LPAR Capacity Consolidation via Multi-Dimensional Bin Packing
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Solution Overview
Problem
Conventional LPAR capacity consolidation systems inefficiently manage processor utilization, leading to undersubscription or oversubscription, resulting in underutilized or overutilized servers, which negatively impact service quality and resource management.
Innovation Solution
A computer-implemented method that optimizes LPAR and server configurations using a bin packing methodology, considering multiple resource dimensions like processor, memory, power, and equipment cost, to determine an optimal configuration that balances resource utilization and reduces unnecessary server usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If LPAR capacity consolidation relies exclusively on processor utilization as a metric, then the configuration process is simple, but the resource allocation becomes inefficient leading to undersubscription or oversubscription
Solution Approach 1:
The patent transforms the configuration approach by changing from a single parameter (processor utilization) to multiple parameters including memory utilization, power consumption, equipment cost, and floor space. This multi-parameter optimization enables efficient resource allocation while avoiding the inefficiencies of single-metric consolidation.
2Reliability
If the number of LPARs is undersubscribed, then service quality is maintained, but server utilization decreases leading to wasted resources
Solution Approach 1:
The patent makes the server configuration serve multiple objectives simultaneously: maintaining service quality (reliability) while optimizing resource utilization, reducing energy consumption, minimizing equipment costs, and decreasing floor space requirements. This multi-functional optimization resolves the contradiction between service quality and resource efficiency.
3Productivity
If the number of LPARs is oversubscribed, then server utilization increases, but service quality is negatively impacted
Solution Approach 1:
The patent adds multiple dimensions to the configuration optimization beyond simple processor utilization. By incorporating memory, power, cost, and space dimensions, the system achieves balanced resource allocation that maintains service quality while improving overall utilization efficiency, avoiding the oversubscription problem.
4Quantity of substance
If multiple physical servers are used, then resource capacity increases, but energy consumption and equipment costs increase
Solution Approach 1:
The patent merges multiple consolidation objectives into a unified optimization framework. By combining processor, memory, power, cost, and space considerations into a single multi-criteria optimization process, the system determines the minimum number of servers needed while maintaining all required resource capacities, thereby reducing energy consumption and equipment costs.
Data Source
AI summary
A method and system for optimizing a configuration of a set of LPARs and a set of servers that host the LPARs. Configuration data and optimization characteristics are received. By applying the configuration data and optimization characteristics, a best fit of the LPARs into the servers is determined, thereby determining an optimized configuration. The best fit is based on a variant of bin packing or multidimensional bin packing methodology. The optimized configuration is stored. In one embodiment, comparisons of shadow costs are utilized to determine an optimal placement of the LPARs in the servers. LPAR(s) in the set of LPARs are migrated to other server(s) in the set of servers, which results in the LPARs and servers being configured in the optimized configuration.