Microservices architecture has emerged as a popular approach for developing software applications in recent years. Instead of building monolithic applications, microservices architecture involves breaking down an application into smaller, independent services that can be developed, deployed, and scaled independently. This approach offers a number of benefits, including increased flexibility, better scalability, and improved fault tolerance. In this blog post, we’ll explore microservices trends and discuss how it can be used to build modern applications.
Top Microservices Trends
Here’s a brief overview of top microservices trends:
- Kubernetes: An open-source container orchestration system introduced by Google in 2014 for managing and deploying applications in containerized environments, now governed by the Cloud Native Computing Foundation.
- Artificial Intelligence Operations (AIOps): A term introduced by Gartner in 2016, AIOps involves using machine learning and big data analytics to automate and enhance IT operations, providing actionable insights for performance improvement and efficiency.
- Adoption of Service Meshes for Managing Microservices: Service meshes are becoming essential in managing the complexities of microservices architectures, providing crucial services like load balancing, security, and observability within a network of interconnected services.
- Serverless Architecture: An application design and deployment model where computing resources are provided as scalable cloud services, allowing for cost-effective, efficient management of server resources, popularized in implementations like AWS Lambda.
Kubernetes is a popular open-source container orchestration system used to manage and deploy applications in a containerized environment. It was first introduced by Google in 2014 and has since become one of the most widely used container orchestration platforms.
Kubernetes was originally released as an open-source project in July 2015. It was quickly adopted by the containerization community and became one of the most popular container orchestration platforms available. Kubernetes was designed to be highly scalable and flexible, allowing developers to easily deploy, manage, and scale their applications.
In 2018, the Cloud Native Computing Foundation (CNCF) took over the governance of Kubernetes. This move helped to solidify Kubernetes as a leading container orchestration platform and ensured its long-term sustainability. The CNCF has continued to invest in the development of Kubernetes, with regular updates and new features being added to the platform.
- 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. 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.”
The goal of AIOps is to provide IT operations teams with actionable insights that can help them quickly detect and resolve issues, improve performance, and increase efficiency. AIOps can be used to automate routine tasks, such as monitoring, alerting, and ticketing, freeing up IT staff to focus on more strategic tasks.
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.
3. Adoption of Service Meshes for Managing Microservices
Service Meshes have been gaining momentum in recent years as a way to manage the complexity of microservices. A service mesh is a dedicated infrastructure layer that handles service-to-service communication within a microservices architecture. It provides features such as load balancing, service discovery, security, and observability. As microservices architectures continue to grow in complexity, the use of service meshes is becoming more prevalent.
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. 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.
Related Reading:Top Challenges of using Microservices