Resource-Aware Job Placement Code Understanding Dataset

#code understanding #resource-aware scheduling analysis #job placement decision reasoning #distributed job scheduling #resource allocation analysis #cluster workload management

Records: 6940
Size: 0.2GB
Format: JSON
Provider: MOBIUSI INC
Updated: v1.02026-11-04

AI Analysis & Value Prop

This dataset presents source-code understanding examples drawn from distributed task and job schedulers, covering resource checks, candidate-node filtering, constraint matching, and job placement logic. Each example pairs code with an analysis question and an explanatory answer about selection conditions, the impact of resource state, and the resulting placement decision. It supports training and evaluation of models that need to understand and explain resource-aware scheduling behavior.

Overview

Dataset Insights

Preview

Sample Examples

sample.json

{}

Schema

Technical Specifications

Field	Type	Description
file_name	string	File name
source_code	string	The scheduler source-code snippet to analyze, including relevant resource checks, node filtering, constraints, or placement logic.
placement_result	string	The resulting job placement behavior, including the selected node or the source-supported reason placement cannot proceed.
analysis_question	string	The question or instruction specifying which resource-aware scheduling behavior should be explained.
selection_conditions	string	An explanation, grounded in the supplied source code, of the conditions used to select or reject candidate nodes or jobs.
reasoning_explanation	string	A coherent explanation connecting the relevant code paths, selection conditions, resource state, and placement result.
resource_state_impact	string	An explanation of how available, requested, reserved, or otherwise tracked resources affect the scheduling decision.

Trust

Compliance Statement

Authorization Type

Proprietary - Commercial AI Training License (No Redistribution)

Commercial Use

Requires exclusive subscription or authorization contract (monthly or per-invocation charging)

Privacy and Anonymization

No PII, no real company names, simulated scenarios follow industry standards

Compliance System

Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

FAQ

Frequently Asked Questions

What does the dataset contain?: It focuses on distributed scheduler source code, including resource checks, candidate-node filtering, constraint matching, and job placement. Examples pair analysis questions with explanatory answers covering selection conditions, resource-state impacts, and placement outcomes.

Which training and evaluation tasks does it support?: It supports code understanding, code question answering, scheduling-logic explanation, and supervised code fine-tuning. It can also help evaluate whether a model can use source code to explain how resource state affects node selection and job placement.

How can I check the fields and sample specifications?: Consult the dataset page or delivery samples for field details. Records include a source-code snippet, analysis question, selection conditions, resource-state impact, placement result, and reasoning explanation. Confirm the sample count, format, and delivery method in the product listing or delivery information.

What are the licensing and usage terms?: Usage rights depend on the product terms and the licenses of any included source code. Before use, review the dataset listing for licensing, redistribution, and commercial-use terms, and verify that the relevant source-code licenses permit your intended use.

Can't find the data you need?

Post a request and let data providers reach out to you.

Get this Dataset

High-quality data, ready for your next AI breakthrough.

Cite this Work

@dataset{Mobiusi2026,
  title={Resource-Aware Job Placement Code Understanding Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/8bb4f1884196761d215e1b37253e8292?dataset_scene_id=19},
  urldate={2026-10-22},
  keywords={resource-aware scheduling,job placement dataset,scheduler code understanding,distributed task scheduling,node filtering,resource checks,code SFT,scheduling logic analysis},
  version={1.0}
}

Using this in research? Please cite us.

placeholder

Scene

Domain

Modality

Task