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AWS App Mesh is now generally available and supported for production use. App Mesh is a service mesh that provides application level networking to make it easy for your services to communicate with each other across multiple types of compute infrastructure. App Mesh standardizes how your services communicate, giving you end-to-end visibility and ensuring high-availability for your applications. Modern applications are typically composed of multiple services. Each service may be built using multiple types of compute infrastructure such as Amazon EC2 and AWS Fargate.

Since we released 1.0 back in July, we’ve done a lot of work to help people get into production. Not surprisingly, we had to do some patch releases (6 so far!), but we’ve also been hard at work adding new features to the product. The theme for 1.1 is Enterprise Ready. We’ve been very pleased to see more and more companies using Istio in production, but as some larger companies tried to adopt Istio they hit some limits.

Hyper-scale, microservice-based cloud environments have been exciting to build but challenging to manage. Along came Kubernetes (container orchestration) in 2014, followed by Istio (container service management) in 2017. Both open-source projects enable developers to scale container-based applications without spending too much time on administration tasks. Now, new enhancements in Istio 1.1 deliver scale-up with improved application performance and service management efficiency. Simulations using our sample commercial airline reservation application show the following improvements, compared to Istio 1.

One of the advantage of deploying a microservice-based application in an Istio service mesh is to allow one to externally control service monitoring, tracing, request (version) routing, resiliency testing, security and policy enforcement, etc., in a consistent way across those services, for the application as a whole. In this blog we will focus on the in-depth telemetry side of the house and see how can we configure mixer to collect a uniform set of metrics across all services.

Istio Multicluster is a feature of Istio–the basis of Red Hat OpenShift Service Mesh–that allows for the extension of the service mesh across multiple Kubernetes or Red Hat OpenShift clusters. The primary goal of this feature is to enable control of services deployed across multiple clusters with a single control plane. The main requirement for Istio multicluster to work is that the pods in the mesh and the Istio control plane can talk to each other.

Here is how Istio and Hystrix differ in the implementation of the circuit breaker pattern, handling the lack of availability of a service. There is no denying that in the last few years, technologies like Docker and Kubernetes, to name a few of the most relevant, have revolutionized how we reason about software development and deployment. But whilst the fast pace of the software development industry pushes developers to adopt the most recent technologies, it is important to take a step back and to have a better look at established patterns that enable parts of these technologies.

Today we’re very happy to announce the release of Linkerd 2.1. This is our first stable update to 2.0, and introduces a host of goodies, including per-route metrics, service profiles, and a vastly improved dashboard UI. We’ve also added a couple exciting experimental features: proxy auto-injection, single namespace installs, and a high-availability mode for the control plane. Source: linkerd.io

Originally created at Lyft, Envoy is a high-performance open source edge, middle, and service proxy. The project helps ease the transition to, and operation of, cloud native architectures by managing the interactions among microservices in order to ensure application performance. Envoy’s out-of-process architecture can be used with any application, in any language or runtime; supported protocols and features include HTTP/2, gRPC, MongoDB, Redis, Thrift, external authorization, global rate limiting, a rich configuration API, and much more.

Microservices have become mainstream in the enterprise. This proliferation of microservices applications generates new problems, which requires a new approach to managing problems. A microservice is a small, independently deployable, and independently scalable software service that is designed to encapsulate a specific semantic function in the larger applicationl. This article explores several approaches to deploying tools to debug microservices applications on a Kubernetes platform like Red Hat OpenShift, includingOpenTracing, Squash, Telepresence, and creating a Squash Operator in Red Hat Ansible Automation.

Today we are thrilled to announce the release of SuperGloo, an open-source project to manage and orchestrate service meshes at scale. SuperGloo is an opinionated abstraction layer that will simplify the installation, management, and operation of your service mesh, whether you use (or plan to use) a single mesh or multiple mesh technologies, on-site, in the cloud, or on any topology that best fits you. A service mesh is an infrastructure layer that handles service-to-service communication, freeing applications from being aware of the complex communication network.