The Internet of Things (IoT) revolution has profoundly transformed modern industries by embedding connected digital devices across critical sectors such as energy, pharmaceuticals, and manufacturing. Such interconnected devices together form Cyber-Physical Systems (CPS), complex structures that enable computation to directly control physical processes. These systems increasingly rely on automation and real-time control to enhance efficiency, productivity, and safety. However, this growing dependence on connectivity has significantly expanded the attack surface, exposing mission-critical infrastructure to sophisticated cyber threats. Among the most dangerous are zero-day vulnerabilities, which are unknown flaws in software or firmware, and zero-day design flaws, which are deeply embedded architectural weaknesses often undetectable by conventional verification methods. Traditional defense mechanisms, largely based on static rules or known threat signatures, are ill-equipped to detect these stealthy and evolving threats - especially in real-time systems, where even minor delays in detection or response can result in catastrophic outcomes. To overcome these limitations, this work presents a transformative security framework that leverages the reasoning and generative capabilities of Large Language Models (LLMs) to construct an intelligent, redundant digital twin of the operational system. Unlike traditional replicas, the LLM-driven digital twin is logically and architecturally isolated, making it inaccessible to attackers targeting the primary system. Crucially, the twin is not a static copy: it is dynamically synthesized and continuously restructured by the LLM to ensure a heterogeneous and evolving codebase. This ensures functional alignment with the physical system while introducing deliberate software diversity, thereby increasing attacker uncertainty and resilience to shared exploits. The central hypothesis of the framework is that semantic deviations between the decisions or behaviors of the digital twin and its physical counterpart can serve as reliable indicators of anomalous activity—whether stemming from external attacks or latent design flaws. By monitoring this divergence, the system facilitates the early detection of zero-day threats and supports real-time mitigation strategies, thereby significantly strengthening the resilience of critical infrastructure. Implementation results demonstrated the effectiveness and efficiency of the proposed approach.

The widespread use of Deep Neural Networks (DNNs) is limited by high computational demands, especially in constrained environments. GPUs, though effective accelerators, often face underutilization and rely on coarse-grained scheduling. This paper introduces DARIS, a priority-based real-time DNN scheduler for GPUs, utilizing NVIDIA’s MPS and CUDA streaming for spatial sharing, and a synchronization-based staging method for temporal partitioning. In particular, DARIS improves GPU utilization and uniquely analyzes GPU concurrency by oversubscribing computing resources. It also supports zero-delay DNN migration between GPU partitions. Experiments show DARIS improves throughput by $15 \%$ and $11.5 \%$ over batching and state-of-the-art schedulers, respectively, even without batching. All high-priority tasks meet deadlines, with low-priority tasks having under 2% deadline miss rate. High-priority response times are 33% better than those of low-priority tasks.

In safety critical computing, real-time and security concerns are often considered separately, though the behavior of a scheduling model itself may be an attack surface which can be exploited by an attacker to reduce system performance. In this work, we explore how the semantics of mode changes in mixed-criticality systems could be used as one such attack vector. This attack, dubbed Mad Monk, uses a mixed criticality scheduler's mode switches against itself by allowing a task of a lower criticality to interfere with tasks of a higher criticality, thereby forcing a disruptive mode switch which could possibly reduce service to some tasks. We describe this attack in detail, along with a case study demonstrating its risk. Furthermore, extensive simulations of this attack demonstrate its potential effectiveness based on a variety of timing and system factors.

Deep Neural Networks (DNNs) are useful in many applications, including transportation, healthcare, and speech recognition. Despite various efforts to improve accuracy, few works have studied DNN in the context of real-time requirements. Coarse resource allocation and sequential execution in existing frameworks result in underutilization. In this work, we conduct GPU speedup gain analysis and propose SGPRS, the first real-time GPU scheduler considering zero configuration partition switch. The proposed scheduler not only meets more deadlines for parallel tasks but also sustains overall performance beyond the pivot point.

Security is increasingly a primary concern in the design of safety-critical embedded systems, yet balancing it with timing constraints is challenging due to limited computing resources. The Multi-Phase Secure (MPS) Sporadic Task Model, proposed in an ISORC-2023 paper, addressed this by balancing overhead from security mechanisms (e.g., trusted-execution environments) with real-time scheduling constraints. However, this model assumed a somewhat pessimistic view of the overhead involved in switching between security mechanisms, often overestimating the necessity of these switches. This paper refines the MPS Sporadic Task Model to more accurately assess when switching security mechanisms is unnecessary, thereby avoiding undue overhead. Our refined model demonstrates a substantial improvement in the schedulability ratio when the utilization of the system approaches one (approximately 15% improvement) for randomly-generated security-aware task systems.

Safety- and mission-critical cyber-physical systems (CPSs) require temporal correctness to ensure safe physical behavior. This manifests as strict timing requirements, which cannot be missed at runtime. Counter-intuitively, this implies that real-time tasks can be delayed so long as they remain guaranteed to meet their deadlines. This paper explores how extra time in a schedule can be analytically recapitalized for the purpose of applying stronger security protection within individual tasks at compile time. This is achieved through the development of a partial context-sensitive pointer-integrity framework (ParCSPI). In this framework, more fine-grained policies can be enforced, with greater runtime overheads, where so doing does not violate real-time constraints. A whole-system optimization framework based upon a mixed-integer linear programming approach to fixed-priority response-time analysis is used to identify precisely which contexts can be checked within the available system-wide time while maximizing system-wide security. ParCSPI leverages Arm pointer authentication (PA) to encode context-based equivalence classes into the modifiers of the pointer signature and is implemented using a customized program analyzer and LLVM compiler passes. An evaluation of ParCSPI is presented that includes per-task and system-wide overhead and security tradeoffs, as well as a demonstration on a real-world CPS. Empirical results are presented showing that ParCSPI achieves up to 62% pointer-integrity protection with only 10% worst-case execution time (WCET) overhead, and can find optimal security trade-offs in complex real-time task sets as well as approximate them in reasonable time.

As vehicles have become increasingly automated, novel vehicular applications have emerged to enhance the safety and security of the vehicles and improve user experience. This brings ever-increasing data and resource requirements for timely computation by the vehicle’s on-board computing systems. To meet these demands, prior work proposes deploying vehicular edge computing (VEC) resources in road-side units (RSUs) in the traffic infrastructure with which the vehicles can communicate and offload compute-intensive tasks. Due to the limited communication range of these RSUs, the communication link between the vehicles and the RSUs — and, therefore, the response times of the offloaded applications — are significantly impacted by vehicle mobility through road traffic. Existing task offloading strategies do not consider the influence of traffic lights on vehicular mobility while offloading workloads onto the RSUs. This causes deadline misses and quality-of-service (QoS) reduction for the offloaded tasks. In this article, we present a novel task model that captures time and location-specific requirements for vehicular applications. We then present a deadline-based strategy that incorporates traffic light data to opportunistically offload tasks. Our approach allows up to 33% more tasks to be offloaded onto RSUs compared with existing work without causing deadline misses, maximizing the resource utilization of RSUs.

Safety-critical embedded systems such as autonomous vehicles typically have only very limited computational capabilities on board that must be carefully managed to provide required enhanced functionalities. As these systems become more complex and inter-connected, some parts may need to be secured to prevent unauthorized access, or isolated to ensure correctness. We propose the multi-phase secure (MPS) task model as a natural extension of the widely used sporadic task model for modeling both the timing and the security (and isolation) requirements for such systems, and develop corresponding scheduling algorithms and associated schedulability tests.

In the world of real-time systems (RTS), security has often been overlooked in the design process. However, with the emergence of the Internet of Things and Cyber-Physical Systems, RTS are now frequently used in interconnected applications where data is shared regularly. Unfortunately, this increased connectivity has also led to a larger attack surface. As a result, it is crucial to redesign RTS to not only meet real-time requirements but also to be resilient to threats. To address this issue, we propose a new real-time security co-design task model, and an accompanying scheduling framework, where schedulability can be used to indicate whether both real-time and security requirements are met. Our algorithm is designed to be flexible, allowing different security mechanisms to be used along with real-time tasks. Specifically, we augment the frame-based task model by introducing an n-dimensional security matrix, which serves as a powerful tool to enable our approach. This matrix clearly indicates which defense mechanisms are available for each task in the system by storing the worst-case execution times of tasks. Then, we transform the problem of maximizing security, subject to schedulability, into a variant of the knapsack problem. To make this approach more practical, we implement a fully polynomial time approximation scheme (FPTAS) that reduces the time complexity of solving the knapsack problem from a pseudo-polynomial to a fully polynomial. We also experiment with a greedy-heuristic approach and compare the results of both algorithms. By using an FPTAS, we were able to significantly improve the efficiency of calculating the maximum security and produce near-optimal results against the optimal solution. Our experiments showed that an FPTAS can process a batch of 10,000 task sets 1.5 times faster than the traditional dynamic programming approach.

The fifteen papers in this special section focus on real-time systems. They present state-of-the-art work in theory, design, analysis, implementation, and evaluation of real-time systems. All the papers address some form of real-time requirements such as deadlines, response times or delays/latency and consider not only hard real-time systems but also time-sensitive systems in general. Following an open call for papers, authors from all over the globe sent 53 submissions on a broad range of topics. The review committee of top experts worldwide conducted rigorous professional reviews. Each paper at least 3 reviews in the first round. Approximately 50 reviews were performed in the second round to evaluate the revised submissions.

Traffic congestion is a costly phenomenon of every-day life. Reinforcement Learning (RL) is a promising solution due to its applicability to solving complex decision-making problems in highly dynamic environments. To train smart traffic lights using RL, large amounts of data is required. Recent RL-based approaches consider training to occur on some nearby server or a remote cloud server. However, this requires that traffic lights all communicate their raw data to some central location. For large road systems, communication cost can be impractical, particularly if traffic lights collect heavy data (e.g., video, LIDAR). As such, this work pushes training to the traffic lights directly to reduce communication cost. However, completely independent learning can reduce the performance of trained models. As such, this work considers the recent advent of Federated Reinforcement Learning (FedRL) for edge-enabled traffic lights so they can learn from each other's experience by periodically aggregating locally-learned policy network parameters rather than share raw data, hence keeping communication costs low. To do this, we propose the SEAL framework which uses an intersection-agnostic representation to support FedRL across traffic lights controlling heterogeneous intersection types. We then evaluate our FedRL approach against Centralized and Decentralized RL strategies. We compare the reward-communication trade-offs of these strategies. Our results show that FedRL is able to reduce the communication costs associated with Centralized training by 36.24%; while only seeing a 2.11 % decrease in average reward (i.e., decreased traffic congestion).

Computing systems, including real-time embedded systems, are becoming increasingly connected to allow for more advanced and safer operation. Such embedded systems are also often resource-constrained, for example, with lower processing capabilities compared to general-purpose computing systems like desktops or servers. With the advent of paradigms such as internet-of-things (IoT), embedded systems in both commercial and industrial contexts are being increasingly interconnected and exposed to the external networks to improve automation and efficiency of operation. However, allowing external interfaces to such embedded systems increases their exposure to attackers. With an increase in attacks against embedded systems ranging from home appliances to industrial control systems operating critical equipment that have real-time requirements, it is imperative that defense mechanisms be created that explicitly consider such resource and real-time constraints. Control-flow integrity (CFI) is a family of defense mechanisms that prevent attackers from modifying the flow of execution. We survey CFI techniques, ranging from the basic to state of the art, that are built for embedded systems and real-time embedded systems and find that there is a dearth, especially for real-time embedded systems, of CFI mechanisms. We then present open challenges to the community to help drive future research in this domain.

Characterizing computational demand of Cyber-Physical Systems (CPS) is critical for guaranteeing that multiple hard real-time tasks may be scheduled on shared resources without missing deadlines. In a CPS involving repetition such as industrial automation systems found in chemical process control or robotic manufacturing, sensors and actuators used as part of the industrial process may be conditionally enabled (and disabled) as a sequence of repeated steps is executed. In robotic manufacturing, for example, these steps may be the movement of a robotic arm through some trajectories followed by activation of end-effector sensors and actuators at the end of each completed motion. The conditional enabling of sensors and actuators produces a sequence of Monotonically Ascending Execution times (MAE) with lower WCET when the sensors are disabled and higher WCET when enabled. Since these systems may have several predefined steps to follow before repeating the entire sequence each unique step may result in several consecutive sequences of MAE. The repetition of these unique sequences of MAE result in a repeating WCET sequence. In the absence of an efficient demand characterization technique for repeating WCET sequences composed of subsequences with monotonically increasing execution time, this work proposes a new task model to describe the behavior of real-world systems which generate large repeating WCET sequences with subsequences of monotonically increasing execution times. In comparison to the most applicable current model, the Generalized Multiframe model (GMF), an empirically and theoretically faster method for characterizing the demand is provided. The demand characterization algorithm is evaluated through a case study of a robotic arm and simulation of 10,000 randomly generated tasks where, on average, the proposed approach is 231 and 179 times faster than the state-of-the-art in the case study and simulation respectively.

Efficient traffic control remains a challenging task, especially during and after special events such as emergency vehicle traversals or blocked links due to disabled vehicles. While existing approaches aim to reduce travel delays, they do not consider recovery from spillbacks caused by such interruptions in the traffic network. This paper (1) presents an optimal algorithm that maximizes the traffic flow through the road network while ensuring that spillbacks do not occur during normal operations, (2) proposes an effective, predictable mitigation strategy to recover from spillbacks caused by special events and which may have propagated through multiple links and/or intersections in the network, and (3) provides worst-case wait time bounds as well as recovery time bounds associated with the proposed techniques. Compared to existing approaches, our optimal strategy shows a 53.2% improvement in worst-case travel times. Additionally, our mitigation strategy can recover from spillbacks that have propagated through multiple links in the network by up to 50.9% quicker than the existing approaches.

Heterogeneous multiprocessor systems on a chips (MPSoCs) consisting of cores with different performance/power characteristics are widely used in many real-time embedded systems, where both soft-error reliability and lifetime reliability are key concerns. Although existing efforts have investigated related problems, they either focus on one of the two reliability concerns or propose time-consuming scheduling algorithms that cannot adequately address runtime workload and environmental variations. This paper introduces an online framework which is adaptive to runtime variations and maximizes soft-error reliability while satisfying the lifetime reliability constraint for soft real-time systems executing on MPSoCs that are composed of high-performance cores and low-power (LP) cores. Based on each core's executing frequency and utilization, the framework performs workload migration between high-performance cores and LP cores to reduce power consumption and improve soft-error reliability. Experimental results based on different hardware platforms show that the proposed approach reduces the probability of failures due to soft errors by at least 17% and 50% on average compared to a number of representative existing approaches that satisfy the same lifetime reliability constraints.

Multiprocessor systems on a chip consisting of integrated CPUs and GPUs are suitable platforms for real-time embedded applications requiring massively parallel processing. For such applications, lifetime reliability due to permanent faults and soft-error reliability due to transient faults are major concerns. Detailed execution profiling has revealed that a CUDA task's CPU execution time significantly increases if the task executes on a different core than the operating system (OS). Based on this observation, an extended task model is introduced to consider the execution time dependencies among tasks and the OS. A hybrid framework is proposed to improve soft-error reliability while satisfying a lifetime reliability constraint for soft real-time systems executing on integrated CPU and GPU platforms. This framework: 1) reduces the total utilization of cores and improves soft-error reliability via off-line task mapping; 2) achieves a higher lifetime reliability through task migration at run time; and 3) improves soft-error reliability by dynamically scaling frequencies of CPU and GPU cores. The experimental results show that the proposed framework leads to a system that can execute without soft errors for at least 4 days (4 times) and 6 days (6 times) longer, on average, than existing approaches.

The Controller Area Network (CAN) is a bus standard commonly used in the automotive industry for connecting Electronic Control Units (ECUs) within a vehicle. The broadcast nature of this protocol, along with the lack of authentication or strong integrity guarantees for frames, allows for arbitrary data injection/modification and impersonation of the ECUs. While mitigation strategies have been proposed to counter these attacks, high implementation costs or violation of backward compatibility hinder their deployment. In this work, we first examine the shortcomings of state-of-the-art CAN intrusion detection and identification systems that rely on multiple frames to detect misbehavior and attribute it to a particular ECU, and show that they are vulnerable to a Hill-Climbing-style attack. Then we propose SIMPLE, a real-time intrusion detection and identification system that exploits physical layer features of ECUs, which would not only allow an attack to be detected using a single frame but also be effectively nullified. SIMPLE has low computational and data acquisition costs, and its efficacy is demonstrated by both in-lab experiments with automotive-grade CAN transceivers as well as in-vehicle experiments, where average equal error rates of close to 0% and 0.8985% are achieved, respectively.

Task parameters in traditional models, e.g., the generalized multiframe (GMF) model, are fixed after task specification time. When tasks whose parameters can be assigned within a range, such as the frame parameters in self-suspending tasks and end-to-end tasks, the optimal offline assignment towards schedulability of such parameters becomes important. The GMF-PA (GMF with parameter adaptation) model proposed in recent work allows frame parameters to be flexibly chosen (offline) in arbitrary-deadline systems. Based on the GMF-PA model, a mixed-integer linear programming (MILP)-based schedulability test was previously given under EDF scheduling for a given assignment of frame parameters in uniprocessor systems. Due to the NP-hardness of the MILP, we present a pseudo-polynomial linear programming (LP)-based heuristic algorithm guided by a concave approximation algorithm to achieve a feasible parameter assignment at a fraction of the time overhead of the MILP-based approach. The concave programming approximation algorithm closely approximates the MILP algorithm, and we prove its speed-up factor is (1+delta)^2 where delta > 0 can be arbitrarily small, with respect to the exact schedulability test of GMF-PA tasks under EDF. Extensive experiments involving self-suspending tasks (an application of the GMF-PA model) reveal that the schedulability ratio is significantly improved compared to other previously proposed polynomial-time approaches in medium and moderately highly loaded systems.

Schedule randomization is one of the recently introduced security defenses against schedule-based attacks, i.e., attacks whose success depends on a particular ordering between the execution window of an attacker and a victim task within the system. It falls into the category of information hiding (as opposed to deterministic isolation-based defenses) and is designed to reduce the attacker's ability to infer the future schedule. This paper aims to investigate the limitations and vulnerabilities of schedule randomization-based defenses in real-time systems. We first provide definitions, categorization, and examples of schedule-based attacks, and then discuss the challenges of employing schedule randomization in real-time systems. Further, we provide a preliminary security test to determine whether a certain timing relation between the attacker and victim tasks will never happen in systems scheduled by a fixed-priority scheduling algorithm. Finally, we compare fixed-priority scheduling against schedule-randomization techniques in terms of the success rate of various schedule-based attacks for both synthetic and real-world applications. Our results show that, in many cases, schedule randomization either has no security benefits or can even increase the success rate of the attacker depending on the priority relation between the attacker and victim tasks.

Safe and efficient traffic control remains a challenging task with the continued increase in the number of vehicles, especially in urban areas. This paper focuses on traffic control at intersections, since urban roads with closely spaced intersections are often prone to queue spillbacks, which disrupt traffic flows across the entire network and increase congestion. While various intelligent traffic control solutions exist for autonomous systems, they are not applicable to or ineffective against human-operated vehicles or mixed traffic. On the other hand, existing approaches to manage intersections with human-operated vehicles cannot adequately adjust to dynamic traffic conditions. This paper presents a technology-agnostic adaptive real-time server based approach to dynamically determine signal timings at an intersection based on changing traffic conditions and queue lengths (i.e., wait times) to minimize, if not eliminate, spillbacks without unnecessarily increasing delays associated with intersection crossings. This work is also the first to provide worst-case bounds on wait time making our approach more dependable and predictable. The proposed approach was validated in simulations and on a realistic hardware testbed with robots mimicking human driving behaviors. Compared to the pre-timed traffic control and an adaptive scheduling based traffic control, our algorithm is able to avoid spillbacks under highly dynamic traffic conditions and improve the average crossing delay in most cases by 10-50%.

Emergency response vehicles' (ERVs) travel is risky, as non-ERV drivers are often unsure of the ERV's next maneuver and how to facilitate its movement. An integer linear program (ILP), introduced in this paper, facilitates the ERV's movement through a transportation link. Leveraging vehicle-to-vehicle communications, information is collected about vehicles on a link section. Then, the ILP finds the ERV's fastest intra-link path. To increase safety, the ILP assigns non-ERVs locations as far away from the ERV as possible while avoiding passing and weaving among vehicles. The ILP can be adapted to different ERV sizes, road types, and surrounding conditions. Sensitivity analysis indicated that scenarios with narrower road segments and higher numbers of non-ERVs led to ERV paths with lane changes and higher computation times. When compared with the current practice requiring non-ERVs to move to the nearest road edge when an ERV with lights and sirens is noticed, the proposed formulation improved the ERV speed while reducing the conflicts and confusion experienced by downstream vehicles.

Technology scaling has increased concerns about transient faults due to soft errors and permanent faults due to lifetime wear processes. Although researchers have investigated related problems, they have either considered only one of the two reliability concerns or presented simple recovery allocation algorithms that cannot effectively use available time slack to improve soft-error reliability. This paper introduces a framework for improving soft-error reliability while satisfying lifetime reliability and real-time constraints. We present a dynamic recovery allocation technique that guarantees to recover any failed task if the remaining slack is adequate. Based on this technique, we propose two scheduling algorithms for task sets with different characteristics to improve system-level soft-error reliability. Lifetime reliability requirements are satisfied by reducing core frequencies for appropriate tasks, thereby reducing wear due to temperature and thermal cycling. Simulation results show that the proposed framework reduces the probability of failure by at least 8% and 73% on average compared to existing approaches.

Engine-triggered tasks are real-time tasks that are released when the crankshaft in an engine completes a rotation, which depends on the angular speed and acceleration of the crankshaft itself. In addition, the execution time of an engine-triggered task depends on the speed of the crankshaft. Tasks whose execution times depend on a variable period are referred to as adaptive-variable rate (AVR) tasks. Existing techniques to calculate the worst-case demand of AVR tasks are either inexact or computationally intractable. In this paper, we transform the problem of finding the worst-case demand of AVR tasks over a given time interval into a variant of the knapsack problem to efficiently find the exact solution. We then propose a framework to systematically reduce the search space associated with finding the worst-case demand of AVR tasks. Experimental results reveal that our approach is at least 10 times faster, with an average runtime improvement of 146 times, for randomly generated tasksets when compared to the state-of-the-art technique.

The split-screen mode in smartphones allows for the simultaneous side-by-side execution of multiple applications, which permits multitasking and improves users' experience. However, such technology results in simultaneously running multiple foreground processes, which increases the power consumption of a smartphone and reduces its battery lifetime. We present an integrated system-level resource management framework that aims to minimize the total energy consumption of a smartphone with negligible impact on the quality of service (QoS) of applications whose resource usage characteristics are not precisely known offline or vary over time. Our proposed solution: 1) leverages applications' offline profiles to detect instantaneous phase changes (i.e., dynamic changes in resource usage patterns) of the workload of a given application at runtime and 2) adaptively adjusts both voltage and frequency settings of the processor and memory bandwidth to achieve the most energy-efficient configuration subject to QoS constraints. Our approach is also able to progressively reduce the energy consumption of newly installed real-world applications for which there exists no prior resource usage data. Experiments on a Nexus 6 smartphone show that our approach achieves an average energy reduction of 23% (19%) and up to 31% (27%) compared to existing work (and default Android governor) for different combinations of real-world applications running side-by-side in split-screen mode. For applications with no prior resource usage data, the proposed framework saves up to 22% (18%) of energy within at most 14 s when compared to existing work (and default Android governor).

Heterogeneous MPSoCs consisting of cores with different performance/power behaviors are widely used in many power-constrained real-time systems. Both soft-error reliability and lifetime reliability are key concerns in such systems. Although existing work have investigated related problems, they either focus on one of the two reliability concerns or propose complicated scheduling algorithms that cannot adequately address run-time workload and environment variations. This paper introduces an on-line heuristic to maximize soft-error reliability while satisfying a lifetime reliability constraint for soft real-time systems executed on MPSoCs composed of high-performance cores and low-power cores. Based on the run-time cores' frequencies and utilizations, the heuristic performs workload migration between the high-performance cores and low-power cores to achieve improved soft-error reliability. Experimental results from both a hardware platform and a simulator show that the proposed algorithm reduces the probability of faults by at least 30% compared to a number of representative existing approaches while satisfying the same lifetime reliability constraints.

This paper studies the problem of maximizing multicore system lifetime reliability, an important design consideration for many real-time embedded systems. Existing work has investigated the problem, but has neglected important failure mechanisms. Furthermore, most existing algorithms are too slow for online use, and thus cannot address runtime workload and environment variations. This paper presents an online framework that maximizes system lifetime reliability through reliability-aware utilization control. It focuses on homogeneous multicore soft real-time systems. It selectively employs a comprehensive reliability estimation tool to deal with a variety of failure mechanisms at the system level. A model-predictive controller adjusts utilization by manipulating core frequencies, thereby reducing temperature, and an online heuristic adjusts the controller sampling window length to decrease the reliability effects of thermal cycling. Experiments with a real quad-core ARM processor and a simulator demonstrate that the proposed approach improves system mean time to failure by 50% on average and 141% in the best case, compared with existing techniques.

Automated vehicles have the potential to revolutionize our nation's transportation systems as they promise to dramatically reduce congestion, accidents, and fuel usage. Namely, as it becomes possible to precisely exert control on and coordinate the movement and placement of vehicles along a stretch of highway, the separation distance between vehicles can be reduced, thus increasing flow and minimizing congestion. Precise and coordinated vehicle controls and placements also improve predictability, resulting in fewer instances of sudden braking, which reduce fuel usage and accidents. Most existing research has focused on the steady-state behaviors and operations of automated vehicles, such as platooning, and assumes complete knowledge of the system, e.g., the number of vehicles and their destinations and/or neglects dynamic or transition operations such as exiting a highway and lane changing. Uncoordinated lane-changing and exiting behaviors by automated vehicles can significantly reduce the flow of traffic as vehicles will require larger separations, are forced to slow down, or worse, collide. In this paper, we present a collision-free runtime approach to efficiently organize the departures of automated vehicles from a highway environment while maintaining highway capacity in extremely dynamic conditions. To maximize the number of safe departures, the key ideas are to: 1) determine when, and where to, an exiting vehicle should lane change in order to make a successful exit given current traffic conditions as provided by connected vehicle technology and 2) execute the actual lane-change operations using a reservation-based approach. Simulation results show that, by coordinating vehicles' behaviors, traffic flow can be improved by up to five times of today's typical flow while ensuring a 100% exit success rate in a collision-free manner.

System lifetime reliability is an important design consideration for many real-time embedded systems. Increasing integrated circuit power density and the subsequent rise in chip temperature negatively impact the lifetime reliability of such systems. Although existing thermal-aware methods are effective in reducing temperature, they cannot increase, and may even hamper, the system lifetime reliability. The complicated relationship between temperature and system lifetime requires that reliability be considered explicitly during system design. This paper presents a reliability-aware utilization control framework for homogeneous multicore soft real-time systems. The framework employs a model predictive controller to increase the system lifetime by manipulating the utilization of real-time tasks. An online heuristic algorithm is introduced to adjust the controller's sampling window in order to reduce the effects of thermal cycling on reliability. Simulation results show that the proposed approach can improve the system mean time to failure by at least 43% and as much as 369% compared to existing techniques.

In a distributed real-time system (DRTS), jobs are often executed on a number of processors and must complete by their end-to-end deadlines. Job deadline requirements may be violated if resource competition among different jobs on a given processor is not considered. This paper introduces a distributed, locally optimal algorithm to assign local deadlines to the jobs on each processor without any restrictions on the mappings of the applications to the processors in the distributed soft real-time system. Improved schedulability results are achieved by the algorithm since disparate workloads among the processors due to competing jobs having different paths are considered. Given its distributed nature, the proposed algorithm is adaptive to dynamic changes of the applications and avoids the overhead of global clock synchronization. In order to make the proposed algorithm more practical, two derivatives of the algorithm are proposed and compared. Simulation results based on randomly generated workloads indicate that the proposed approach outperforms existing work both in terms of the number of feasible jobs (between 51% and 313% on average) and the number of feasible task sets (between 12% and 71% on average).

Vehicles that are becoming more highly automated are revolutionizing the world's transportation systems for their promise of increased safety and efficiency. The advantage of vehicles incorporating automation is that they do not suffer from the same limitations as human drivers, such as being distracted or impaired. In order to realize the potential of these vehicles, which operate in highly dynamic environments, online techniques are needed. This paper presents such an algorithm to minimize the disruption of traffic flow by optimizing for the number of safe lane changes, thereby increasing throughput and reducing congestion. The proposed algorithm is distributed in nature and makes use of vehicle-to-vehicle and/or vehicle-to-infrastructure communication technologies to judiciously make local lane-change decisions while guaranteeing that no collisions will occur. In contrast to existing work, the proposed technique requires no assumption on the number of lanes, nor on the dynamic attributes of the vehicles such as velocity and acceleration. Simulation results show that the proposed algorithm is both efficient and effective in maximizing the number of lane changes on a given stretch of a highway.

System reliability is a crucial concern especially in multicore systems which tend to have high power density and hence temperature. Existing reliability-aware methods are either slow and non-adaptive (offline techniques) or do not use task assignment and scheduling to compensate for uneven core wear states (online techniques). In this article, we present a dynamically-activated task assignment and scheduling algorithm based on theoretical results that explicitly optimizes system life-time. We also propose a data distillation method that dramatically reduces the size of the thermal profiles to make full system reliability analysis viable online. Simulation results show that our algorithm results in between 27–291% improvement to system lifetime compared to existing techniques for four-core systems.

We consider wireless real-time systems that execute computationally-intensive applications and must transmit packets over the network in a timely manner. Existing methods do not consider the importance (i.e., urgency) of a packet as perceived by end users in conjunction with energy consumption, real-time task deadlines, and packet deadlines, inadvertently causing packet priority inversion during transmissions and possibly starvation of some streams. We present an online holistic scheduling framework that explicitly considers packet importance to select packets to transmit and guarantee their deadline requirements using both packet and energy-aware job assignment and scheduling. Our framework is applicable to wireless real-time systems equipped with either a single processor or a multicore system. Based on extensive simulations, we show that our proposed method allows for timely transmissions of the most important packets, which helps to control packet urgency, while saving processor(s) energy.

In a distributed real-time system, jobs are often executed on a number of processors and must be completed by their end-to-end deadlines. Without considering resource competition among different jobs on each processor, deadline requirements may be violated. The paper introduces a distributed approach to assigning local deadlines to the jobs on each processor. The approach leads to improved schedulability results by considering disparate workloads among the processors due to competing jobs having different paths. Simulation results based on randomly generated workloads indicate that the proposed approach outperforms existing work in terms of both the number of feasible task sets (between 22% and 75%) and the number of feasible jobs (between 57% and 46%).

Increasing integrated circuit (IC) power densities and temperatures may hamper multiprocessor system-on-chip (MPSoC) use in hard real-time systems. This paper formalizes the temperature-aware real-time MPSoC assignment and scheduling problem and presents an optimal phased steady-state mixed integer linear programming-based solution that considers the impact of scheduling and assignment decisions on MPSoC thermal profiles to directly minimize the chip peak temperature. We also introduce a flexible heuristic framework for task assignment and scheduling that permits system designers to trade off accuracy for running time when solving large problem instances. Finally, for task sets with sufficient slack, we show that inserting idle times between task executions can further reduce the peak temperature of the MPSoC quite significantly.

Increasing power densities and the high cost of low thermal resistance packages and cooling solutions make it impractical to design processors for worst-case temperature scenarios. As a result, packages and cooling solutions are designed for less than worst-case power densities and dynamic voltage and frequency scaling (DVFS) is used to prevent dangerous on-chip temperatures at run time. Unfortunately, DVFS can cause unpredicted drops in performance (e.g., long response times). We propose and optimally solve the problem of thermally-constrained online work maximization for general-purpose computing systems on uniprocessors with discrete speed levels and non-negligible transition overheads. Simulation results show that our approach completes 47.7% on average and up to 68.0% more cycles than a naive policy.

The elastic task model is a powerful model for adapting periodic real-time systems in the presence of uncertainty. This work generalizes the existing elastic scheduling approach in several directions. First, it presents a general framework, which formulates a trade-off between task schedulability and a specific performance metric as an optimization problem. Such a framework allows real-time systems under overloads to graciously adapt by adjusting their performance level. Second, it is shown in this work that the well-known task compression algorithm in fact solves a quadratic programming problem that seeks to minimize the sum of the squared deviation of a task's utilization from initial desired utilization. This finding indicates that the task compression algorithm may be applied to efficiently solve other similar types of problems that often arise in real-time applications. In particular, an iterative approach is proposed to solve the period selection problem for real-time tasks with deadlines less than respective periods. Further, the framework is adapted to solve the deadline selection problem, which is useful in some control systems with fixed periods.

Thermal effects in MPSoCs may cause the violation of timing constraints in real-time systems. This paper presents a mixed integer linear programming based solution to this problem. Tasks are assigned and scheduled to an MPSoC to minimize peak temperature, subject to real-time constraints. The proposed approach outperforms existing methods, reducing peak temperature by up to 24.66degC and by an average of 8.75degC when compared to minimal-energy solutions. We also present a heuristic for use on large problem instances. Steady- state thermal analysis is used for tasks with long execution times compared to the RC thermal time constants of the cores. Transient analysis is used otherwise. The steady-state analysis based heuristic finds solutions with at most 3.40degC deviation from optimal peak temperature (0.22degC on average) while improving upon existing technique by as much as 25.71degC and 10.86degC on average. The transient analysis based heuristic further reduce peak temperature by 1degC in the best case and 0.17degC on average.

Task period adaptations are often used to alleviate temporal overload conditions in real-time systems. Existing frameworks assume that only task periods are adjustable and that task deadlines remain unchanged at all times. This paper formally introduces a more general real-time task model where task deadlines, which are less than or equal to task periods, are functions of task periods. This tight coupling between task deadlines and task periods has been discussed in a recent work in control systems and presents a novel real-time scheduling challenge. To solve the period and deadline selection problem, this article identifies a feasible period-deadline combination and proposes a heuristic, which iteratively adjusts task periods and deadlines in such a way that the task set becomes schedulable. Experimental results show that the heuristic finds a solution to the period and deadline selection problem over 73% of the time, using less than three search iterations. When it is unable to find a solution to the problem, the heuristic requires less than 0.02s to run in the worst-case (with at most 100 search iterations).

Increasing power densities due to process scaling, combined with high switching activity and poor cooling environments during testing, have the potential to result in high integrated circuit (IC) temperatures. This has the potential to damage ICs and cause good ICs to be discarded due to temperature-induced timing faults. We first study the power impact of scan chain testing for the ISCAS89 benchmarks. We find that the scan-chain test power consumption is 1.6× higher for at-speed testing than normal operating power consumption. We conclude that if the testing frequency is less than half of the normal frequency, then the testing power consumption may in fact be lower. However, due to differences in the cooling environments, the peak die temperatures may still be higher. Second, we present an optimal formulation for minimal-duration temperature-constrained test scheduling. Our results improve on the test schedule time of the best existing algorithm by 10.8% on average for a packaged IC thermal environment. We also present an efficient heuristic that generally produces the same results as the optimal algorithm, while requiring little CPU time, even for large problem instances.

Reducing energy consumption is an important consideration in embedded real-time system development. This work examines systems that contain a DVFS managed CPU executing packet producing tasks and a DPM-controlled network interface. We introduce a novel approach to minimize energy consumed by the network resource on such a system, through careful selection of voltage and frequency levels on the CPU. Contrary to existing claims which state that DVFS should not be employed when the CPU is not a significant consumer of energy, we show that our DVFS technique can reduce system energy by as much as 35%, even when the CPU energy consumption is negligible. Furthermore, we motivate the need to balance the CPU and network energy and present two techniques to do so. One is based on off-line analysis and the other is a conservative on-line approach. We then validate the proposed methods using both simulation and an implementation in the Linux kernel

The elastic task model (Buttazzo et al., 2002) is a powerful model for adapting real-time systems in the presence of uncertainty. This paper generalizes the existing elastic scheduling approach in several directions. It reveals that the original task compression algorithm in (Buttazzo et al., 2002) in fact solves a quadratic programming problem that seeks to minimize the sum of the squared deviation of a task's utilization from initial desired utilization. This finding indicates that the task compression algorithm may be applied to efficiently solve other similar types of problems. In particular, an iterative approach is proposed to solve the task compression problem for real-time tasks with deadlines less than respective periods. Furthermore, a new objective for minimizing the average difference of task periods from desired values is introduced and a closed-form formula is derived for solving the problem without recursion