Workload Optimization System Dynamic Agent Scaling
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Solution Overview
Problem
Information handling systems face inefficiencies due to long job queues and unmanageable agent configurations, leading to unsatisfactory queue times and resource wastage, especially when a limited number of agents handle all workloads, resulting in high idle times and redundant agent provisioning.
Innovation Solution
A workload optimization system that includes a communication system, processing system, and memory system with a workload resource optimization engine to monitor job queues and dynamically generate or deactivate container hosts and agents, adjusting resources based on utilization conditions to optimize processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a limited number of agents are created to perform all workloads, then the number of processing resources is reduced, but job queue times become unsatisfactory and configuration becomes unmanageable
Solution Approach 1:
The system implements dynamic agent provisioning where agents are created and deactivated based on real-time workload conditions. The workload manager continuously monitors job queue depth and system utilization, automatically spawning new agents when queues exceed thresholds and deactivating them when utilization is low, transforming the static agent pool into a dynamic, adaptive resource that responds to changing workload demands
Solution Approach 2:
The system employs feedback mechanisms where the workload manager monitors job queue depth, agent utilization metrics, and system performance, then uses this information to control agent provisioning decisions. This closed-loop control ensures agents are provisioned based on actual workload needs rather than static configurations, preventing both over-provisioning and under-provisioning
2Adaptability or versatility
If custom agents for individual workloads are used, then workload-specific processing capability is improved, but hardware footprint and resource waste increase due to redundant agent provisioning
Solution Approach 1:
The system creates universal agents that can handle multiple workload types through dynamic configuration and job routing. Instead of provisioning specialized custom agents for each workload type, the workload manager assigns jobs to agents based on current capabilities and compatibility, allowing a smaller pool of multi-functional agents to replace a larger pool of specialized agents, thereby reducing hardware footprint while maintaining adaptability
Solution Approach 2:
The system dynamically changes agent parameters such as job type assignments, capacity limits, and operational states based on current workload demands. This allows the same physical agent infrastructure to adapt to different workload requirements by modifying software configurations rather than provisioning dedicated hardware for each workload type
3Productivity
If more agents are provisioned to reduce queue times, then job processing speed is improved, but resource waste increases due to high idle times
Solution Approach 1:
The system implements dynamic agent provisioning where agents are created and deactivated based on real-time workload conditions. The workload manager continuously monitors job queue depth and system utilization, automatically spawning new agents when queues exceed thresholds and deactivating them when utilization is low, transforming the static agent pool into a dynamic, adaptive resource that responds to changing workload demands
Solution Approach 2:
The system maintains continuous useful action by keeping agents active only when they are processing jobs or waiting for work, and deactivating them during idle periods. This ensures that processing resources are continuously utilized when needed while eliminating waste during low-demand periods
Data Source
AI summary
A workload optimization system includes a workload manager subsystem that provides a plurality of workloads that each generates a jobs, an agent infrastructure subsystem that includes a first container host hosting a first container having a first agent and that processes jobs generated by at least one of the plurality of workloads, and a workload resource optimization subsystem that monitors a job queue of jobs that were generated by the plurality of workloads and that are to be processed by the first container. The workload resource optimization subsystem then determines whether the job queue satisfies a container generation condition. If so, the workload resource optimization subsystem provides instructions to the first container host to generate a second container that includes a second agent. The workload resource optimization subsystem monitors container host utilization to provide instructions to activate a second container host to provide a third container having a third agent.