18150615. INTENDED STATE BASED MANAGEMENT OF RISK AWARE PATCHING FOR DISTRIBUTED COMPUTE SYSTEMS AT SCALE simplified abstract (VMware, Inc.)
Contents
INTENDED STATE BASED MANAGEMENT OF RISK AWARE PATCHING FOR DISTRIBUTED COMPUTE SYSTEMS AT SCALE
Organization Name
Inventor(s)
Daniel James Beveridge of Valrico FL (US)
INTENDED STATE BASED MANAGEMENT OF RISK AWARE PATCHING FOR DISTRIBUTED COMPUTE SYSTEMS AT SCALE - A simplified explanation of the abstract
This abstract first appeared for US patent application 18150615 titled 'INTENDED STATE BASED MANAGEMENT OF RISK AWARE PATCHING FOR DISTRIBUTED COMPUTE SYSTEMS AT SCALE
Simplified Explanation: The patent application describes a method for updating a system's configuration based on risk policies, by determining update timings and modifying manifest files to trigger updates.
- The method determines update timings for compute stack entities based on risk policies.
- Manifest files are modified at the determined update timings to trigger updates.
- Updates are performed by a host monitoring the manifest files.
Potential Applications: This technology can be applied in cloud computing environments, data centers, and network security systems to ensure timely and secure updates based on risk policies.
Problems Solved: This technology addresses the challenge of managing and updating system configurations in a risk-aware manner, ensuring that updates are performed according to predefined policies to mitigate potential risks.
Benefits: - Enhanced security through timely updates based on risk policies. - Improved system performance by ensuring that updates are carried out efficiently. - Simplified management of system configurations by automating the update process.
Commercial Applications: Risk-aware updating technology can be utilized by cloud service providers, cybersecurity companies, and IT departments to enhance the security and performance of their systems while reducing the risk of vulnerabilities.
Prior Art: Prior research in the field of risk-based system updates includes studies on automated update mechanisms and risk assessment frameworks for IT systems. Researchers have explored the use of machine learning algorithms to predict system vulnerabilities and prioritize updates based on risk levels.
Frequently Updated Research: Ongoing research in risk-aware updating focuses on developing more advanced algorithms for determining update timings based on dynamic risk assessments and integrating machine learning techniques for predictive maintenance in system configurations.
Questions about Risk-Aware Updating: 1. How does risk-aware updating differ from traditional update mechanisms? Risk-aware updating takes into account predefined risk policies to determine when and how system configurations should be updated, whereas traditional mechanisms may not consider risk factors in the update process.
2. What are the key considerations for implementing risk-aware updating in a complex IT environment? Implementing risk-aware updating requires defining clear risk policies, establishing reliable monitoring mechanisms, and integrating automated update processes to ensure timely and secure updates.
Original Abstract Submitted
An example method of risk aware updating of an intended state configuration system is provided. The method generally includes determining, for each of one or more risk policies, for each of corresponding one or more compute stack entities associated with the risk policy, an update timing for updating the compute stack entity based on the risk policy. The method further includes modifying, for each compute stack entity of the plurality of compute stack entities, one or more manifest files at the determined update timing for updating the compute stack entity, wherein modifying the one or more manifest files causes the compute stack entity to be updated by a host monitoring the one or more manifest files.
