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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 presentRasco, a co-design technique for multicore resource allocation and scheduling of real-time DAG applications with end-to-end deadlines.Rascoleverages the resource-dependent multi-phase model of each task to simultaneously allocate resources at a fine granularityandassign task deadlines. This approach maximizes execution progress under resource constraints while providing hard real-time schedulability guarantees. Our evaluation shows thatRascosubstantially enhances schedulability and reduces end-to-end latency compared to the state of the art. 

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. In this paper, we take a first stab at extending BFT to this larger class of systems. We focus on multi-robot systems (MRS), an emerging technology that is increasingly being deployed for applications such as target tracking, warehouse logistics, and exploration. An MRS can consist of dozens of interacting robots and is thus a bona-fide distributed system. The classic masking guarantee is not practical in a MRS, but we propose a variant called bounded-time interaction that can be implemented, and we present an algorithm that achieves it, in combination with a few small hardware tweaks. We built a simulator and prototyped wheeled robots to show that our algorithm is effective, and that it has a reasonable overhead. 

Aerial vehicles with dozens of rotors are becoming increasingly common in important applications such as transportation and construction. One challenge with building such a system is to ensure that the system is robust against faults: as the number of rotors increases, the likelihood of a rotor failing during operation also increases; despite the spare thrust capacity provided by the redundant rotors, a rotor fault can significantly impact the motion and safety of the system. This paper presents an efficient fault detection and isolation (FDI) method for aerial vehicles with a large number of rotors. Our approach relies on two key insights: First, the effect of a faulty rotor directly affects the tracking error in roll and in pitch. This property can be used to order our faulty rotor search space. Second, the error in either roll or pitch is related to both the distance from the (relevant) axis and the severity of a fault. With these observations, we can use probe faults to isolate faulty rotors. Evaluation results show that our technique can efficiently detect and isolate faults in multi-rotor aerial vehicles with up to 64 rotors (8 more rotors than in existing FDI work), and that it can help improve robustness. To the best of our knowledge, our FDI method is the first that scales to several dozens of rotors. 

Vertical take-off and landing (VTOL) vehicles are becoming increasingly popular for real-world transport; but, as with any vehicle, guaranteeing safety is both extremely critical and highly challenging due to issues like rotor faults. Existing fault detection and isolation (FDI) techniques usually focus on multirotor systems or fixed wing systems, rather than the hybrid VTOLs. Since VTOLs have both rotors and ailerons, a fault in a rotor may be masked by the (correctly working) ailerons, making it much more difficult to detect faults. However, this masking only works when ailersons are used (e.g., during cruising), leaving the takeoff and landing vulnerable to crashes. This paper presents an online rotor fault detection and isolation (FDI) method for VTOLs. The approach uses pose analysis and aileron command data to quickly and accurately identify the faulty rotor and to compute the severity of the fault. Our method works for hard-to-detect fault scenarios, such as small-severity faults that are masked during cruise flight but not during vertical motion. We evaluated our technique in a SITL PX4 simulation of a modified Deltaquad QuadPlane. The results show that our FDI technique can quickly detect and isolate faults in real time (within 1s-2.5s) and achieve high isolation success rate (91.67%) across six rotors, and that it can estimate the severity of faults to within 2%. When applying a simple recovery process post-isolation, the system consistently achieved safe landing. 

In contemporary database applications, the demand for memory resources is intensively high. To enhance adaptability to varying resource needs and improve cost efficiency, the integration of diverse storage technologies within heterogeneous memory architectures emerges as a promising solution. Despite the potential advantages, there exists a significant gap in research related to the security of data within these complex systems. This paper endeavors to fill this void by exploring the intricacies and challenges of ensuring data security in object-oriented heterogeneous memory systems. We introduce the concept of Unified Encrypted Memory (UEM) management, a novel approach that provides unified object references essential for data management platforms, while simultaneously concealing the complexities of physical scheduling from developers. At the heart of UEM lies the seamless and efficient integration of data encryption techniques, which are designed to ensure data integrity and guarantee the freshness of data upon access. Our research meticulously examines the security deficiencies present in existing heterogeneous memory system designs. By advancing centralized security enforcement strategies, we aim to achieve efficient object-centric data protection. Through extensive evaluations conducted across a variety of memory configurations and tasks, our findings highlight the effectiveness of UEM. The security features of UEM introduce low and acceptable overheads, and UEM outperforms conventional security measures in terms of speed and space efficiency. 

Real-time embedded systems perform many important functions in the modern world. A standard way to tolerate faults in these systems is with Byzantine fault-tolerant (BFT) state machine replication (SMR), in which multiple replicas execute the same software and their outputs are compared by the actuators. Unfortunately, traditional BFT SMR protocols areslow, requiring replicas to exchange sensor data back and forth over multiple rounds in order to reach agreement before each execution. The state of the art in reducing the latency of BFT SMR iseager execution, in which replicas execute on data from different sensors simultaneously on different processor cores. However, this technique results in 3–5× higher computation overheads compared to traditional BFT SMR systems, significantly limiting schedulability. We presentCrossTalk, a new BFT SMR protocol that leverages the prevalence of redundant switched networks in embedded systems to reduce latency without added computation. The key idea is to use specific algorithms to move messages between redundant network planes (which many systems already possess) as the messages travel from the sensors to the replicas. As a result,CrossTalkcan ensure agreementautomaticallyin the network, avoiding the need for any communication between replicas. Our evaluation shows thatCrossTalkimproves schedulability by 2.13–4.24× over the state of the art. Moreover, in a NASA simulation of a real spaceflight mission,CrossTalktolerates more faults than the state of the art while using nearly 3× less processor time.