As indicated previously, the traditional applications need to be accordingly modernized in order to reap the cloud benefits. The scalability and other requirements of modern applications need to be inscribed within the application. There are certain programming languages and architectural patterns in order to attach non-functional requirements (NFRs) into applications. The traditional applications typically use a single database to store all the application information. This database provides the information stored in it to various application clients (users as well as other application components) on a need basis. However, with the data explosion, the conventional databases could be scaled up.
The scale-out (horizontal scalability) of SQL databases is beset with a lot of challenges. However, due to the massiveness of cloud infrastructures, cloud databases have to be designed and developed using new database types, such as NoSQL, NewSQL, in-memory, and in-database databases for data storage and analytics. The object storage is very popular in the cloud era. Every cloud service provider is betting and banking on cloud storage to meet the fast-rising storage needs. Apart from databases, there are enterprise-grade data warehouses and data lakes. Data storage options are on the rise. Cache storage is one such which is garnering a lot of support. Furthermore, there are distributed filesystems, such as HDFS for big data storage and analytics. There are database abstractions on filesystems in order to provide several possibilities for developers, database administers, and businesses. Besides, there are backup and archival options for data in order to ensure data and disaster recovery (DR). The following diagram vividly illustrates where and how cloud application architecture deviates and differs from traditional applications. With multi-channel, device, media, and modal clients, cloud applications are being methodically advanced:
Cloud applications are distinctively different. Increasingly, applications are service-oriented with the faster maturity and stability of service-oriented applications. In the recent past, there have been further refinements and optimizations in order to tackle newer requirements. Polyglot programming is picking up. That is, there are several programming and script languages to bring forth cloud applications as a dynamic collection of microservices. In addition, there is a myriad of database management systems. Each database type is appropriate for certain application needs. Thus, the flexibility of linking multiple technologies, tools, and techniques for bringing forth cloud applications is being facilitated by the most popular microservices architecture (MSA). With the widespread adoption of containers (Docker) as the highly optimized application holder and runtime environment, there is a sharp convergence of multiple technologies in order to enable agile and accelerated software engineering, deployment, delivery, and management. Multi-container and multi-host cloud applications spearheaded and shepherded by the containerization movement is the talk of the town.
In short, with the cloud embarkation, the widely deliberated quality of service (QoS) and quality of experience (QoE) factors and facets of next-generation applications are being accurately accomplished. The cloud infrastructures are being astutely tweaked in order to tackle brewing challenges at the infrastructure level. Another prominent design requirement is to design cloud applications to handle the latency issue. That is, fault tolerance is one such important factor for cloud applications, platforms, and infrastructures. The cascading effect of failures and bugs needs to be arrested in the budding stage itself. As clouds are being built on commodity servers, the failure rate is quite high. Besides, there can be network congestion/outages, resource conflicts, request contentions, and IOPS challenges for storage systems. Furthermore, there can be hardware and software failures. Cloud environments are becoming hugely complicated, and hence viable complexity mitigation and moderation techniques need to be in place. Systems have to come out gracefully from any kind of constricting and cascading issues and limitations. A popular design pattern to address latency and failure is the request/response queue, where requests and responses are stored in queues. Also, cloud and application interfaces have to be highly intuitive, informative, and instructive. The user experience has to be maintained even if cloud resources and assets are not responsive.