Workload Distribution Based on Serviceability Metrics
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Solution Overview
Problem
Data centers face increased downtime and ownership costs due to varying serviceability of computing systems, which depend on geographical location, accessibility, and component difficulty, leading to uneven maintenance costs and frequencies.
Innovation Solution
A system generates a serviceability metric for each computing system, considering geographical location, accessibility, and component complexity, and distributes workload to balance serviceability, prioritizing easier-to-maintain systems to reduce maintenance costs and downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If computing systems are distributed across various geographic locations for data center operations, then service coverage and availability are improved, but maintenance cost and downtime increase due to varying serviceability conditions
Solution Approach 1:
The patent assigns different serviceability metrics to different computing systems based on their specific characteristics (rack position, geographic location, accessibility). This local differentiation allows the workload distribution algorithm to account for varying maintenance conditions at each location, optimizing both service coverage and maintenance efficiency.
Solution Approach 2:
The system dynamically adjusts workload distribution based on real-time or updated serviceability metrics. As metrics change (e.g., rack repositioning, location changes), the workload allocation automatically adapts, ensuring that systems with better serviceability bear more workload while minimizing overall downtime.
2Adaptability or versatility
If computing systems are placed in difficult-to-access locations (high in racks, remote areas), then serviceability flexibility is improved, but maintenance cost and time increase
Solution Approach 1:
The patent quantifies serviceability factors (rack position, geographic location, accessibility) as numerical metrics. By changing these physical parameters into measurable values, the system can algorithmically determine optimal workload distribution, balancing serviceability flexibility with maintenance cost efficiency.
Solution Approach 2:
The system continuously monitors and updates serviceability metrics for each computing system. This feedback mechanism allows the workload distribution algorithm to respond to changing conditions (e.g., a system moved to a harder-to-access location), automatically adjusting allocations to minimize maintenance costs while preserving operational flexibility.
3Productivity
If workload is distributed uniformly across all computing systems, then resource utilization is improved, but overall serviceability and maintenance efficiency deteriorate
Solution Approach 1:
Instead of uniform workload distribution, the patent implements localized quality-based allocation where each computing system receives workload proportional to its serviceability characteristics. Systems with better accessibility and lower maintenance costs handle more workload, while harder-to-maintain systems receive less, optimizing both resource utilization and maintenance efficiency.
Solution Approach 2:
The patent deliberately creates asymmetric workload distribution based on serviceability metrics. Rather than treating all systems equally, the system intentionally allocates workloads unevenly according to each system's maintenance characteristics, achieving better overall efficiency while maintaining high resource utilization.
4Ease of manufacture
If computing systems with better serviceability are prioritized for workload, then maintenance cost and downtime are reduced, but system utilization becomes uneven
Solution Approach 1:
The system dynamically balances workload allocation based on serviceability metrics while ensuring all systems remain utilized. As serviceability conditions change or workload demands shift, the allocation automatically adjusts, preventing any single system from being over-utilized while maintaining cost efficiency. This dynamic approach resolves the tension between prioritizing easy-to-maintain systems and achieving balanced utilization.
Data Source
AI summary
Workload distribution based on serviceability includes: generating, for each of a plurality of computing systems, a metric representing serviceability of the computing system for which the metric is generated; and distributing workload among said plurality of computing systems in dependence upon the metrics.