Microservices trends

Unlike a traditional monolithic approach, in which all components form an inseparable entity, microservices work in synergy to accomplish the same tasks while being separate. Each of these components or processes is a microservice. Granular and lightweight, this type of software development allows a similar process to be used in multiple applications. This is a key element in optimizing application development for a cloud-native model. Here will look at some of the top microservices trends that will be shaping the microservices landscape in the year to come.

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1. Kubernetes 

Often described as the “operating system of the cloud,” Kubernetes is an open-source platform for managing clusters of containerized applications and services. Developed in 2014 by Google engineers Joe Beda, Brendan Burns, and Craig McLuckie and released as open-source shortly thereafter, Kubernetes has quickly become a thriving cloud-native ecosystem in its own right. Today, Kubernetes, which means “helmsman” or “pilot” in ancient Greek, is managed by the Cloud Native Computing Foundation (CNCF), a branch of the Linux Foundation. With Kubernetes, you can:

  • Orchestrate containers across multiple hosts;
  • Optimize your hardware utilization to maximize the resources required to run your business applications;
  • Control and automate application deployments and upgrades;
  • Mount and add storage systems to run stateful applications;
  • Scale containerized applications and their resources on the fly;
  • Manage services declaratively to ensure that deployed applications always run the way you deployed them;
  • Verify the integrity of your applications and automatically repair them with automatic placement, startup, replication, and scaling.

2. Serverless architecture 

Serverless computing is an event-based application design and deployment model in which computing resources are delivered as scalable cloud services. More information about the impact of Serverless Computing on modern application development can be found in our white paper.

In traditional application deployments, server computing resources represent fixed, recurring costs, regardless of the actual volume of the processing activity. In a serverless deployment, the cloud customer pays only for the services consumed, never for downtime or inactivity. Serverless computing does not eliminate servers but aims to push compute resource issues to the back burner during the design phase. The term is often associated with the NoOps movement, and the concept is also known as FaaS (Function as a Service) or RaaS (Runtime as a Service).

AWS Lambda is an example of serverless computing in a public cloud. Developers can enter code, build backend applications, create event management routines, and process data without worrying about the underlying servers, virtual machines (VMs), and compute resources needed to support a considerable number of events because the provider manages the hardware and infrastructure.

Read more on some of the most innovative and profitable microservices examples. Serverless is one way to host microservice and has been gaining popularity because it allows focusing on business, not technology management, allowing you to stay ahead of all the major microservices trends.

3. Service mesh 

A Service Mesh, such as the open-source Istio project, is a way to control how distinct elements of an application share data with each other. Unlike other communication management systems, a Service Mesh is a dedicated infrastructure layer created directly in the application. This visible infrastructure layer can show how the different elements of an application interact with each other. This makes it easier to optimize communications and avoid downtime as the application develops.

Each element, or “service,” of an application depends on other services to satisfy user expectations. Let’s take the example of an online sales application. Before buying an item, the user needs to know if it is in stock. The service that communicates with the inventory database must then communicate with the product’s web page, which must communicate with the user’s online shopping cart. The retailer may also decide to integrate a product recommendation service within the application to guide users. This new service will need to communicate with a database of product tags to generate recommendations, but also with the inventory database that the product page already had to communicate with. That’s a lot of reusable mobile elements.

Modern applications are often broken down like this, as a network of services, each performing a particular business function. To perform its function, a service may need to request data from several other services. But what happens when some services, like the reseller’s inventory database, are overloaded with requests? That’s where the Service Mesh comes in: it routes requests from one service to the next so that the moving parts work more efficiently.

4. Artificial Intelligence Operations or AIOps

The term “AIOps” refers to applying machine learning and analytics to big data to automate and enhance IT operations. AI can automatically analyze considerable amounts of network and machine data for patterns to identify the cause of existing problems but also anticipate future ones.

Gartner coined the term “AIOps” in 2016. In the AIOps Platform Market Guide, Gartner defines AIOps platforms as “software systems that combine big data and Artificial Intelligence (AI) or machine learning to improve and partially replace a wide range of IT processes and tasks, including availability and performance monitoring, event correlation and analysis, IT service management and automation.”

Microservices have started to reach their full potential as a conduit for organizations of all sizes to achieve vast amounts of value. Experts predict by 2022, 90% of All New Apps Will Feature Microservices Architectures that improve the ability to design, debug, update, and leverage third-party code. Microservices architectures will continue to help businesses reduce downtime, optimize resources, and decrease infrastructure costs.

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Microservices and DreamFactory

So what do these microservices trends mean for you? Building a microservices architecture requires not only the right technical skills but requires a shift in how you manage your projects internally. This can make it feel like a daunting undertaking, but with the right resources on your side, you’ll be able to reap the many benefits of microservices sooner rather than later.

DreamFactory is a modern, easy to use, no-code auto API generation platform that is tailor made to support microservice development, making it easy to stay on top of all the microservices trends. Interested to learn more about how DreamFactory can help spearhead your adoption of microservices architecture? Start your free trial!