Dynamic Data Migration for Distributed Grid Load Balancing
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Solution Overview
Problem
Distributed data grids face performance degradation due to hot spots, where certain components become overwhelmed by high loads, leading to increased latency and reduced throughput across the system, as tasks for a particular datum serialize and impact other components.
Innovation Solution
A system and method for dynamic data migration that identifies hot components and partitions by analyzing wait times and execution times, then migrates non-hot partitions from hot components to less-loaded ones, using incremental or batch migration algorithms to optimize throughput and alleviate load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is concentrated on certain components to improve access efficiency, then throughput increases, but hot spots form causing latency to increase
Solution Approach 1:
The patent implements dynamic load balancing by continuously monitoring system state and automatically migrating data partitions between components. The load balancer dynamically adjusts data distribution based on real-time metrics such as throughput and latency, transitioning the system from static to adaptive operation to prevent hot spot formation while maintaining high throughput
Solution Approach 2:
The system employs feedback mechanisms where the load balancer monitors performance metrics (throughput, latency) from various components and uses this information to make informed decisions about data migration. This closed-loop control ensures that data redistribution responds to actual system conditions, optimizing the balance between throughput and latency
2Loss of time
If data is evenly distributed across components, then latency is reduced, but throughput decreases due to lack of optimization
Solution Approach 1:
Rather than maintaining static even distribution, the system dynamically adjusts data placement based on real-time performance feedback. The load balancer continuously evaluates which components are underutilized and migrates data to optimize both latency and throughput simultaneously, adapting to changing workloads and system conditions
Solution Approach 2:
The system changes the distribution parameters of data across components based on monitored performance metrics. By adjusting placement decisions according to throughput and latency measurements, the system optimizes the balance between these conflicting objectives rather than relying on fixed distribution patterns
3Productivity
If data migration is performed frequently to maintain balance, then load balancing improves, but system complexity increases
Solution Approach 1:
The load balancer operates autonomously to manage data migration, automatically monitoring system state and executing rebalancing operations without external intervention. This self-managing approach handles the complexity internally while presenting a simplified interface to the rest of the system, reducing the perceived complexity for users and operators
Data Source
AI summary
A system and method for load balancing between components of a distributed data grid. The system and method support dynamic data migration of selected data partitions in response to detection of hot spots in the data grid which degrade system performance. In embodiments, the system and method relies upon analysis of per-partition performance statistics for both the identification of data nodes which would benefit from data migration and the selection of data nodes for migration. Tuning of the data migration thresholds and method provides for optimizing throughput of the data grid to avoid degradation of performance resulting from load-induced hot spots.