Improving Hybrid Cloud System Resiliency with Generative AI

As businesses increasingly adopt digital transformation and migrate workloads to cloud platforms, their systems naturally become hybrid in nature. While hybrid cloud architecture offers flexibility and scalability, it also presents challenges in maintaining highly resilient systems. System outages can have a significant impact on business operations and revenue. Studies have shown that the frequency of major outages has either remained constant or slightly increased in recent years, and the financial impact of these outages has risen significantly.

There are several factors contributing to the increased impact of outages. The accelerated rate of changes in systems, which is necessary for business agility, often leads to outages. In addition, the lack of skills in managing hybrid cloud systems and the challenges with network and infrastructure components can further exacerbate the impact of outages.

Mitigating Outages with Generative AI

In order to address these challenges and improve the resiliency of hybrid cloud systems, organizations can leverage generative AI along with traditional AI and automation techniques. Generative AI can help in various ways:

Release Management

Generative AI can assist in tracking system changes, summarizing them, and connecting them to specific work items or user stories. This can help in identifying the impact of changes and facilitating rollbacks if necessary.

Toil Elimination

Generative AI, combined with automation, can streamline the process of deploying fixes and reduce the time required for phase gate decision-making, such as reviews and approvals.

Virtual Agent Assist

Virtual agent assist powered by generative AI can provide IT personnel with answers to common incidents and assist in issue resolution by summarizing knowledge management systems. This can significantly speed up problem resolution.

AIOps

Generative AI infused AIOps can create executable runbooks for faster issue resolution based on historical incidents and current system health. This can help predict and proactively address potential issues, improving mean time to resolution (MTTR).

Challenges and Next Steps

Implementing generative AI for improving system resiliency also comes with challenges. These include selecting the most appropriate Large Language Model (LLM), ensuring data lineage transparency, and providing training to IT professionals on prompt engineering and generative AI concepts.

However, generative AI, when combined with traditional AI and automation, has the potential to significantly enhance productivity in IT operations and make hybrid cloud systems more resilient. By mitigating the impact of outages, businesses can ensure uninterrupted operations and maintain customer satisfaction.

CTO and Vice President, IBM Consulting

Summary

Hybrid cloud systems pose challenges in maintaining resilience due to their complex nature and multiple service providers. Major outages have remained stable or slightly increased in recent years, with a significant impact on revenue. Generative AI can mitigate these issues by assisting in release management, eliminating toil, providing virtual agent support, and enabling proactive incident resolution with AIOps. However, implementation challenges exist, including selecting the right language model and providing appropriate training. Despite these challenges, leveraging generative AI in combination with traditional AI and automation can greatly improve hybrid cloud system resiliency.

FAQs

What is generative AI?

Generative AI is a technology that uses machine learning models to generate new content, simulate real-world scenarios, and make creative decisions. It can be used to automate tasks, generate reports, and provide assistance in various domains.

How can generative AI improve hybrid cloud system resiliency?

Generative AI can assist in release management by tracking changes, summarizing them, and connecting them to specific work items. It can also eliminate manual interventions in the deployment process, provide virtual agent support for issue resolution, and enable proactive incident resolution through AIOps.

What are the challenges in implementing generative AI for hybrid cloud systems?

Challenges in implementing generative AI include selecting the most appropriate language model, ensuring transparency in data lineage, and providing training to IT professionals on prompt engineering and generative AI concepts.

How can generative AI benefit businesses?

Generative AI can bring significant productivity gains in IT operations, improve system resiliency, and mitigate the impact of outages. By ensuring uninterrupted operations, businesses can maintain customer satisfaction and minimize financial losses associated with system outages.

What is hybrid cloud architecture?

Hybrid cloud architecture refers to an infrastructure that combines on-premises infrastructure with public and/or private cloud services. It offers flexibility and scalability by leveraging the benefits of both cloud and on-premises resources.