Cloud Computing Resource Recycling Prioritization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for recycling computing resources in cloud-based environments result in substantial downtime and inefficiencies, as they often require taking entire computer systems offline for wiping and reinstallation, leading to lost revenue and excessive resource usage.
Innovation Solution
A recycling prioritization system that determines priority rankings for computer systems based on metrics such as usage duration, turnover rate, and machine learning estimates to efficiently allocate cloud-based resources for background updating and recycling, minimizing downtime by prioritizing systems likely to be vacated soon.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional wiping and reinstallation methods are used to recycle computing resources, then data sanitization is achieved, but computer system downtime increases substantially
Solution Approach 1:
The system performs preliminary actions by creating and staging updated firmware and data images before the actual recycling event. Virtual copies of computing resources are prepared in advance with updated firmware, so when a recycling event occurs, the transition is immediate rather than requiring time-consuming wiping and reinstallation operations at that moment.
Solution Approach 2:
The patent creates virtual copies of computing resources that can be used while the physical system is being recycled. A virtual machine or copy maintains service continuity, allowing the physical hardware to undergo recycling operations without causing downtime. The copy serves as a placeholder that preserves service availability during the recycling process.
2Reliability
If entire computer systems are taken offline for recycling operations, then complete data sanitization and firmware updates are achieved, but revenue loss increases
Solution Approach 1:
Firmware updates and data sanitization preparations are performed in advance during periods when the system is less critical or using spare capacity. Recycling prioritization identifies systems that are likely to be vacated soon and prepares them beforehand, so when recycling occurs, minimal downtime is required, thereby reducing revenue loss.
Solution Approach 2:
Virtual copies allow the system to remain online and generate revenue while the physical recycling operations proceed on separate virtual instances or during maintenance windows. The copy maintains business operations, preventing revenue loss even while recycling activities are underway.
3Productivity
If cloud-based computing resources are used for recycling operations, then recycling capability is enhanced, but resource consumption increases
Solution Approach 1:
The system recovers and reuses computing resources efficiently by prioritizing recycling of systems that are soon to be vacated anyway. Instead of recycling all systems uniformly, the patent identifies and recycles only those that will become available soon, thereby maximizing recycling capability while minimizing the consumption of cloud resources needed to perform recycling operations on actively used systems.
4Reliability
If recycling operations are performed on all computer systems uniformly, then comprehensive updates are achieved, but efficiency decreases
Solution Approach 1:
The patent applies different recycling priorities to different computing resources based on local conditions such as usage patterns, likelihood of vacating, and business criticality. Instead of uniform recycling, systems are categorized into priority levels, and recycling operations are concentrated on high-priority systems that are most likely to be vacated soon, achieving comprehensive updates where needed while maintaining efficiency.
Solution Approach 2:
The system performs preliminary assessment and prioritization of computing resources for recycling. By identifying which systems are most likely to be vacated in the near future, the system prepares recycling operations in advance for those specific systems, achieving comprehensive updates on priority systems while avoiding wasteful consumption of resources on systems that will remain in use longer.
Data Source
AI summary
Example embodiments facilitate prioritizing the recycling of computing resources, e.g., server-side computing systems and accompanying resources (e.g., non-volatile memory, accompanying firmware, data, etc.) leased by customers in a cloud-based computing environment, whereby computing resources (e.g., non-volatile memory) to be forensically analyzed/inspected, sanitized, and/or updated are prioritized for recycling based on estimates of when the computing resources are most likely to require recycling, e.g., via background sanitizing and updating. Computing resources that are likely to be recycled first are given priority over computing resources that are more likely to be recycled later. By prioritizing the recycling of computing resources according to embodiments discussed herein, other cloud-based computing resources that are used to implement computing resource recycling can be efficiently allocated and preserved.