To meet the rapidly changing customer needs of the digital world, IT departments must help their organizations stay ahead of the curve and stay within budget. For example, to enable IT to increase agility and increase the speed at which services and innovations are delivered, they are migrating more and more workloads to the public cloud. In addition, in the spirit of efficiency and accessibility, companies are abstracting applications from the Infrastructure as a Service (IaaS) model and rebuilding it into a Platform as a Service (PaaS) model. Finally, enterprise architects are busy using microservices and containers to rebuild the entire application.
While a flexible IT environment is good for application development and deployment, it presents significant operational challenges. For example, in the case of abstracting applications from the IaaS model, if these applications are working well, the infrastructure resources (CPU, memory, I/O, network) they need must be available whenever the application needs it. The bigger challenge is the scale that needs to happen. The new IT reality involves supporting hundreds of thousands of containers instead of processing thousands of workloads or workloads per virtual machine (VM). This reality is beyond the capabilities of the staff.
People cannot solve the problem of IT scalability. In other words, rather than trying to manage various environments through endless low-level r-specific rules, it is better to abstract the environment into general concepts and behaviors that are simple and easily expandable. Abstraction can help hide the clutter of the management environment and expose the key factors necessary to control and maintain a healthy environment.
Appropriate abstraction can provide four key benefits:
(1) Scalability: By collecting and analyzing the information needed, the management platform can scale in large environments while eliminating large amounts of data and all its challenges.
(2) Simplicity: Abstraction simplifies management by allowing users to manage heterogeneous environments without having to understand all of the underlying complexity. For example, users can manage Microsoft Azure just like managing an AWS cloud platform. In addition, if environmental resources are more unique, more rules need to be defined and maintained, which means that the correlation analysis will be more complicated. However, abstract analysis only needs to deal with one resource (such as disk I/O) rather than many different versions of different device models.
(3) Mobility: After purchasing a new platform, most users immediately integrated a large number of platform-specific proprietary tools, which made it more difficult to eventually migrate the business to the new platform. For example, abstraction can ease platform locking and allow users to easily migrate from VMWare to OpenStack.
(4) Appropriate abstraction provides a way to compare different resources: CPU, memory, IOPS, network and storage latency, response time, TPS, heap size, connection pool size, etc., and make the necessary trade-offs to ensure the application performance.
Abstraction is especially useful when rebuilding an application to take advantage of microservices or edge computing. When using a single-chip application, it is fairly straightforward to determine the correct size of the virtual machine and which host and storage device the virtual machine should be placed on. However, for microservices, if the application is a collection of containers running on multiple virtual machines or bare metal, there are many questions to answer, such as:
How many containers do you need to meet your application needs?
How big should the container be?
How many containers can I accommodate in a node?
Should the container be expanded vertically or horizontally?
Should the node be extended vertically or horizontally?
Where should the node be placed?
How close should the containers be to each other?
How close should nodes be to each other?
How much underlying infrastructure do you need?
However, if there is no proper abstraction, it is virtually impossible to try to answer questions continuously and simultaneously like this. If you need to consider edge computing, it includes real-time processing of terabytes of data generated by millions of IoT sensors.
This can create problems that are more difficult to solve, such as:
How many processes do you need to process the data?
How close are these processes to the data?
How close should each be?
How many processes are running on the node?
How much data can I store on a node?
When enterprises transform a single application into microservices or edge computing, a unified autonomous platform with general abstraction and general analysis can achieve scalability. Through semantic integration control of all technology silos and required management functions, autonomous technology can help IT teams better understand who will consume what, and who, and how all environmental dimensions affect the quality of service for all running workloads. A unified, autonomous platform can also manage and maintain the entire environment as people expect, resulting in greater IT efficiency.
While many IT organizations have modernized their infrastructure assets, many IT departments have also struggled to provide agility, resiliency, and scalability. To effectively monitor, control, and optimize today's complex environments, next-generation IT must rethink the way it operates.
More specifically, this repositioning requires a workload automation platform that relies on abstraction to organize infinite detail and automate the decision-making and control of IT assets to ensure application performance. By leveraging intelligent analytics to enable continuous real-time decisions driven by abstract captured knowledge, IT can ensure application performance while reducing costs and maintaining compliance with business policies.
IT faces a decisive moment to push and implement the digital business agenda, while facing the exponential complexity challenge that staff can't manage. Point out the appropriate abstraction that plays a central role in today and in the future. IT teams that support and incorporate autonomous technology with real-time analytics can control any type of workload on any infrastructure at any time. What is the result? IT can manage new workloads securely and seamlessly while also planning future infrastructure changes and trends in the cloud and in the future.
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