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Priority Queue with Aging Scheduler

Priority Queue with Aging Scheduler MCP Connector for Claude

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A deterministic scheduler that manages task execution using priority-based queues with an anti-starvation aging mechanism.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner
More Details Connect to Claude

This MCP server provides advanced scheduling capabilities for managing task queues. It uses a deterministic priority-based approach enhanced by an aging mechanism to prevent task starvation. By increasing a task's effective priority as it waits, the system ensures that even low-priority tasks are eventually executed. You can use simulate_scheduling to run full simulations, get_task_by_id to retrieve specific performance metrics, and analyze_starvation_risk to evaluate if your configuration is causing delays. It is designed to help developers analyze wait times, turnaround times, and throughput in complex task environments.

priority-queueagingstarvation-preventiontask-managementdeterministic

3 tools expose this connector's capabilities to your AI agent.

analyze_starvation_risk

Evaluates a set of task wait times to determine if the current scheduling configuration is causing significant starvation

get_task_by_id

Retrieves specific metric details for a single task from a previously generated simulation

simulate_scheduling

Executes a full scheduling simulation based on a specific set of tasks and environmental parameters

See how to talk to your AI agent using Priority Queue with Aging Scheduler.

Run a simulation with 3 tasks: ID 'A' (priority 50, arrival 0, execution 100), ID 'B' (priority 10, arrival 0, execution 200), and ID 'C' (priority 80, arrival 10, execution 50). Use an aging rate of 0.5 and 1 agent.

The simulation completed with task C executing first, followed by task A, and then task B. The average wait time was 45ms and throughput was 0.015 tasks/ms.

Check if my current simulation results show any starvation risk using a wait time list of [10, 12, 15, 11, 100] and an average wait of 29.5.

The starvation risk is High because the task with a wait time of 100 exceeds ten times the average wait time.

What were the specific metrics for task 'task_001' in my last simulation?

Task 'task_001' had a wait time of 50ms, a turnaround time of 150ms, a response time of 50ms, and a normalized turnaround of 1.5.

The effective priority of a task increases based on the `agingRate` multiplied by the time the task has spent waiting in the queue, up to the defined `maxPriority`.

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