Proxies

Service meshes, traditional load balancers, and reverse proxies are all tools used to manage traffic and optimize the performance of applications. However, they differ in their design principles, functionality, and use cases.

Traditional Load Balancers

Load balancers have been around for decades and are a crucial component of many enterprise networks. They act as intermediaries between clients and servers, distributing incoming traffic across multiple backend instances to ensure no single server becomes overwhelmed.

Traditional load balancers operate at layer 4 (TCP/UDP) of the OSI model and use simple algorithms such as round-robin, least connections, or IP hash to determine which server receives the next request.

Reverse Proxies

Reverse proxies build upon the concept of traditional load balancers by adding additional layers of abstraction and functionality. Like load balancers, reverse proxies sit between clients and servers, but they operate at a higher level of the network stack, typically at layer 7 (HTTP). This allows them to perform more sophisticated tasks beyond mere load balancing, such as URL rewriting, caching, compression, and security filtering. Reverse proxies can also perform A/B testing, SSL offloading, and other advanced functions that traditional load balancers cannot.

Service Meshes

Service meshes take the concepts of reverse proxies even further by introducing a new architectural pattern specifically designed for modern, distributed systems. Unlike traditional load balancers and reverse proxies, service meshes operate at the application layer (layer 7), close to the code, and are tightly integrated with the application’s logic. This proximity to the application enables service meshes to collect rich contextual data about the requests and responses flowing through them, allowing for more informed decision-making when it comes to traffic management.

Key Differences

The role of APIs and API gateways in service meshes

API (Application Programming Interface) is a set of rules and protocols used for building software applications. It defines how different parts of an application communicate with each other, including request and response formats, error handling, and security measures.
On the other hand, an API gateway sits at the edge of an API, acting as an entry point for external requests and directing them to the appropriate internal endpoints. Think of it like a receptionist who takes incoming calls, determines their purpose, and routes them to the right person within an organization.
A service mesh is essentially a configurable infrastructure layer that handles all aspects of inter-service communication. It provides features such as load balancing, circuit breaking, service discovery, and traffic management, making it easier for developers to build resilient and performant distributed systems. When designing a service mesh, there are two main approaches for routing requests between services: using APIs and API gateways, or relying on the service mesh itself to handle communication.

So, how do APIs and API gateways facilitate communication in a service mesh?

The key lies in their ability to decouple services from one another. By exposing APIs through an API gateway, services can be designed independently without worrying about the implementation details of other services. This separation allows for greater flexibility when scaling individual components or updating existing ones without affecting the entire system. Additionally, APIs and API gateways offer a standardized interface for communicating with external clients or services, further simplifying integration efforts.

There are several benefits to using APIs and API gateways in service meshes:

  • they promote loose coupling between services, enabling better modularity and maintainability. With well-defined APIs, teams can work on separate services independently, reducing coordination overhead during development cycles
  • APIs and API gateways allow for versioning and backward compatibility, ensuring that changes made to individual services won’t break the overall system
  • using APIs and API gateways enables organizations to leverage industry standards and best practices around security, authentication, and authorization. For example, OAuth2, OpenID Connect, and JWT are commonly used protocols for securing API access and protecting sensitive data

Key Features of Service Meshes

Traffic Management and Routing

Traffic management and routing are core capabilities of service meshes. They enable developers to control the flow of traffic between microservices, ensuring that requests are properly directed and processed.

Load Balance

Load balancing is a critical feature of service meshes. It ensures that no single instance of a service is overwhelmed with requests, leading to poor performance or even failure.

Resilience and Fault Tolerance

Microservices applications must be designed to handle failures gracefully. Service meshes provide various features to improve resilience and fault tolerance.

Circuit Break

Circuit breaking is a safety mechanism designed to prevent cascading failures in distributed systems. It detects when a service is experiencing problems and automatically redirects traffic away from that service to avoid further failures.

Canary Releases

Canary releases are a deployment strategy that enables developers to gradually introduce new versions of a service into production, reducing the risk of introducing bugs or compatibility issues.

Blue-Green Deployments

Blue-green deployments are a variation of canary releases that involve running two identical sets of instances (blue and green) in parallel, with traffic routed to both sets simultaneously.


One of the most critical aspects of any distributed system is security. Service meshes offer several security features to protect your services from unauthorized access and data breaches.

Observability and Tracing

As microservices applications grow in complexity, it becomes challenging to understand how services interact with each other. Service meshes address this challenge by providing built-in observability and tracing capabilities.

Popular Service Mesh Options

Recommendations for Selecting the Right Service Mesh Option

Advanced Topics in Service Meshes

Service meshes have evolved beyond simple service discovery and load balancing, offering advanced features that enhance the reliability, security, and performance of modern microservices architectures.

Multicluster Service Meshes

In traditional service meshes, a single mesh instance is deployed across a single Kubernetes cluster or namespace. However, modern cloud-native applications often span multiple clusters or even cloud providers. To address this challenge, multicluster service meshes were developed.

A multicluster service mesh allows multiple mesh instances to communicate with each other, forming a federation of interconnected meshes. This enables seamless service discovery, load balancing, and traffic management across multiple clusters and regions.

With multicluster service meshes, you can easily migrate workloads between clusters, scale services horizontally across multiple clusters, and build globally distributed applications.

Serverless Service Meshes

Serverless computing is becoming increasingly popular due to its many advantages, including reduced operational overhead, faster time-to-market, and lower costs. However, serverless functions often lack the networking and security capabilities provided by traditional service meshes. This gap is filled by serverless service meshes.

A serverless service mesh is designed specifically for serverless functions, providing features like service discovery, load balancing, authorization, and encryption. It integrates seamlessly with serverless platforms like AWS Lambda, Azure Functions, and Google Cloud Functions.

By leveraging a serverless service mesh, you can enjoy the benefits of service meshes without the need to manage servers or infrastructure.

Machine Learning-Based Service Meshes

Traditional service meshes rely on static configurations and fixed rules to handle traffic management and routing decisions. However, this approach can lead to suboptimal traffic flow and poor application performance. To overcome these limitations, machine learning-based service meshes were introduced.

A machine learning-based service mesh uses artificial intelligence and machine learning algorithms to dynamically optimize traffic flow, predict demand, and automatically adjust routing decisions. It learns from historical data and real-time feedback to continuously improve application performance and user experience.

Additionally, machine learning-based service meshes can detect anomalies and predict potential issues, enabling proactive remediation and minimizing downtime.

Real-Time Analytics and Machine Learning in Service Meshes

While traditional service meshes provide basic monitoring and logging capabilities, they often fall short in terms of real-time analytics and machine learning. Modern service meshes now incorporate powerful analytics and machine learning engines, enabling real-time insights into application performance, traffic patterns, and user behavior.

With real-time analytics and machine learning in service meshes, you can track key performance indicators (KPIs), identify trends, and detect anomalies as they occur. This enables prompt action to address performance issues, optimize resource utilization, and improve overall application quality.

Moreover, these capabilities empower developers and operators to collaborate more effectively, aligning their efforts towards delivering better customer experiences and business outcomes.


Service meshes have come a long way since their inception, expanding beyond simple service discovery and load balancing to tackle advanced challenges in modern microservices architecture.



Roman Glushach

Senior Software Architect & Engineer Manager at Freelance