Independent scalability is a major benefit of microservices, a challenging thing to implement. Scale microservices with a focus on users' priorities.
|Ronak Kumar Samantray||Feb 3|| 1|
IT teams can face several challenges when scaling micro services based applications.
With a monolithic application, IT teams can carry out straightforward, well-established tactics to scale both vertically and horizontally. A load balancer can allocate traffic across various resources as needed. If there is too much of a load on the application, teams can even spin up new instances of the application to create more room for workloads.
A monolithic application is deployed as a single unit behind the load balancer. All you need to do is add more resources as transaction volume increases. However, the components of this monolith often scale inter-independently, so you might need to deploy more resources for the entire application even if you only experience demand for one individual component.
On the other hand, a micro services based application comprises a collection of loosely coupled services built to run on a mix of platforms. Because of the distributed nature of a micro services based architecture, IT teams must scale traffic differently than they would with a monolithic application. They must devise scalability strategies that protect micro services based applications from unexpected outages and help maintain fault-tolerance.
Teams must have a grasp on scalability and the reasons for it.
The scale cube, established in The Art of Scalability by Martin L. Abbott and Michael T. Fisher of AKF Partners, is a three-dimensional scaling model that illustrates three approaches to application scaling. The scale cube's X, Y and Z axes represent the three different scaling approaches. The traditional monolithic scaling method that replicates application copies falls along the X axis. Micro services based application scaling or types of scaling that break monolithic code fall along the Y axis. Lastly, Z-axis scaling involves the strategy of splitting servers based on geography or customer base in order to strengthen fault isolation.
Ways to monitor and optimise performance
End-user performance is the most important aspect of a micro services based application. Users notice slow and unintuitive application performance immediately. Even if a team uses the best technologies and tools to build a micro services based application, that IT strategy doesn't pay off if there is no improvement in user experience.
Teams should prioritise application performance and the end user's perspective to efficiently address micro services scaling issues. To prevent performance problems in a micro services based application, take advantage of an application delivery controller providing Layer 7 load balancers that adeptly facilitate scaling automation. Choose systems that are designed to handle micro services applications. Use them to monitor and optimise the performance of services in real time.
To scale a micro services based application effectively, teams must also track performance and efficiency goals. An effective monitoring system alongside a scaling strategy can help maintain optimal performance for a micro services based application.
Tracing the problems
All software application teams should take advantage of logging; however, tracing is difficult to carry out in a micro services based application. A micro services architecture comprises several services and service instances, likely spread across multiple systems.
Every service instance has the capability to write log data, such as errors and load balancing issues. The application support staff must aggregate the logs, and search and analyse them when needed.
Allocate resources appropriately
IT teams should remember that resource availability and allocation play a vital role in scaling a micro services application. Resource allocation specifically presents several challenges. The first layer, or the hardware layer, must be appropriate for the micro services ecosystem. Prioritise particular micro services for CPU, RAM and disk storage allocation.
Teams must understand scalability problems, set achievable goals with qualitative and quantitative consideration, and then apply resource-appropriate measures with a view toward performance and the end user's perspective.
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