Ensuring Data Freshness in Multi-Rate Task Chains Scheduling

2026-03-10

AI Summary

This work addresses the performance degradation and resource inefficiency in safety-critical autonomous systems caused by traditional scheduling approaches such as Logical Execution Time (LET), which introduce redundant delays that impair high-frequency control loops and waste resources in multi-rate sensor fusion tasks. To overcome these limitations, the paper proposes a scheduling framework centered on data freshness as a core constraint. By leveraging data lifetimes to drive scheduling decisions and employing task offsets for just-in-time (JIT) data production, the approach aligns offsets of shared and private predecessor tasks through data dependency graph decomposition, critical path extraction, and a consensus offset search algorithm. This ensures end-to-end data freshness without LET-induced buffering, eliminates redundant sampling overhead, and rigorously preserves 100% schedulability under Global Earliest Deadline First (Global EDF) scheduling.

Technology Category

Planning, Routing, and Scheduling: Scheduling under UncertaintyConstraint Satisfaction and Optimization: Distributed CSP/OptimizationReasoning under Uncertainty: Sequential Decision Making

Application Category

Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processingSecurity and Privacy: Data transparency and provenanceSemantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

Abstract

In safety-critical autonomous systems, data freshness presents a fundamental design challenge. While the Logical Execution Time (LET) paradigm ensures compositional determinism, it often does so at the cost of injected latency, degrading the phase margin of high-frequency control loops. Furthermore, mapping heterogeneous, multi-rate sensor fusion requirements onto rigid task-centric schedules typically implies in resource-inefficient oversampling. This paper proposes a Task-based scheduling framework extended with data freshness constraints. Unlike traditional models, scheduling decisions are driven by the lifespan of data. We introduce task offset based on the data freshness constraint to order data production in a Just-in-Time (JIT) fashion: the completion of the production of data with strictest data freshness constraint is delayed to the instant its consumers will be ready to use it. This allows for flexible task release offsets. We introduce a formal methodology to decompose Data Dependency Graphs into Dominant Paths by tracing the strictest data freshness constraints backward from the actuators. Based on this decomposition, we propose a Consensus Offset Search algorithm that synchronizes shared producers and private predecessors. This approach enforces end-to-end data freshness without the artificial latency of LET buffering. We formally prove that this offset-based alignment preserves the 100\% schedulability capacity of Global EDF, ensuring data freshness while eliminating the computational overhead of redundant sampling.

Problem

• data freshness
• multi-rate task chains
• scheduling
• real-time systems
• sensor fusion

Innovation

• data freshness
• task offset
• Just-in-Time scheduling
• Dominant Paths
• Consensus Offset Search