With widespread use of common-off-the-shelf components and the drive towards connection with external environments, the real-time systems are facing more and more security problems. In particular, the real-time systems are vulnerable to the schedule-based attacks because of their predictable and deterministic nature in operation. In this paper, we present a security-aware real-time scheduling scheme to counteract the schedule-based attacks by preventing the untrusted tasks from executing during the attack effective window (AEW). In order to minimize the AEW untrusted coverage ratio for the system with uncertain AEW size, we introduce the protection window to characterize the system protection capability limit due to the system schedulability constraint. To increase the opportunity of the priority inversion for the security-aware scheduling, we design an online feasibility test method based on the busy interval analysis. In addition, to reduce the run-time overhead of the online feasibility test, we also propose an efficient online feasibility test method based on the priority inversion budget analysis to avoid online iterative calculation through the offline maximum slack analysis. Owing to the protection window and the online feasibility test, our proposed approach can efficiently provide best-effort protection to mitigate the schedule-based attack vulnerability while ensuring system schedulability. Experiments show the significant security capability improvement of our proposed approach over the state-of-the-art coverage oriented scheduling algorithm.

Automotive systems are often subjected to stringent requirements on the maximum data age of certain cause-effect chains. In this paper, we present an efficient method for formally analyzing maximum data age of cause-effect chains. In particular, we decouple the problem of bounding the maximum data age of a chain into a problem of bounding the releasing interval of successive Last-to-Last data propagation instances in the chain. Owing to the problem decoupling, a relatively tighter data age upper bound can be effectively obtained in polynomial time. Experiments demonstrate that our approach can achieve high precision analysis with lower computational cost.

Mobile edge computing enables users to run resource-intensive applications at the network edge equipped with small server clusters. The mobile services are heterogeneous and edge servers are generally resource-limited. Only a subset of services can be processed by an edge server in a time slot. In this paper, we study the joint service placement and computation scheduling (JSPCS) problem to optimize both service quality and operation cost. We formulate the optimization problem to maximize the worst utility among all the services, under the constraints of multiple types of resources, and the JSPCS problem is proved to be NP-hard. By linear relaxation, we provide a dual decomposition approach to decouple this hard problem into a sequence of tractable sub-problems. We propose the Lagrange duality based joint optimal service placement and computation scheduling (LD-JSPCS) algorithm to derive the optimal solution to JSPCS problem with linear relaxation. By iteratively solving a series of feasibility problems, we prove the proposed algorithm guarantees the convergence to the optimum. Theoretical analysis and extensive simulations are performed, validating the efficiency of LD-JSPCS in service provision with limited resources.

Deep Neural Networks (DNNs) are increasingly deployed in battery-powered and resource-constrained devices. However, the most accurate DNNs usually require millions of parameters and operations, making them computation-heavy and energy-expensive, so it is an important topic to develop energy efficient DNN models. In this paper, we present an efficient DNN training framework under energy constraint to improve the energy efficiency of DNN inference. The key idea of this research is inspired by the observation that the input data of DNNs is usually inherently sparse and such sparsity can be exploited by sparse tensor DNN accelerators to eliminate ineffectual data access and compute. Therefore, we can enhance the inference accuracy within the energy budget by strategically controlling the sparsity of the input data. We build an energy consumption model for the sparse tensor DNN accelerator to quantify the inference energy consumption from the perspective of data access and data processing. In particular, we define a metric (named sporadic degree) to characterise the influence of the number of sporadic values in the sparse input on the energy consumption of data access for the sparse tensor DNN accelerator. Based on the proposed quantitative energy consumption model, we present an efficient pooling-based input mask training algorithm to optimize the energy efficiency of DNN inference by enhancing the input sparsity and reducing the number of sporadic values in the masked input. Experiments show that compared with the state-of-the-art methods, our proposed method can achieve higher inference accuracy with lower energy consumption and storage requirement owing to higher sparsity and lower sporadic degree of the masked input.

Directed acyclic graph (DAG) becomes a popular model for modern real-time embedded software. It is really a challenge to bound the worst-case response time (WCRT) of DAG task. Parallelism, dependencies and mutual exclusion become three of the most critical properties of real-time parallel tasks. Recent work applied prioritizing techniques to reduce DAG task's WCRT bound, which has well studied the first two properties, i.e., parallelism and dependencies, but leaves the mutually exclusive property as an open problem. This paper focuses on all the three properties of real-time parallel software, and investigates how to estimate the WCRT of the DAG task model with mutually exclusive vertices and under prioritized list scheduling algorithms. We derive a reasonable WCRT bound for such a complicated DAG task, and prove that the corresponding WCRT bound computation problem is strongly NP-hard. It means that there are no pseudo-polynomial time algorithms to compute the WCRT bound. For the prioritized DAG with a constant number of mutual exclusive vertices, we develop a dynamic programming algorithm that is able to estimate the WCRT bound within pseudo-polynomial time. Experiments are conducted to evaluate the performance of our analysis method implemented with different priority assignment policies against the state-of-the-art.

Many modern real-time parallel applications can be modeled as a directed acyclic graph (DAG) task. Recent studies show that the worst-case response time (WCRT) bound of a DAG task can be significantly reduced when the execution order of the vertices is determined by the priority assigned to each vertex of the DAG. How to obtain the optimal vertex priority assignment, and how far from the best-known WCRT bound of a DAG task to the minimum WCRT bound are still open problems. In this article, we aim to construct the optimal vertex priority assignment and derive the minimum WCRT bound for the DAG task. We encode the priority assignment problem into an integer linear programming (ILP) formulation. To solve the ILP model efficiently, we do not involve all variables or constraints. Instead, we solve the ILP model iteratively, i.e., we initially solve the ILP model with only a few primary variables and constraints, and then at each iteration, we increment the ILP model with the variables and constraints which are more likely to derive the optimal priority assignment. Experimental work shows that our method is capable of solving the ILP model optimally without involving too many variables or constraints, e.g., for instances with 50 vertices, we find the optimal priority assignment by involving 12.67% variables on average and within several minutes on average.

Multiprocessor platforms, widely adopted to realize real-time systems nowadays, bring the probabilistic characteristic to such systems because of the performance variations of complex chips. In this paper, we present a harmonic partitioned scheduling scheme with workload awareness for periodic probabilistic realtime tasks on multiprocessors under the fixed-priority preemptive scheduling policy. The key idea of this research is to improve the overall schedulability by strategically arranging the workload among processors based on the exploration of the harmonic relationship among probabilistic real-time tasks. In particular, we define a harmonic index to quantify the harmonicity among probabilistic real-time tasks. This index can be obtained via the harmonic period transformation and probabilistic cumulative worst case utilization calculation of these tasks. The proposed scheduling scheme first sorts tasks with respect to the workload, then packs them to processors one by one aiming at minimizing the increase of harmonic index caused by the task assignment. Experiments with randomly generated task sets show significant performance improvement of our proposed approach over the existing harmonic partitioned scheduling algorithm for probabilistic real-time systems.