Load Manager for Distributed Task Scheduler Configuration
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Solution Overview
Problem
Monitoring and management of load balancing in distributed computing systems are inadequate, leading to inefficiencies in task execution due to unbalanced server loads.
Innovation Solution
Implementing a load manager that monitors server load metrics and adjusts the time-based task scheduler configurations to balance the load by modifying the frequency or number of tasks for servers that are overloaded or underutilized, using a configuration manager to maintain a centralized inventory and modify task scheduler configurations accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If time-based task scheduler configurations are implemented on each server to automatically execute jobs periodically, then job automation efficiency is improved, but load balancing capability deteriorates
Solution Approach 1:
The system implements feedback mechanisms where the load manager continuously monitors server load metrics and uses this information to dynamically adjust task scheduler configurations. The load manager receives feedback about current server states and modifies future task assignments accordingly, creating a closed-loop control system that balances automation with adaptability.
Solution Approach 2:
The patent transforms static time-based task scheduler configurations into dynamic ones by introducing a load manager that continuously adapts task distribution based on real-time server conditions. The system moves from fixed scheduling to flexible scheduling where task assignments can be modified based on changing load metrics, achieving both automation and load balancing.
2Stability of the object's composition
If servers execute tasks according to fixed time-based scheduler configurations, then task execution predictability is improved, but system responsiveness to load changes deteriorates
Solution Approach 1:
The load manager performs preliminary actions by proactively adjusting task scheduler configurations before servers become overloaded. By monitoring load metrics and predicting future states, the system modifies task assignments in advance to prevent performance degradation, maintaining both predictability and responsiveness.
Solution Approach 2:
The system implements self-service mechanisms where the load manager autonomously monitors server states and automatically adjusts task distributions without manual intervention. This self-managing approach maintains predictable task execution while adapting to load changes through automated decision-making based on monitored metrics.
3Adaptability or versatility
If load manager dynamically adjusts task scheduler configurations based on load metrics, then load balancing performance is improved, but system complexity increases
Solution Approach 1:
The load manager is designed as a universal component that handles multiple functions: monitoring load metrics, analyzing server states, making adjustment decisions, and communicating with task schedulers. By consolidating these diverse functions into a single multi-functional manager, the system achieves improved load balancing without proportionally increasing overall complexity.
Solution Approach 2:
The load manager serves as an intermediary layer between the task scheduling system and server execution environment. This mediator abstracts the complexity of dynamic adjustments from individual servers, centralizing the decision-making logic and simplifying the overall system architecture while maintaining adaptive load balancing capabilities.
4Device complexity
If monitoring and management of server loads are performed without dynamic adjustment, then system simplicity is maintained, but task execution efficiency deteriorates
Solution Approach 1:
The system implements self-service monitoring where the load manager automatically tracks server load metrics and makes adjustments without external intervention. This autonomous operation maintains system simplicity by eliminating the need for complex manual management mechanisms while simultaneously improving task execution efficiency through dynamic optimization.
Data Source
AI summary
A load manager comprises a configuration manager and a load monitor. The load manager is configured to monitor and manage aspects of a distributed computer system comprising a plurality of servers. Each server is configured to perform tasks according to a respective time-based scheduler configuration. In some embodiments, the load monitor monitors one or more load metrics of each of the one or more servers. In response to one or more load metrics exceeding a threshold, the configuration manager determines the current time-based task scheduler configuration of the server exceeding the threshold. The load manager is further configured to modify the time-based task scheduler configuration to adjust a further task load on the server based on the at least one or more load metrics.