Edge devices for robotics in hazardous environments, such as rescue drones, navigate complex terrains while transmitting images to remote servers for anomaly detection, including wildfires. However, these devices operate under strict resource constraints, prioritizing operational-critical tasks (e.g., autonomous navigation) while handling image-processing workloads with minimal overhead. Offloading computation to a remote server can alleviate this burden, but unstable network conditions can degrade accuracy and timeliness. To address these challenges, this paper presents a novel offloading framework that balances computational efficiency and accuracy in image-processing tasks. Specifically, it ensures (R1) a minimum accuracy level for individual image-processing tasks associated with different camera sensors and (R2) maximizes the overall image-processing accuracy across all sensors. Our approach builds on an edge-server collaborative image reconstruction architecture, where images are divided into patches and selectively reconstructed. To achieve R1 and R2, we introduce: (i) a hierarchical scheduler that effectively prioritizes patch transmissions under resource constraints and (ii) a feedback mechanism that adapts to network instability, ensuring reliable offloading and inference. Experimental results demonstrate that our framework maintains high accuracy and timely processing, even under network failures.

Consensus algorithms are widely adopted in clustered systems to distribute data efficiently, with Raft being a prominent failover algorithm due to its effectiveness and fault tolerance. However, Raft and other consensus algorithms do not provide timing guarantees, limiting their application to time-critical systems such as industrial control systems, drone swarms, and autonomous vehicle networks, where it is critical to distribute state machines in time and prevent multiple timing misses of data. In this paper, we propose CARTEL (Consensus Adapting Real-Time and Efficient Logging), a novel consensus algorithm that integrates time-predictability into the Raft framework. To achieve this aim, we examine Raft's mechanisms and identify the characteristics that impact the timing of data propagation within a distributed system. Based on these insights, we design CARTEL by developing two mechanisms that address the primary limitations of Raft: (i) CARTEL voting to solve the uncertainty of leader election timing, and (ii) CARTEL node buffer to limit the number of indeterminately deferred data during leader failure. Moreover, we propose how to utilize the mechanisms of CARTEL to ensure time-predictability in leader elections and mitigate the indeterminate nature of data logging during leader transitions, without harming the integrity of Raft. We validate the effectiveness of CARTEL through real-world implementation. The experiments confirm that CARTEL not only reduces the uncertainty of system recovery inherent in the election process (by 65.7% compared to Raft) but also enhances the integrity of the distributed system by guaranteeing timely data logging.

Targeting a multiobject tracking (MOT) system with multiple MOT tasks, this article develops Batch-MOT, the first system design that achieves both (G1) timing guarantee and (G2) accuracy maximization, by utilizing batch execution that allows multiple deep neural network (DNN) executions to perform simultaneously in a single DNN inference resulting in significantly decreased execution time without accuracy loss. To this end, we propose an adaptable scheduling framework that allows run-time execution behaviors deviated from our base scheduling algorithm (i.e., nonpreemptive fixed-priority scheduling) without compromising G1. Based on the adaptable framework, we then develop 1) a run-time batching mechanism that finds and executes a batch set of MOT tasks and 2) a run-time idling mechanism that waits for the future releases of MOT tasks for batch execution. Both run-time mechanisms can achieve G1 and G2 without incurring high run-time overhead, as they systematically exploit the run-time execution behaviors allowed by the adaptive framework. Our evaluation conducted with a real-world data set demonstrates the effectiveness of Batch-MOT in improving tracking accuracy while providing a timing guarantee compared to the state-of-the-art real-time MOT system for multiple MOT tasks.

Although blockchain technology is being increasingly utilized across various fields, the challenge of providing timing guarantees for transactions remains unmet, which is an obstacle in implementing blockchain solutions for time-sensitive applications such as high-frequency trading and real-time payments. In this paper, we propose the first solution to achieve a timing guarantee on blockchain. To this end, we raise and address two issues for timely transactions on a blockchain: (a) architectural support, and (b) real-time scheduling principles specialized for blockchain. For (a), we modify an existing blockchain network, offering an interface to preferentially select the transactions with the earliest deadlines. We then extend the blockchain network to provide the flexibility of the number of generated blocks at a single block time. Under such architectural supports, we achieve (b) with three steps. First, to resolve a discrepancy between a periodic request of a transaction-generating node and the corresponding arrival on a block-generating node, we translate the former into the latter, which eases the modeling of the transaction load imposed on the blockchain network. Second, we derive a schedulability condition of the modeled transaction load, which guarantees no missed deadline for all transactions under a work-conserving deadline-based scheduling policy. Last, we develop a lazy scheduling policy and its condition, which reduces the number of generated blocks without compromising the degree of timing guarantees for the work-conserving policy. By implementing RT-blockchain on top of an existing open-source blockchain project, we demonstrate the effectiveness of the proposed scheduling principles with architectural supports in not only ensuring timely transactions but also reducing the number of generating blocks.

Different from existing MOT (Multi-Object Tracking) techniques that usually aim at improving tracking accuracy and average FPS, real-time systems such as autonomous vehicles necessitate new requirements of MOT under limited computing resources: (R1) guarantee of timely execution and (R2) high tracking accuracy. In this paper, we propose RT-MOT, a novel system design for multiple MOT tasks, which addresses R1 and R2. Focusing on multiple choices of a workload pair of detection and association, which are two main components of the tracking-by-detection approach for MOT, we tailor a measure of object confidence for RT-MOT and develop how to estimate the measure for the next frame of each MOT task. By utilizing the estimation, we make it possible to predict tracking accuracy variation according to different workload pairs to be applied to the next frame of an MOT task. Next, we develop a novel confidence-aware real-time scheduling framework, which offers an offline timing guarantee for a set of MOT tasks based on non-preemptive fixed-priority scheduling with the smallest workload pair. At run-time, the framework checks the feasibility of a priority-inversion associated with a larger workload pair, which does not compromise the timing guarantee of every task, and then chooses a feasible scenario that yields the largest tracking accuracy improvement based on the proposed prediction. Our experiment results demonstrate that RT-MOT significantly improves overall tracking accuracy by up to 1.5 ×, compared to existing popular tracking-by-detection approaches, while guaranteeing timely execution of all MOT tasks.

This paper aims at developing a tight schedulability analysis for real-time global gang scheduling, in which threads of each task subject to timing requirements are assigned to multiple processors in parallel (i.e., following the rigid gang task model). Focusing on the RTA (Response Time Analysis) framework known to exhibit high schedulability performance for other task models, we address two following issues: i) how to generalize the existing RTA framework to gang scheduling and utilize existing RTA components of other task models for the generalized framework, and ii) how to incorporate important characteristics of gang scheduling into the RTA framework in a systematic way to minimize the framework's pessimism in judging schedulability. By addressing the issues, our RTA framework enables to derive tight schedulability analysis for EDF, FP and potentially more scheduling algorithms for real-time global gang scheduling. Also, our simulation results demonstrate that the proposed RTA framework outperforms/complements existing studies for real-time global/non-global gang scheduling, in terms of schedulability performance.

As machine learning (ML) has been proven effective in solving various problems, researchers in the real-time systems (RT) community have recently paid increasing attention to ML. While most of them focused on timing issues for ML applications (i.e., RT for ML), only a little has been done on the use of ML for solving fundamental RT problems. In this paper, we aim at utilizing ML to solve a fundamental RT problem of priority assignment for global fixed-priority preemptive (gFP) scheduling on a multiprocessor platform. This problem is known to be challenging in the case of a large number (n) of tasks in a task set because exhaustive testing of all priority assignments (as many as n!) is intractable and existing heuristics cannot find a schedulable priority assignment, even if exists, for a number of task sets. We systematically incorporate RT domain knowledge into ML and develop an ML framework tailored to the problem, called PAL. First, raising and addressing technical issues including neural architecture selection and training sample regulation, we enable PAL to infer a schedulable priority assignment of a set of n tasks, by training PAL with same-size (i.e., with n tasks) samples each of whose schedulable priority assignment has already been identified. Second, considering the exhaustive testing of all priority assignments of each task set with large n makes it intractable to provide training samples to PAL, we derive inductive properties that can generate training samples with large n from those with small n, through empirical observation of PAL and mathematical analysis of the target gFP schedulability test. Finally, utilizing the inductive properties and additional techniques, we propose how to systematically implement PAL whose training sample generation process not only yields unbiased samples but also is tractable even for large n. Our experimental results demonstrate PAL covers a number of additional task sets, each of which has never been proven schedulable by any existing approaches for gFP.