Organizations have long been yearning for greater speed, agility, and efficiency as they update their operating models and improve digital capabilities. Many CIOs want to accelerate innovation by shortening the release and deployment cycles. While doing this, they hope to provide a better experience to customers. Of course, the goal is always to outpace their competition and get ahead of the pack.
One way organizations are tackling the need for accelerated application development and delivery is by embracing agile development frameworks, allowing them to gain time-to-market by combining tasks like development, quality assurance, and operational tasks using microservices and containers.
A microservices architecture splits your application into multiple services that do a fine-grained function of small services that are lightweight, with independent deployment, scalability, and portability. Microservices that are enabled by containers are becoming more popular because developers can easily isolate functions, which saves them time and effort while increasing productivity. Unlike with monoliths, each microservice deals with just one concern so there isn’t a need to rebuild and deploy the entire application. Learn more about microservice architectures, objectives, benefits and challenges here.
Container technology provides an ideal environment for deployment of microservices, with respect to speed, isolation management, and application lifecycle. Some of the latest strategies for development utilize continuous integration and continuous development (CI/CD) methodologies and break apart silos to help streamline the entire process. Some are opting to use container technology to help with this process.
Container orchestration platforms like Kubernetes, Docker Swarm, Helios, etc. have become very popular in operating at scale. Kubernetes can schedule any number of container replicas across a group of node instances. Docker Swarm provides clustering, scheduling, and integration capabilities. It is important to consider tools when there's a need to massively scale containers.
With clustering, scheduling, and orchestration technology, developers can ensure that applications that exist inside of containers can scale and are resilient.
Memory is crucial when it comes to using containers. Figuring out memory capacity that is available on the host when compared to the memory needed by the containers helps to make the decision on which host to deploy it. By doing this, teams are able to free up shared resources and avoid capacity constraints that have become commonplace due to the single encapsulated process with shared infrastructure.
At the same time, there can be bottlenecks on the network used within the cluster and the network visualization layer used to connect containers. Teams have to closely monitor performance, load balancing, and interactions. This is most important when operating at scale in a multi-cloud environment where there are many containers spread across different service providers.
Most of the traditionally used IT monitoring tools don’t provide visibility into the containers that make up microservices, which can lead to gaps between hosts and applications. As soon as applications are deployed and have gone live, IT teams may find themselves overcome with alerts to problems. Organizations need to put monitoring that covers the entire IT stack into play.
At the same time, organizations need to ensure that there are enough team members and power to cover the exponentially growing estate of microservices. Microservices have a tendency to multiply in rapid succession if they are not managed diligently. These microservices then start to compete for the same IT infrastructure underneath. It is best for organizations to use analytics tools to discover duplicate microservices and detect any patterns in container behavior and consumption to help with resource management.
CIOs have been placed under unimaginable pressure to create agile IT environments that support digital business. Already, and increasingly into the future, containers and microservices will play important roles in meeting objectives by enabling faster release cycles.
If your organization hasn’t started this process just yet, CAST AIP and Imaging can help get you started by analyzing the current/legacy application/s and splitting the same to fine-grained function to facilitate microservices transformation.
CAST AIP can help with sizing the microservices application and act as a CI/CD quality gate and check before deployment into the Container.
CAST security analysis help in identification of vulnerabilities and security exposures on the container
At the same time, CAST AIP analysis can identify the FAN in/out and single point failure proactively and thus ensure that apps or services within a container are resilient.
CAST’s automated function point analysis can help in transaction identification and scheduling.
CAST AIP can also help with Sizing and Capacity planning for Container replication and Scheduling.
As systems become more and more complex with increasing numbers of microservices, sophisticated software intelligence from CAST can help gain deep insight into the system design and characteristics, avoid costly design mistakes or developer noncompliance to the architecture.