Dynamic Tree Grid Management for Workload Bottlenecks
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Solution Overview
Problem
In data centers, server clusters often inefficiently manage unpredictable workloads, leading to underutilization of resources due to over-provisioning for peak demands, which results in network bottlenecks and inefficiencies in grid computing environments.
Innovation Solution
A dynamic tree structure for grid management is implemented, where grid managers with hierarchical relations classify each other as superior or inferior, enabling efficient allocation and deallocation of computational resources through communication channels, allowing for dynamic reconfiguration and resource sharing across networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If servers are over-provisioned to handle peak workload demands, then network bottlenecks are avoided, but resource utilization deteriorates due to idle servers operating well under capacity
Solution Approach 1:
The patent implements dynamic workload distribution where servers can be assigned to different work queues based on real-time capacity. The system dynamically adjusts server assignments using capacity metrics and workload predictions, allowing servers to be allocated or deallocated from queues as their capacity changes, thereby optimizing resource utilization while maintaining network reliability during peak demands
Solution Approach 2:
The system performs preliminary actions by predicting future workload demands and pre-assigning servers to appropriate work queues before peak loads occur. Workload predictions are generated in advance, and servers are pre-positioned in queues based on predicted capacity requirements, enabling proactive resource allocation that avoids both over-provisioning and under-utilization
2Loss of energy
If servers are under-provisioned to reduce costs, then resource utilization improves, but network bottlenecks occur during peak workload spikes
Solution Approach 1:
The system dynamically adjusts server capacity assignments in real-time based on actual workload conditions. When peak demands occur, the system can rapidly assign additional servers to critical work queues, and when workload decreases, it deallocates servers from queues, providing flexible capacity management that maintains reliability without permanent over-provisioning
Solution Approach 2:
The system performs periodic capacity recalculations and server reassignments based on predicted workload patterns. By periodically updating capacity metrics and re-evaluating server-to-queue assignments, the system prepares for upcoming peak demands while maintaining cost-effective operation during normal periods
3Device complexity
If static server assignments are used, then system configuration is simple, but adaptability to varying workload demands deteriorates
Solution Approach 1:
The patent implements dynamic server-to-queue assignment that automatically adapts to varying workload demands. The system continuously monitors actual and predicted capacity of servers and work queues, then dynamically reassigns servers based on real-time conditions, providing high adaptability while managing complexity through automated decision-making rather than manual configuration
Solution Approach 2:
The system incorporates feedback mechanisms where actual server capacity and workload metrics are continuously measured and fed back into the assignment algorithm. This feedback loop enables the system to learn from actual performance data and adjust future assignments accordingly, improving adaptability while maintaining manageable configuration complexity through automated feedback-driven optimization
Data Source
AI summary
A method includes, in a grid computing environment, maintaining systems having grid managers having hierarchical relations, the relations of each grid manager stored in each of the systems. Each of these hierarchical relations are classified as superior or inferior.