1How to Overcome Limitations of Conventional Discovery in the Cloud
Following a few years of fits and starts, enterprise use of the cloud is growing exponentially, and wider deployment is coming as more companies adopt cloud-first strategies. IT leaders are looking for better ways to manage on-premises IT assets with newer cloud-based assets. Common early cloud use cases (e.g., development/test) aren’t mission-critical and don’t require rigorous asset management. This is changing as organizations move production workloads to the cloud. As IT migrates to the cloud, major obstacles surface. One is a lack of comprehensive visibility. In this eWEEK slide show, Walker White, vice president of data platform at Flexera, offers his expertise about the limits of conventional discovery and best practices for cloud asset management.
2Cloud Discovery Tools Are Limited to Instance Data at the OS Level
3Cloud Discovery Tools Are Typically Unable to Recognize All Applications
Conventional discovery tools are generally unable to identify specific applications running on cloud instances. When they do look at cloud instances, they may not recognize data beyond the instance level to the applications. If they are able to recognize at the application level, only the most common applications may be included while others may be missed, leaving a gap in discovery data.
4Holistic Views Are Impossible Without Integrated Cloud + On-Premises Discovery Tools
Conventional discovery tools for the cloud typically identify instance data only on the cloud and not the applications. They are also often in a format that doesn’t allow a full view when combined with on-premises data that has been identified by on-premises discovery tools. In addition, what may be discovered often contains duplicate, irrelevant and incomplete data.
5Production Workloads in the Cloud Demand Disciplined Asset Management
Cloud platform services are typically used for development and testing. Quick access to cloud-based virtual machines makes it easy for developers and test engineers to quickly “spin up” and “spin down” new machines to test software. Unlike development and test workloads that are short-lived, production workloads are subject to rigorous service-level agreements (SLAs), vendor audits and regulatory review for companies that operate in highly regulated industries, such as financial services, government and health care.
6Cloud Discovery Tools Cannot See Linux Applications
With the inability to dive deeper than the OS level and recognize applications that have been migrated to the cloud, the challenge is even more daunting with the inability to recognize the thousands of Linux-specific commercial applications that may have been migrated. Without a standard trusted and comprehensive source of all of the available applications, ensuring complete and sufficient coverage is impossible.
7Best Practice #1: Begin With the Big Picture of Cloud Asset Management
8Best Practice #2: Strive to Achieve Native Integration of Cloud Vendor Toolsets
9Best Practice #3: Remember to Collect EC2 Information Before Termination
10Best Practice #4: Normalize Discovery Data for Accurate Views
11Best Practice #5: Enrich Normalized Data With Info About EOL, EOS and Vulnerabilities
Clean, normalized data enriched with market information such as end-of-life (EOL) data, end-of-support (EOS) data and vulnerability information can enable more insights about IT asset data to enable better decisions for software licensing, procurement, software audits, IT service management, cyber-security risks and much more.