Workload Provisioning via Risk Weighted Resource Allocation
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Solution Overview
Problem
Computer systems with multiple resources face challenges in dynamically managing workload distribution due to varying availability and failure risks, which complicates resource provisioning and affects system reliability and availability.
Innovation Solution
A method that identifies failure risks for each resource, combines likelihood of failure with expected resolution time to calculate risk weights, and uses these weights to provision workload across resources, prioritizing important tasks on resources with lower accumulated risk weights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If redundancy is incorporated into the system to improve reliability, then system availability is improved, but system expense increases
Solution Approach 1:
The patent applies partial redundancy by calculating risk weights for each resource based on failure likelihood and resolution time, then allocating resources selectively to achieve target availability. Instead of full redundancy across all resources, the system dynamically determines the optimal level of redundancy needed for each specific resource based on its risk profile, thereby reducing overall system expense while maintaining required availability levels.
2Reliability
If resources are dynamically reallocated based on failure risks, then system availability is improved, but resource manager complexity increases
Solution Approach 1:
The patent transforms the complex multidimensional resource allocation problem into a simpler decision framework by introducing risk weight as a consolidated parameter. Each resource is assigned a risk weight calculated from failure likelihood and resolution time, allowing the resource manager to make allocation decisions based on this single composite metric rather than analyzing multiple independent factors simultaneously, thereby reducing operational complexity.
Solution Approach 2:
The system implements continuous monitoring of resource status, failure risks, and resolution times, with the resource manager dynamically adjusting allocations based on feedback from these measurements. This closed-loop control enables automatic adaptation to changing conditions without requiring complex manual intervention, as the feedback mechanism guides reallocation decisions systematically.
3Device complexity
If workload is statically allocated to resources, then resource manager simplicity is maintained, but system adaptability to failures deteriorates
Solution Approach 1:
The patent transitions from static workload allocation to dynamic allocation based on real-time risk assessments. The resource manager continuously evaluates failure likelihood and resolution time for each resource, adjusting workload assignments accordingly. This dynamic approach allows the system to automatically adapt to changing conditions such as emerging failures or varying resource availability, maintaining simplicity through systematic decision rules while achieving high adaptability.
Data Source
AI summary
A method for operating a system comprising multiple resources. The method comprises identifying for each resource a set of one or more failure risks for that resource. For each identified failure risk, a likelihood of failure is combined with an expected resolution time to provide a risk weight for the identified failure risk. For each resource, the risk weights for each failure risk are accumulated to provide an accumulated risk weight for the resource. A resource manager provisions workload across the multiple resources based on the accumulated risk weights for each resource.