Cloud technology: A synergistic environment for KM and generative AI
Finally, cloud native involves the deployment of applications that were designed specifically for the cloud. “Lift and shift tends to be the most favored initial approach but should only be seen as a starting point in the journey,” advised Benjamin. “Ultimately, the goal should be to move toward modernization and cloud native because they provide the greatest scalability and performance gains.”
Another key factor for success is to select cloud architecture options and deployment models that are well-suited for the business, noted Benjamin. For example, depending on the use cases leveraged by an organization, a combination of on-prem, public cloud, and private cloud may be the best solution. Most organizations tend to use some or all aspects of this hybrid cloud approach to manage applications and data.
Understanding cloud costs can be a challenge. Multiple cost models are used for cloud computing, including pay-as-you-go, which is based on consumption of services such as storage and networking; a subscription-based model, in which the customer prepays for a specified package of services; reserved instances, intended for long-term use; and spot instances, a discounted version of pay-as-you-go. “Companies need to be highly mindful about the total cost of ownership, especially in the realm of current and future resource demands,” Benjamin continued. “Establishing ongoing cloud cost management, accountability, visibility, and alerting is also critical.”
Higher-than-expected costs can result from a number of factors, including engaging more cloud capacity than is needed, lack of centralized control, and inadequate collaboration across departments. If certain workloads vary in a predictable pattern, required usage can be reduced, and if services included with one cloud provider are not needed for a certain function, that function can be moved to a lower-cost provider. Using an observability platform can help mitigate the impact of unpredicted costs.
Monitoring the cloud
The various elements of cloud-native applications are interconnected and dynamic—they generate a lot of data about their own activities. Therefore, performance monitoring is also very data-intensive. If problems arise, they need to be detected in real time so they can be corrected before they start affecting downstream data or processes. Observability tools are designed to monitor performance and to flag issues as they are occurring, or, in some cases, are about to occur.
Chronosphere was founded by the team behind an open source project for monitoring cloud-native performance and now offers a SaaS solution. “Organizations need to find out the source of problems quickly,” said Rachel Dines, head of product and technical marketing at Chronosphere. “Today’s pace of business cannot wait for batch processing—analyses need to take place in real time.”
Understanding the unique nature of native cloud environments is important because the many computing elements, referred to as containers, need to be orchestrated. “This environment changes constantly, and there are many points at which performance needs to be monitored,” Dines continued. System failure in a cloud environment also includes the same issues as on-prem environments, such as data breaches, hardware failure, software corruption, and power loss.