Preemptive Resource Diagnostics in Disaggregated Data Centers
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Solution Overview
Problem
Traditional computing systems face challenges in performing deep health diagnostics on resources without disrupting critical workloads, leading to potential resource failures and increased costs due to the inability to interrupt or replace resources while they are in use.
Innovation Solution
A disaggregated computing environment allows for dynamic resource allocation and health check diagnostics to be performed without disrupting workloads, by identifying failure patterns, predicting resource failures, and swapping resources in real-time, enabling proactive replacement and optimization of resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep health diagnostics are performed on resources, then resource failure prediction accuracy is improved, but workload disruption occurs
Solution Approach 1:
The system segments resources into multiple interchangeable units within a disaggregated architecture. When deep diagnostics are needed, only the specific resource undergoing testing is temporarily removed from its workload, while other resources continue operating. This segmentation allows diagnostics on individual components without system-wide disruption.
Solution Approach 2:
The system introduces intermediary components including a resource manager and virtualization layer that mediate between workloads and physical resources. When a resource requires deep diagnostics, the intermediary layer redirects workloads to alternative resources or virtual representations, allowing the original resource to be tested without directly impacting workload execution.
2Reliability
If resources are replaced proactively to prevent failures, then system reliability is improved, but resource allocation complexity increases
Solution Approach 1:
The system performs preliminary health assessments and deep diagnostics on resources before actual failures occur. By continuously monitoring health metrics and conducting predictive analytics, the system identifies resources at risk and proactively replaces them with healthy alternatives from the resource pool, preventing failures before they impact workloads.
Solution Approach 2:
The system implements continuous feedback loops through health monitoring systems that track resource performance and predict failures. This feedback mechanism provides real-time information to the resource manager, enabling dynamic allocation decisions. The feedback loop continuously refines failure predictions and optimizes resource replacement timing, managing complexity through automated decision-making.
3Reliability
If resources are swapped in real-time without interruption, then service availability is improved, but diagnostic depth is reduced
Solution Approach 1:
The system dynamically adjusts resource allocation based on real-time health assessments. When resources show signs of degradation, the system can swap them out during low-utilization periods or redirect workloads, maintaining service availability. The diagnostic process is dynamic, adapting test intensity and duration based on resource criticality and current workload conditions.
Solution Approach 2:
The system performs preliminary health checks continuously in the background before failures occur. When resources show early signs of problems, deep diagnostics are initiated proactively during scheduled maintenance windows or low-utilization periods. This preliminary action allows comprehensive testing without interrupting critical workloads, as healthy replacement resources are already prepared in the resource pool.
Data Source
AI summary
Embodiments for preemptive deep diagnostics of resources in a disaggregated computing environment. Responsive to detecting a threshold breach of a recurrent event associated with a first resource of a first resource type executing a workload, an alert is generated; and responsive to receiving the alert, the execution of the workload on the first resource is ceased. Health check diagnostics are identified and invoked on the first resource based on the alert and a server telemetry. Results of the health check diagnostics are mapped to a set of learned failure patterns; and a potential failure of the first resource is predicted based on the mapping.