Cross-Cluster Load Management for Secure Failover and Bottleneck Detection
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Solution Overview
Problem
Existing systems face challenges in efficiently managing and securing data across geographically dispersed data clusters, particularly in ensuring data availability, integrity, and reliability while addressing bottlenecks and predicting failures.
Innovation Solution
Implementing a system with self-learning adapters that monitor and identify bottlenecks, securely share loads between multiple sites, utilize a security module to filter network traffic, and provide a reporting console for performance issues, along with a load management module to optimize resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If organizations expand geographically to diversify data clusters, then data integrity and reliability are improved, but system complexity and difficulty in managing loads increase
Solution Approach 1:
The patent segments the load management system into multiple independent components: a load management server that handles global coordination, site-specific adapters that manage local workloads, and a database that stores configuration information. This segmentation allows each component to operate independently, reducing overall system complexity while maintaining reliability across geographically dispersed data clusters.
Solution Approach 2:
The patent introduces a load management server as an intermediary between multiple data clusters and their respective adapters. This intermediary coordinates load distribution decisions, manages failover between sites, and provides centralized control without requiring direct complex interconnections between all sites, thereby simplifying the overall system architecture.
2Reliability
If organizations expand geographically to diversify data clusters, then data integrity and reliability are improved, but difficulty in identifying bottlenecks increases
Solution Approach 1:
The patent implements feedback mechanisms where site-specific adapters continuously monitor local workload performance and send status information to the load management server. The server aggregates this feedback from multiple sites and uses it to identify bottlenecks, adjust load distribution, and trigger failover procedures when necessary, making bottleneck detection automated and centralized.
Solution Approach 2:
The load management server performs multiple functions including monitoring performance across all sites, identifying bottlenecks, distributing loads, managing failover, and providing reporting. This multi-functional approach consolidates what would otherwise require multiple separate monitoring and management systems into a single unified platform, reducing the difficulty of detecting and measuring system-wide bottlenecks.
3Reliability
If organizations expand geographically to diversify data clusters, then data integrity and reliability are improved, but predicting failures becomes more difficult
Solution Approach 1:
The patent implements preliminary actions by continuously monitoring performance metrics at each site and proactively identifying potential failures before they occur. The load management server analyzes trends in the feedback data to predict future failures and can trigger failover procedures in advance, reducing the difficulty of predicting failures in geographically dispersed clusters.
Solution Approach 2:
The feedback mechanism provides continuous real-time information about site performance, storage availability, and workload patterns to the load management server. This feedback enables the server to detect deteriorating conditions and predict failures by analyzing performance trends, allowing for proactive failure prediction across multiple geographic locations.
4Reliability
If organizations expand geographically to diversify data clusters, then storage availability is improved, but load management complexity increases
Solution Approach 1:
The load management system is segmented into hierarchical levels: a central load management server that handles high-level coordination and decision-making, and distributed site-specific adapters that manage local workload details. This segmentation reduces the complexity at each level while maintaining overall system coordination, enabling effective load management across geographically dispersed clusters.
Solution Approach 2:
The patent implements dynamic load management where the load management server continuously adjusts load distribution based on real-time feedback about site performance, storage availability, and workload patterns. This dynamic approach allows the system to adapt to changing conditions automatically, reducing the need for complex static configuration and manual intervention.
Data Source
AI summary
A method for managing loads in data clusters includes: identifying, by a first load management module (LMM) of a first data cluster, a load performance decline event associated with the first data cluster; in response to identifying the load performance decline event: selecting a second LMM associated with a second data cluster, wherein the second LMM is associated with an authenticated connection with the first LMM; sending requests to the second LMM using a secure string identifier associated with the authenticated connection, wherein the LMM services the requests using the second data cluster.