Cluster Resource Allocation Using Historical Wear Data
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Solution Overview
Problem
Computer clusters experience increased failures and reduced service availability due to resource over-utilization, leading to time and cost inefficiencies in maintaining and replacing components.
Innovation Solution
A method for managing computer cluster resources using automated means that allocate resources based on historical data to avoid over-utilization, considering resource wear, cluster topology, and job requirements, with the goal of extending the cluster's operational lifetime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of stationary object
If automated means allocate resources based on historical data to avoid over-utilization, then the lifetime of cluster resources is extended, but the device complexity increases
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing historical usage data before allocating resources. The automated means examine past utilization patterns, wear levels, and failure histories to predict which resources are most likely to succeed, thereby preventing over-utilization before it occurs and extending resource lifetime without requiring complex real-time monitoring infrastructure
Solution Approach 2:
The system implements feedback mechanisms where allocation decisions are continuously refined based on historical data about resource performance, wear accumulation, and actual usage patterns. This feedback loop allows the automated means to learn from past allocations and improve future decisions, extending resource lifetime through data-driven optimization rather than complex mechanical controls
2Strength
If historical data is collected and analyzed for each resource allocation decision, then resource wear is optimized, but the loss of time in processing allocation increases
Solution Approach 1:
The system performs preliminary analysis of historical data in advance, building predictive models and wear assessment algorithms before allocation decisions are needed. By pre-processing historical usage patterns, failure data, and wear metrics, the system creates ready-to-use allocation recommendations that can be quickly applied when jobs need resources, thus optimizing wear without incurring time penalties during actual allocation
Solution Approach 2:
The system applies partial analysis by focusing historical data examination on the most critical wear indicators and high-impact resources rather than analyzing every possible parameter for every allocation decision. This selective approach to historical data analysis provides sufficient wear optimization while keeping processing time acceptable for operational needs
Data Source
AI summary
A method for managing resources of a computer cluster, wherein automated means allocate to a job at least one resource among several resources from the cluster, the automated means selecting the resource based on at least one historical data relative to previous uses of the resources and/or data relative to the arrangement, temperature, power consumption, bandwidth, or maintenance of the cluster or one or more components thereof, the automated mechanism thus determining the wear of the various resources available and choosing the resources with the lowest wear to perform the job, thereby avoiding the over-utilization of resources, one of the main causes of failures in a computer cluster, and increasing the lifetime of the cluster.