Elastic Scaling Control Trajectory Dataset for Stream Processing Engines
#State-Action Modeling #Control Policy Learning #System Response Prediction #Constraint Satisfaction Evaluation #Stream Processing Resource Management #Dynamic Workload Scheduling #Elastic Scaling Policy Analysis #System Performance Optimization
- 6480
- 0.4GB
- JSON
- CATL
MOBIUSI INC
Updated:2026-11-02
AI Analysis & Value Prop
This dataset records simulated operation and control trajectories for stream processing engines under changing workloads and resource constraints. It covers input traffic changes, initial engine states, scaling and task migration decisions, and system responses such as throughput, latency, resource utilization, and backlog. Aligned state-action-response traces, control performance summaries, and checks for resource, parallelism, and cooldown constraints support analysis of elastic resource management, control policy training and evaluation, and simulation-based reasoning.
Dataset Insights
Sample Examples
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Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| workload_trace | array | The original time series of input traffic and burst intensity during simulation. |
| control_event_log | array | The original records of resource actions, target parallelism levels, and task migration events produced by the scaling policy. |
| initial_engine_state | object | The stream processing engine's resource allocation and backlog state at the start of simulation. |
| resource_constraints | object | The original constraints on available compute resources and control boundaries during elastic scaling. |
| constraint_check_trace | array | A derived trace indicating whether each control action satisfies resource, parallelism, and cooldown constraints. |
| observed_metrics_trace | array | The original observations of throughput, latency, resource utilization, and backlog during simulation. |
| control_performance_summary | object | Summary metrics of scaling behavior and system performance computed from the complete trajectory. |
| state_action_response_trace | array | A derived trace aligning system states and control actions with the system responses that follow over time. |
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 |
Frequently Asked Questions
- What does the dataset contain?
- It includes workload change traces, resource constraints, initial engine states, scaling and task migration events, observed system metrics, aligned state-action-response traces, control performance summaries, and constraint check results.
- Which tasks can these trajectories support?
- They can support analysis of relationships between system states and control actions, training or evaluation of elastic scaling policies, prediction of responses such as throughput, latency, and backlog, and checks that actions satisfy resource and operational constraints.
- Was the data collected from production systems?
- The supplied description identifies the data as simulated trajectories that represent relationships among modeled workloads, resource configurations, control events, and system metrics. Validate findings against the target engine's operational data before using them for production decisions.
- How can I access the data, and what are the licensing terms?
- Refer to the dataset product page for the available download method, file specifications, and license terms. Check the permitted scope and commercial-use conditions before use; the information provided here does not specify an access method or license type.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Elastic Scaling Control Trajectory Dataset for Stream Processing Engines},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/396c64cc2493c51cd5a941afbde14b82?dataset_task_cate_id=8},
urldate={2026-10-28},
keywords={stream processing dataset,elastic scaling,control trajectories,resource management,workload scheduling,system performance simulation,state-action modeling,stream processing engine},
version={1.0}
}Using this in research? Please cite us.
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