Generative Profiling for Soft Real-Time Systems and its Applications to Resource Allocation

ArXiv.org · 2026-04-01

Modern real-time systems require accurate characterization of task timing behavior to ensure predictable performance, particularly on complex hardware architectures. Existing methods, such as worst-case execution time analysis, often fail to capture the fine-grained timing behaviors of a task under varying resource contexts (e.g., an allocation of cache, memory bandwidth, and CPU frequency), which is necessary to achieve efficient resource utilization. In this paper, we introduce a novel generative profiling approach that synthesizes context-dependent, fine-grained timing profiles for real-time tasks, including those for unmeasured resource allocations. Our approach leverages a nonparametric, conditional multi-marginal Schrödinger Bridge (MSB) formulation to generate accurate execution profiles for unseen resource contexts, with maximum likelihood guarantees. We demonstrate the efficiency and effectiveness of our approach through real-world benchmarks, and showcase its practical utility in a representative case study of adaptive multicore resource allocation for real-time systems.

Generative Profiling for Soft Real-Time Systems and its Applications to Resource Allocation

arXiv (Cornell University) · 2026-04-01

Modern real-time systems require accurate characterization of task timing behavior to ensure predictable performance, particularly on complex hardware architectures. Existing methods, such as worst-case execution time analysis, often fail to capture the fine-grained timing behaviors of a task under varying resource contexts (e.g., an allocation of cache, memory bandwidth, and CPU frequency), which is necessary to achieve efficient resource utilization. In this paper, we introduce a novel generative profiling approach that synthesizes context-dependent, fine-grained timing profiles for real-time tasks, including those for unmeasured resource allocations. Our approach leverages a nonparametric, conditional multi-marginal Schrödinger Bridge (MSB) formulation to generate accurate execution profiles for unseen resource contexts, with maximum likelihood guarantees. We demonstrate the efficiency and effectiveness of our approach through real-world benchmarks, and showcase its practical utility in a representative case study of adaptive multicore resource allocation for real-time systems.

Running Distributed Systems like Clockwork

Leibniz-Zentrum für Informatik (Schloss Dagstuhl) · 2026-01-01

Distributed Systems are commonly built using a set of standard assumptions: we assume that message delays are unbounded, that any packet can be lost in the network, and that clocks cannot be closely synchronized. On the one hand, these conservative assumptions result in robust systems that can operate reliably in a wide variety of conditions. On the other hand, they also force the system to do a lot of complex ad-hoc coordination and thus limit the performance it can achieve. In this paper, we take a look at what lies beyond this standard model. We observe that, on modern hardware in a single-tenant data center, distributed systems are able to closely coordinate and essentially "run like clockwork" with very little effort. If we are willing to additionally rule out some worst-case failure scenarios, this results in a large performance improvement, both in practice and even in theory. We demonstrate this effect using state-machine replication (SMR) as a case study: our SMR protocol, Watchmaker, exceeds the throughput of state-of-the-art algorithms by two orders of magnitude, and it requires only half as many replicas to tolerate the same number of faults.

CORD: Co-design of Resource Allocation and Deadline Decomposition with Generative Profiling

ArXiv.org · 2025-01-14

As multicore hardware is becoming increasingly common in real-time systems, traditional scheduling techniques that assume a single worst-case execution time for a task are no longer adequate, since they ignore the impact of shared resources on execution time. When tasks execute concurrently on different cores, their execution times often vary substantially with their allocated budgets of shared resources, such as cache and memory bandwidth. Even under a specific resource allocation, the resource use pattern of a task also changes with time during a job execution. It is therefore important to consider the relationship between multicore resources and execution time in task modeling and scheduling algorithm design. In this paper, we propose a much more precise execution model for DAG-based real-time tasks that captures the time-varying resource use characteristics of a task under different budgets of shared resources. We present a generative resource profiling algorithm that efficiently predicts, from limited measurement data, the resource profile of a task at any time during its execution under a given resource budget. The generative profiles can then be used to construct the execution models for tasks, using which one can make informed resource allocation decisions. We further introduce a multicore resource allocation and deadline decomposition co-design technique for DAG-based tasks that leverages the generated execution models to jointly allocate resources and deadlines to subtasks, to maximize resource efficiency and schedulability. Our evaluation results show that our generative profiling algorithm achieves high accuracy while being efficient, and that our co-allocation technique substantially improves schedulability compared to a state-of-the-art deadline decomposition method.

Quasi-Static Scheduling for Deterministic Timed Concurrent Models on Multi-Core Hardware

ACM Transactions on Embedded Computing Systems · 2025-09-01 · 3 citations

To design performant, expressive, and reliable cyber-physical systems (CPSs), researchers extensively perform quasi-static scheduling for concurrent models of computation (MoCs) on multi-core hardware. However, these quasi-static scheduling approaches are developed independently for their corresponding MoCs, despite commonality in the approaches. To help generalize the use of quasi-static scheduling to new and emerging MoCs, this article proposes a unified approach for a class of deterministic timed concurrent models (DTCMs), including prominent models such as synchronous dataflow (SDF), Boolean-controlled dataflow (BDF), scenario-aware dataflow (SADF), and Logical Execution Time (LET). In contrast to scheduling techniques tailored exclusively to specific MoCs, our unified approach leverages a common intermediate formalism called state space finite automata (SSFA), bridging the gap between high-level MoCs and executable schedules. Once identified as DTCMs, new MoCs can directly adopt SSFA-based scheduling, significantly easing adoption. We show that quasi-static schedules facilitated by SSFA are provably free from timing anomalies and enable straightforward worst-case makespan analysis. We demonstrate the approach using the reactor model—an emerging discrete-event MoC—programmed using the Lingua Franca ( LF ) language. Experiments show that quasi-statically scheduled LF programs exhibit lower runtime overhead compared to the dynamically scheduled LF programs, and that the analyzable worst-case makespans enable compile-time deadline checking.

Rasco: Resource Allocation and Scheduling Co-design for DAG Applications on Multicore

ACM Transactions on Embedded Computing Systems · 2025-08-19

As multicore hardware becomes increasingly prevalent in real-time embedded systems, traditional scheduling techniques that assume a single worst-case execution time for each task are no longer adequate, as they fail to account for the impact of shared resources—such as cache and memory bandwidth—on execution time. When tasks execute concurrently on different cores, their execution times can vary substantially with their allocated resources. Moreover, the instruction rate of a task during a job execution varies with time, and this variation pattern differs across tasks. Therefore, to improve performance it is crucial to incorporate the relationship between the resource budget allocated to each task and its time-varying instruction rate in task modeling, resource allocation, and scheduling algorithm design. Yet, no prior work has considered the fine-grained dynamic resource allocation and scheduling problems jointly while also providing hard real-time guarantees. In this article, we introduce a resource-dependent multi-phase timing model that captures the time-varying instruction rates of a task under different resource allocations and that enables worst-case analysis under dynamic allocation. We present a method for constructing estimates of such a model based on task execution profiles, which can be obtained through measurements. We then present Rasco , a co-design technique for multicore resource allocation and scheduling of real-time DAG applications with end-to-end deadlines. Rasco leverages the resource-dependent multi-phase model of each task to simultaneously allocate resources at a fine granularity and assign task deadlines. This approach maximizes execution progress under resource constraints while providing hard real-time schedulability guarantees. Our evaluation shows that Rasco substantially enhances schedulability and reduces end-to-end latency compared to the state of the art.

The sign of Leser-Trélat guided the diagnosis of gastric cancer – A case report in Vietnam

2025-07-03

The sign of Leser-Trélat guided the diagnosis of gastric cancer – A case report in VietnamNgan Mai1, Tri Ngo2, Hung Nguyen1, Linh Phan1, Tan Nguyen2, Linh Nguyen31Hanoi Medical University Hospital2Hanoi Medical University3Nghe An Oncology Hospital

RoboRebound: Multi-Robot System Defense with Bounded-Time Interaction

2025-03-26 · 1 citations

Byzantine Fault Tolerance (BFT) is a classic technique for defending distributed systems against a wide range of faults and attacks. However, existing solutions are designed for systems where nodes can interact only by exchanging messages. They are not directly applicable to systems where nodes have sensors and actuators and can also interact in the physical world - perhaps by blocking each other's path or by crashing into each other.

FairDP: Achieving Fairness Certification with Differential Privacy

2025-04-09 · 2 citations

This paper introduces FairDP, a novel training mechanism designed to provide group fairness certification for the trained model's decisions, along with a differential privacy (DP) guarantee to protect training data. The key idea of FairDP is to train models for distinct individual groups independently, add noise to each group's gradient for data privacy protection, and progressively integrate knowledge from group models to formulate a comprehensive model that balances privacy, utility, and fairness in downstream tasks. By doing so, FairDP ensures equal contribution from each group while gaining control over the amount of DP-preserving noise added to each group's contribution. To provide fairness certification, FairDP leverages the DP-preserving noise to statistically quantify and bound fairness metrics. An extensive theoretical and empirical analysis using benchmark datasets validates the efficacy of FairDP and improved trade-offs between model utility, privacy, and fairness compared with existing methods. Our empirical results indicate that FairDP can improve fairness metrics by more than 65% on average while attaining marginal utility drop (less than 4% on average) under a rigorous DP-preservation across benchmark datasets compared with existing baselines.

Stochastic Learning of Computational Resource Usage as Graph-Structured Multimarginal Schrödinger Bridge

IEEE Transactions on Control Systems Technology · 2025-05-06 · 1 citations

We propose to learn the time-varying stochastic computational resource usage of software as a graph-structured Schrödinger bridge problem (SBP). In general, learning the computational resource usage from data is challenging because resources, such as the number of CPU instructions and the number of last level cache requests are both time-varying and statistically correlated. Our proposed method enables learning the joint time-varying stochasticity in computational resource usage from the measured profile snapshots in a nonparametric manner. The method can be used to predict the most-likely time-varying distribution of computational resource availability at a desired time. We provide detailed algorithms for stochastic learning in both single-core and multicore cases, discuss the convergence guarantees, computational complexities, and demonstrate their practical use in two case studies: a single-core nonlinear model predictive controller (NMPC) and a synthetic multicore software.