Cloud Performance Engineering for AI Deployments

Along with the extensively discussed technological trending topics like AI, hyper-automation, blockchain, edge computing, and hyper-automation, Cloud computing is the central component in the upcoming years of various firms' IT strategies.

These days the benefits of flexibility, alertness, fast speed, and cost efficiency have become essential for various CIOs. 

A few businesses are currently working on refining their overall IT cloud strategy. They take fundamental considerations such as which plan of action to opt Whether they should go for public, private, or a mixture of both. Others have progressed even further. They are working with full efforts to modify their applications. Moreover, they are taking advantage of different PaaS capabilities provided by the cloud to maximize benefits.

Challenges Faced by Cloud Computing

Such firms can also overcome the essential issues of Cloud computing, such as safety, data coherence, flexibility, and functional coherence, by focusing on the main elements of the cloud: simplifying the cloud performance.

The frequent question in the area of cloud performance engineering is which execution can be achieved by the relocated and modified system in comparison to a pure on-site landscape. Is it going to be less, similar, or even higher and better performance? 

Cloud Scalability Options

Many experts claim that in dynamic scalability possibilities in the cloud, it is simple to grow the system linearly just by amplifying the number of machines. It is unquestionably the initial Step that should be observed. Same as on-site systems, the vertical scalability capabilities are first employed traditional hardware capacities like CPUs and Read-Only-Memory raised.

However, larger firms' IT systems with high output, access rates, and peak loads are reaching the breaking point. When ambitious expansion strategies combined with disorganized application might result in IT needs that exceed Moore's Law. Thus, requisite hardware is not yet accessible.

Next-Generation and Upgradation of Cloud Hardware 

On one side, CIOs can aspire that the upcoming generation of hardware is ready to enter the market and can be provided to its users soon. On the other side, the subject of horizontal scaling has also achieved a lot of traction. Different from increasing servers for similar sections of the application. In many situations, this needs substantial changes in the application itself, like on-site systems. In particular, databases need an elaborated concept permitting the data to persist autonomously across many servers. 

In this situation, there might be an alternative for applications. That is an increasing number of read-only transactions. To gain execution goals in the absence of "real" horizontal scaling, implementing such PaaS offerings can be a solution. Such as, Microsoft provides the so-called Hyper-scale service for SQL databases, which dynamically scales the computing power through caching techniques and divides it horizontally to read replicas used as images of the database. AWS Cloud also provides read copies for RDS MySQL, PostgreSQL, MariaDB, Amazon Aurora, and Oracle Cloud. They depend on their popular Oracle RAC.  

Classical Approach

There are other possibilities beyond vertical and horizontal scalability offered by Cloud performance engineering. There are still many well-known options available in the cloud compared to the availability on-premise. 

The most common classic approach is to boost your indexes, which helps to determine I/O performance for over 80% of your performance activity. However, if any one indicator is missing, the performance of the entire IT system may suffer. As a result, cloud performance engineers should always prioritize database indexing.

In addition, topics related to batch processing and session handling, the definition of maximum batch sizes, connection durations, read frequencies, idle times, and possible connection pooling of, for example, SSL connection, can be decisive for the performance of the system. Due to this, your interface partner's CPUs from being overloaded by opening a new connection for each HTTPS request.

It says that it is desirable to reduce the number of requests to the database and actively apply caching mechanisms. Similarly, the number of instances, the number of threads, and the hardware itself can be varied until a self-defined level of perfection is reached. 

Elasticity

In cloud computing, scalability is just one aspect of performance engineering. One of the features that the cloud promises are fully automated elasticity, allowing resources to be dynamically adjusted to meet every demand. The hurdle is that on-premises applications are usually designed with static environments in mind, so they need to respond to dynamic scaling first.

As a result, it requires defining and testing different test scenarios for the cloud. Attention should be on the interaction between the cloud and the applications. An essential metric is how well the application responds to the dynamic scaling of the cloud, whether it doesn't lose connections or experience other unusual behavior, and whether it doesn't suffer from the usual performance degradation that occurs on a system.

Additional Features

Cloud service providers present numerous new possibilities to quickly create test environments and analyze and evaluate performance KPIs at runtime. The best way to cover planned testing concepts in the cloud is to combine existing testing tools with new testing options in the cloud.

It is always preferable to consider a complete rebuild of old applications instead of heavily customizing an existing application. This approach works when various functional, non-functional, and technical requirements are not implemented in the current application. 

Support of IT Organizations

IT organizations are playing a vital role here by supporting them in the best possible ways. They support all the activities and improve the performance of the cloud, which has been recognized and benefited by agile processes and architectures of modular containers and not only to them but also to the new concept that is time-to-market, for instance, CI/CD pipelines. Most of the time, it is beneficial to implement such ideas beforehand before opting for the cloud. 

Conclusion

Lastly, even though shifting to the cloud offers multiple opportunities and benefits, cloud performance engineering is a challenge that needs to be defeated by approved and new methods. The feature of automatically usable scalability in the cloud has to be opposed by many large-scale companies. The budget and time frame plan customization needed during the implementation. It is because well-planned, high-level supervision is highly recommended to obtain the best possible reaction among the users and facilitate them in the best possible way. Apart from this, there are other activities of testing. Those are data integrity, security, and resilience. These are significant to provide exclusive performance to the world. 

A good connection between all the teams involved in this regard, like the CEO, CIO, architects, cloud experts, and performance engineering specialist, is essential to achieve the shift to the cloud and successfully convey this new topic, cloud performance engineering, to the world.