Overview of Managing Cloud Native Workloads

Gursimran Singh | 12 December 2021


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Introduction to Cloud-Native Development

For every organization, the application deployment process is a crucial part. As per the traditional approach, the deployment process will complete on the on-premises(legacy). But that approach is not suitable to deploy applications for large-scale access. If an organization needs to grow, it must adopt the changes to remain in the cut-throat competition era. To make the deployment process easy and run the application in high availability, cloud-native development is necessary. The term cloud-native is used to describe the container-based environment.

A container includes the services, libraries, and all the dependencies required to run an application. In simple words, all the benefits and dependencies of an application are packaged in a container deployed as a microservice. Such microservices are managed on elastic infrastructure by using continuous delivery workflows and agile DevOps processes.

Companies that adopt the cloud well bring new capabilities to market more quickly, innovate more easily, and scale more efficiently—while also reducing technology risk. Source: Accelerate Digital Transformations through Cloud

What are the Cloud-Native Storage Fundamentals?

When we talk about the cloud-native development and deployment of an application, we should also take care of the storage part. An application's deployment process will not accomplish until it does not have any storage attach to the application. As the application gets traffic and data from different sources, it is necessary to attach storage during the application's deployment to save, and desire operations can perform on that data. Some storage fundamentals can be helpful to decide some specific parameters:


An application runs to provide a service. To run an application, it needs resources to configure as per requirements. The scaling depends upon the resources of an application. Application is scale-up and scale-down as per the usage of resources. Here the scaling means not only to scale the application by running its multiple replicas, but the resources provided to an application should also be scalable.


If the application is running, then failures will also occur due to any interruption. After a failure, the application's new replicas may start on another node or in a different zone or data center. There should be a data service that can provide access to the application whenever it comes up after restart in such cases. The failure can be in a microservice, or it can be a node failure. To make data available in such situations, we need a data service to support microservices' resiliency.


Multitenancy provides the sharing of resources to run multiple applications. In the case of a cloud-native premise, a variety of applications run on the same infrastructure. It means, applications with different dependencies and requirements run on the same infrastructure and share the same resources. In such cases, the isolation should take care of for applications and their data and security.


Tiering can be done as per the requirement of the application. In simple words, tiering can be prioritized for a particular application according to its need. For example, the tiering can be done by taking care of required access by applications. Some applications may require high-level access, or some may need low-level access. The cloud tiering includes data lifting from on promises to cloud-based upon the defined policies for access.


Mobility is necessary not only for application workloads but also for data. Efficient data mobility must be there for application mobility for efficient migration and replication.
Curious to know why Cloud-Native Applications are necessary?

What are the Cloud Native Development Services?

In data services, two things are essential to keep in mind. These things are RPO and RTO. RPO stands for recovery point objective, which means how far back you need to recover your data. RTO stands for recovery time objective, which defines how long it takes to recover data from these services. The data services that help to achieve these objectives are:

Tape Backup

In the case of traditional tape backup, it typically takes days before we can get our data and have your application up and run. This traditional approach is time-consuming in terms of other approaches.


Disk backup, Cloud backup also takes hours before we can populate our data and the application can be up. The backup will take how much time to complete. It depends upon the size of the data.


Snapshots depend upon the architecture and how much replication time objective you can achieve. Schedule engine is configurable for a particular timestamp that defines RPO, but how long it takes to recover it depends upon the architecture. If it involves data copy, it takes hours to bring up our application and recover the data.


In replication, the data recovery and application uptime are dependent upon the same factor as in snapshots. The data will replicate and probably be a few minutes behind to reflect on the website. The application can bring up within a minute once the failure is detected. Replication is beneficial to create multiple copies of data objects of a database which makes the data distribution efficient.


The mirroring creates copies of a database to a geographically different location. Mirroring provides nearly zero recovery point objective and zero recovery time objective because the data synchronously gets into both places. The other plus point of mirroring is that once the failure occurs in one side or one zone, it can shift immediately in the second zone. From all the above data services, snapshots technology forms the base or core of the data services, and backups are typically on the top of snapshots to be consistent. Replication also possible on the top of snapshots as continuous replication enables. Still, if the recovery point objective requirement is 10-20 minutes, then snapshots replication will be a good option.


As we know, data backup is a crucial thing to handle. One should take care of the need for data backup. In simple words, the recovery point objective can explain by keeping in mind the data size, architecture, and data services because the recovery time objective also depends upon these factors.

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