Tenant-Driven Peak-Hour Patch Override System
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Solution Overview
Problem
Existing server farm patching systems are ill-equipped to quickly address disruptions caused by software regressions, which result in inefficient use of computing resources and lack of tenant input in patch scheduling, leading to one-size-fits-all patching that can be disruptive during peak hours.
Innovation Solution
Enabling tenant administrators to initiate request-driven peak-hour builds that override off-peak patching schedules, allowing for expedited installation of patches during peak hours when performance failures occur, thereby reducing disruption and optimizing resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If patches are installed during off-peak hours according to a rigid schedule, then disruption to information workers is minimized, but the system cannot quickly address software regressions that occur during peak hours
Solution Approach 1:
The patching schedule transitions from a static, predetermined off-peak schedule to a dynamic system that can adapt between scheduled off-peak patching and on-demand peak-hour patching. The system dynamically adjusts patching timing based on real-time conditions such as software regressions, tenant requests, and system state, allowing it to resolve contradictions between maintaining stability and responding quickly to issues.
Solution Approach 2:
The system changes the time parameter of patching operations from fixed off-peak windows to flexible timing that can include peak hours when necessary. By modifying when patches are applied based on severity indicators, tenant consent, and system conditions, the system resolves the contradiction between minimizing disruption and enabling rapid response to critical issues.
2Device complexity
If a one-size-fits-all off-peak patching schedule is enforced, then system-wide coordination is simplified, but tenant-specific needs and disruptions cannot be addressed
Solution Approach 1:
The system segments the patching process into separate controllable components: a base off-peak schedule for routine patches and tenant-specific override capabilities for critical issues. This segmentation allows different tenants or patch types to have different scheduling rules, resolving the contradiction between simplified management and tenant-specific flexibility.
Solution Approach 2:
The system dynamically adjusts patching schedules based on tenant-specific conditions and requests. Tenants can provide input through service requests with severity indicators and consent parameters, allowing the system to adapt the generic off-peak schedule to meet specific tenant needs while maintaining overall system coordination.
3Reliability
If intermediate builds are installed in sequence during peak hours, then all necessary patches are applied, but unnecessary network traffic and computing resource usage occur
Solution Approach 1:
The system applies partial action by installing only the specific build containing the patch needed to resolve the performance failure, rather than installing all intermediate builds in sequence. This selective approach achieves the necessary reliability of applying the critical patch while avoiding the excessive action of installing unnecessary intermediate builds that would consume additional network traffic and computing resources.
Data Source
AI summary
A system enables initiation of request driven peak-hour builds to override “off-peak” patching schedules for updating server applications. An “off-peak” patching schedule is generated to minimize disruption from installing builds of patches. Notwithstanding the “off-peak” patching schedule, a tenant administrator initiates request driven peak-hour builds when some performance failure occurs during peak business hours. For example, the tenant administrator may generate a service request that includes incident data that is usable to identify and/or develop a particular patch for resolving the performance failure. Based on the service request, the “off-peak” patching schedule is overridden to expedite an out-of-sequence installation of a particular patch. In this way, a tenant administrator that becomes aware that some performance failure is disrupting information workers during a peak usage time-range (e.g., business hours) is empowered to initiate a request driven peak-hour build to quickly resolve the performance failure during the peak usage time-range.