Aiops Are Required for Hybrid Cloud Systems Because Data Can Be Lost in Transit 

Lily Simpson
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The complexity of modern hybrid cloud systems will only grow, making it impossible for information technology to tune or fix problems. But AI is capable of doing so. 

The benefits of hybrid cloud systems are numerous, and it is clear that their use will only grow in the future. However, the complexity that comes with these benefits, which include adaptability, scalability, and reliability, comes at a cost. This complexity is due to the large number of components included in hybrid cloud systems, as well as their dispersed locations. The interplay of these components is where the potential for transitional losses lies: losses of efficiency, accuracy, and the benefits that businesses were promised when they chose hybrid cloud architecture. 

This is where artificial intelligence comes in and takes over the time-consuming tasks. The primary goal of artificial intelligence (AI) is to recognize patterns and solve problems that a human brain cannot comprehend. It can address these extremely difficult problems by removing the operational impediments that exist in the hybrid environment. AIOps refers to the use of artificial intelligence (AI) to analyze and improve business operations. 

Every business that migrates its operations to a hybrid cloud platform will face unique challenges during the transition. The examples below should provide a high-level understanding of some of those aspects, as well as the role that AIOps plays in making the transition easier. 

Computer programs and systems from the past 

Established businesses have built their technical platforms on systems and applications that have been in use for years, if not decades. Some of these applications and systems are still in use. Legacy, proprietary, or core applications cannot always use genuine cloud operating models in their current configuration. This is particularly important for mission-critical applications. 

The hybrid model is designed with the knowledge that some components simply cannot be stored in the cloud. An on-premises data center that houses all of its resources (hardware, software, and networking) in a single location and is reliant on a large number of interdependencies with other applications housed within the same system may present a challenging transition. When certain applications are moved to the cloud, their interdependencies can cause deployment delays and stability issues. 

Legacy apps may contain a subcomponent that attempts to access services that do not exist in the public cloud or have not yet been transferred there. As a result, the legacy applications fail because they receive a “nobody here” response. Even in this case, the cloud-hosted microservices applications have the potential to be transient. They ascend to the top, pause for a few seconds, and then begin their descent again. They are also constantly on the move, going wherever the resources are to ensure maximum utilization and efficiency. Because a legacy application was developed with the assumption that the required service can always be found in a specific location, the legacy program may have difficulty utilizing a dynamic service.¬†

Despite the fact that these issues are typically difficult to identify, AIOps can sift through the mountain of log data that you have to find the source of the problem. It may be capable of providing potential solutions to the problem, and with continued use, these potential solutions should improve. 

Why Are Automation and Artificial Intelligence Required for IT Operations? 

To ensure a successful digital transformation utilizing automation and artificial intelligence in your business, you should follow these four stages. 

Stack components that change 

It would be ideal to have a server architecture in which all components carried the same branding. Unfortunately, the volatile technological environment has resulted in the emergence, fall, and merger of numerous technology organizations. A company’s server stacks may contain components from any of a half-dozen to six distinct brands. By the way, that’s just the hardware.¬†

The following is one possible outcome: Assume you are a large corporation. Your company’s growth could happen naturally or as a result of an acquisition. The software that powers your customer interface was created by a company that you later purchased, and it is hosted on Amazon Web Services. The most important datasets for your company are stored both locally and in Microsoft Azure. Because of the company’s rapid growth, its web services are disorganized.¬†

In either scenario, the most important factor to consider is how the various brands and services will communicate with one another on the local network or in the cloud. Any change to either environment may result in faults or unexpected behavior involving multiple components in a way that human analysts find difficult to comprehend. 

Again, AIOps shines when confronted with such complexities. An adequately prepared AI operations system can be knowledgeable about all of the various products and platforms, as well as up to date on changes to their configurations, to provide advice on even the most difficult installations. 

Keeping experience within departments and removing silos 

In contrast to the previous two examples, which are mostly based on software and hardware, the problems caused by departmental silos are directly related to human fallibility. Departments working together on a hybrid cloud platform using their various tools may provide a fragmented view of all activities, even within the same company. 

This is especially true if each department uses a different cloud service. Even with the assistance of a detailed procedure manual that has been prepared and kept up to date by all departments, it appears that the issue of “I can’t do my job until that other person does” persists.¬†

The difficulty in recalling past events makes the story more difficult to follow. A process manual may contain a knowledge database, but in most cases, it is the employee with years of experience who understands when to apply that knowledge. When someone leaves an organization, they are usually replaced by someone who does not have the same level of expertise or approaches problem-solving in a different way. 

With the assistance of AIOps, artificial intelligence can learn how to address these issues, and it can then become the subject matter expert who advises humans on how to fix the things that the AI does not have explicit authorization to touch. The impact of a senior employee leaving the company is reduced once they have left. As problems beyond the current capabilities of AI are solved, the dataset used to train and retrain the model can be updated. This eliminates the need to update manuals and processes manually. 

Visibility in operations 

A lack of visibility into your entire data center architecture creates gaps, which increases the risk of loss during the transition. This relates to the other points that have been raised. 

Traditionally, end users had access to every layer, component, and layer of the stack (hardware, software, networking). They were able to extract various types of data from the stack, including the following: Is the central processing unit operational? Is the network link experiencing high traffic? 

Do we have access to the lock states of the database? When moving to a hybrid architecture, where you can have either reserved capacity or parts of the application run in the public cloud, the visibility required to answer all of the inquiries is significantly reduced. You will not be able to inspect the actual physical Azure rack and query it whenever it is convenient for you. You obtain the data made available to you by the public cloud provider at the time it is made available to you. 

In an ideal world, all that would be needed to monitor hybrid cloud systems would be a specialized IT team, but the nature of hybrid cloud platforms, combined with their increased speed, makes maintaining operational visibility difficult. Traditional monitoring and operational tools are simply not flexible or fast enough to function in a hybrid world because there are simply too many moving elements moving too quickly in too many locations. This number, however, is not excessive for AIOps. 

AIOps is the solution to the problem

AI can process massive amounts of data about the hybrid environment and send out messages to detect flaws along the way. For example, AI may detect a problem with a cloud service or a localized networking configuration. 

The level of analytic complexity that can be achieved by programmatically developing an AI model that touches data points in both an on-premises data center and any cloud-based services is significantly higher than that achieved by humans or even traditional methods. AIOps will collect data, analyze it, and run diagnostics on the service all at the same time. 

In an ideal world, the market will unite around a set of standards that will allow all suppliers, hardware, and software to write performance data, improved error condition handling, and other informational data required to build a complete model. This contradicts the notion that large suppliers should set up closed systems to give the impression that their solutions are more valuable than others. Large cloud providers interested in expanding their services into the on-premises hybrid cloud realm are likely to promote the development of such a standard. 

Organizations that use hybrid cloud platforms can still use AI to manage transitions for themselves, even though such a standard has not yet gained widespread acceptance.