Witness the Revolution of Cloud Architecture
Cloud is no more the cherry on the cake. It is the whole cake that more and more organizations are interested in. Cloud is not an evolution of existing technology, but an actual revolution that has taken place in the world of technology. Revolutions change the perception and redefine the meanings. Though cloud has evolved over years it is today that organizations understand the real value of deploying cloud. However, with many advancements made in the cloud technologies, cloud architecture itself has evolved.
With cloud computing, everyone can manage to develop a service with just a penny of investment. Though, the cloud architecture should also scale accordingly. With the changes in the technology and cloud server hosting, it is necessary to keep pace with the technological changes and adopt new technology to survive in the rat race. Let’s see how the architecture of cloud has revolved with time
Commodity Hardware instead of high-end Hardware
With time this has changed a lot. Some largest cloud service providers use commodity hardware. This commodity hardware is very much prone to breakdowns compared to traditional environments. However, this will not prevent you from using the cloud for company applications with high accessibility and high-performance needs. As per different resiliency criteria and distribution, it is essential to restructure the cloud architecture.
One of the essential characteristics of an excellent architecture is scalability. Scaling of architecture is altogether completely different as you need to take into consideration budget allocation, planning, systems reconfigurations, hardware purchase etc. Very steady architectures can be implemented after a period of time and maximum load can be sized which is able to support the service or application.
When we are talking about the cloud, resources utilized in the cloud are dematerialized and on demand, requests are initiated. A high level of abstraction of hardware resources can be managed by Application Program Interface (API) so that provisioning and de-provisioning of resources can be automated. Architecture so deployed should be flexible and adaptable as per the demands and should possess the ability to expand as and when required.
Resiliency in terms of Availability
“Resources are anytime available” is an assumption which is believed in traditional architecture. On the other hand, inaccessibility of resources is an anomaly. It is neither planned nor is it a desirable event. However, in the cloud, the question of unavailability of resources does not arise as commodity hardware is used. Transient failures come now and then and the architecture so deployed should have the ability to handle such failures. In case of uncertainties, it should be programmed to respond properly and the transaction should be completed. In short, it should be resilient enough. In progressive cloud computing architectures, to create deliberate failures, some agents have been deployed to carry out this activity in production environments to make sure applications respond properly. To gain resilience, a system of queues should be implemented so that the components of the application is coupled loosely and is autonomous.
Distribution and Decomposition of Performance
Technology has some inherent performance constraints like commodity hardware, resource sharing or non-proximity of resources, cloud architectures being sensitive to it. Methods which were earlier deployed to release the stress of the resources such as increasing the bandwidth, the number of IOPS, size of the RAM or the processor speed are no longer effective. Scaling up in the cloud is very limited and sometimes unmanageable. Instead of focusing on strengthening a single unit of architecture, decomposing the architecture in multiple modules established across various nodes should be given due importance.
If by increasing the nodes and requests the performance remains stable, then and only then the system is considered scalable. This scenario was prevalent in traditional architecture. Scalability is termed as a function of performance. Today, it is reverse of the ratio and the scalability should be scaled up to achieve better performance results. To achieve better performance, computational cost should be subdivided. This approach should be applied on application layer as well as on data layer by sharing of a database which enables distribution of the database and then ultimately the load on multiple nodes take care of managing specific portions data. If cache systems are used wisely at all levels, the performance of the architecture can be improved certainly.
MTTR instead of MTBF in Reliability
The reliability of architecture is measured with the concept of MTBF i.e. Mean time between failures. In other words, the reliability of a system depends upon how extensive the period is. However, this can’t be achieved because of commodity servers. In that sense to review the concept, connect reliability to resiliency. Cloud architecture is reliable when you achieve a lower MTTR which means Mean Time to Repair. If MTTR is zero or close to zero, this ensures that the organization has a reliable architecture and is resilient to any failure.
Capacity Planning in terms of Scale Unit Planning
In traditional architecture, capacity planning was designed in order to estimate the sizing which is needed to handle the maximum load so that the system has the capability to react proactively taking into consideration the worst situations as well. Resources are wasted for the most part of the system’s lifecycle. On the other hand, in cloud architecture the capacity planning is enabled to regulate the scale unit that is scaling up and down of the load, well may be automatically.
Fundamental concepts of the architecture which are capacity planning, scalability, reliability, performance are profoundly revised. We have witnessed how cloud architectures have diverged from everything established in the past as it is inevitable to respond to changing scenarios. Two golden rules of properly designed cloud architecture are “Enable Scaling” and “Expect Failure”. Half success is already achieved if cloud architecture is well designed and implemented.