Load Balancer Using Interpolation for Heterogeneous Server Clusters
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Solution Overview
Problem
In computing systems with clusters of servers of varying performance capabilities, existing load balancers lack the ability to efficiently distribute workloads across different types of servers, leading to inefficient resource utilization and performance disparities.
Innovation Solution
A system and method that determine performance metric ratios between different types of servers using benchmark datasets, compute interpolation functions to adjust dataset distribution, and configure a load balancer to distribute workloads based on these metrics, ensuring concurrent workload completion across servers of varying capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a load balancer distributes workload uniformly across all servers using simple algorithms like round robin, then the load distribution is easy to implement, but servers with different performance capabilities experience inefficient resource utilization and performance disparities
Solution Approach 1:
The patent applies local quality by assigning different workload amounts to different server types based on their specific performance capabilities. The load balancer determines performance metric ratios for each server type relative to a baseline, then distributes workload proportionally - faster servers receive more data while slower servers receive less, optimizing each server's resource utilization according to its local characteristics
Solution Approach 2:
The patent changes the load distribution parameters from uniform allocation to performance-based allocation. By computing performance metric ratios and using interpolation functions to determine optimal workload distribution parameters, the system transforms the load balancing approach to account for server heterogeneity, improving overall productivity while maintaining manageable complexity
2Productivity
If a load balancer configures complex performance-based distribution algorithms to optimize resource utilization across heterogeneous servers, then resource utilization efficiency improves, but the system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-computing performance metric ratios for each server type using benchmark datasets before actual workload distribution. The load balancer establishes performance baselines and determines interpolation functions in advance, so that when real workloads arrive, the distribution decisions can be made efficiently based on pre-analyzed server capabilities rather than complex real-time calculations
Solution Approach 2:
The patent introduces an intermediary mechanism - the performance metric ratio and interpolation function - that mediates between server capabilities and workload distribution. This intermediary layer translates heterogeneous server performance characteristics into a standardized distribution scheme, simplifying the load balancing logic while maintaining optimization benefits
3Measurement precision
If benchmark datasets of multiple sizes are used to determine performance metric ratios for different server types, then accurate load distribution is achieved, but the time and resources required for performance measurement increase
Solution Approach 1:
The patent applies partial action by selecting a representative set of benchmark dataset sizes rather than testing all possible sizes. The system determines performance metric ratios for a manageable plurality of dataset sizes that capture the performance characteristics across the range, using interpolation to fill in intermediate values, thus achieving sufficient accuracy without exhaustive measurement
Data Source
AI summary
A system (and associated method) includes a processor which determines a performance metric ratio of a performance metric of a second type of server relative to a performance metric of a first server type for each of multiple sizes of multiple benchmark datasets to thereby determine a plurality of performance metric ratio values. The processor also determines an interpolation function for the plurality of performance metric ratio values usable to compute interpolation performance metric ratios of the second type of server relative to the first type of server for dataset sizes other than the first plurality of sizes. Given a second dataset, the processor determines an amount of the second dataset to provide to each of the respective server types using the interpolation function. The processor configures a load balancer based on the amount of the second dataset determined for each type of server.