Partition Mapping in Multi-Node Computing Environments
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Solution Overview
Problem
Managing workload distribution and resource consumption in multi-node computing environments is complex due to the lack of efficient partition-level metrics, leading to sub-optimal architecture and potential performance issues from uneven node loads.
Innovation Solution
The development of techniques to gather and utilize organization-level metrics aggregated at the partition level, employing tools like SPLUNK for data capture and analysis, and algorithms such as the Greedy Algorithm and Karmarkar-Karp Algorithm for optimal partition mapping and rebalancing, ensuring even workload distribution across nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If partition-level metrics are implemented for workload distribution, then workload balancing improves, but system complexity increases
Solution Approach 1:
The system segments the multi-tenant environment into hierarchical levels (node level and partition level), allowing independent monitoring and management of workload distribution at each level. This segmentation enables targeted optimization without requiring complete system redesign.
Solution Approach 2:
The patent introduces an intermediary layer that aggregates tenant-level metrics into partition-level metrics, which then feed into workload balancing algorithms. This intermediary structure simplifies the complexity by providing a middle layer of abstraction between raw tenant data and balancing decisions.
2Measurement precision
If comprehensive monitoring of multiple nodes and partitions is implemented, then workload distribution accuracy improves, but overhead increases
Solution Approach 1:
The patent merges monitoring functions by aggregating metrics from multiple tenants at the partition level, reducing redundant data collection and processing. This combining approach maintains measurement precision while reducing the total overhead through consolidated metric gathering.
Solution Approach 2:
The monitoring system is designed with multi-functionality, where the same infrastructure collects both tenant-level metrics and aggregated partition-level metrics simultaneously. This universal approach allows comprehensive monitoring without proportionally increasing overhead, as a single data collection mechanism serves multiple purposes.
3Productivity
If frequent partition rebalancing is performed, then performance optimization improves, but system stability decreases
Solution Approach 1:
The system implements periodic rebalancing based on aggregated partition-level metrics rather than continuous or frequent adjustments. This periodic approach allows the system to maintain stability between rebalancing events while still achieving performance optimization through regular, controlled adjustments triggered by metric thresholds.
Data Source
AI summary
Mapping of database partitions to available nodes. Metric information related to the partitions of the database are stored. One or more metrics associated with the partitions are gathered. A plurality of potential mappings of partitions to nodes are evaluated. One of the potential mappings of partitions to nodes to result in improved metric distribution among the nodes and the partition moves are within a pre-selected move constraint is selected. The selected potential mapping is implemented by moving one or more partitions between one or more nodes.