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Multi-Processor Systems-on-Chip (MPSoCs) have emerged as an evolution trend to meet the growing complexity of embedded applications with increasing computation parallelism. Particularly, real-time applications make out a significant portion of the embedded field. Networks-on-Chip (NoCs) are the backbone of communications in an MPSoC platform. However, the use of NoCs in real-time systems imposes complex constraints on the overall design. This paper discusses the challenges faced, when designing NoCs for real-time applications. Contributions in this area are surveyed on the level of guaranteed Quality-of-Service (QoS) support, adaptivity, and energy efficient techniques. Furthermore, the evaluation methodologies and experimental performance measurements of real-time NoCs are examined. This survey provides a comprehensive overview of existing endeavors in real-time NoCs and gives an insight towards future promising research points in this field. [ABSTRACT FROM AUTHOR]

On emerging multicore systems, task replication is a powerful way to achieve high reliability targets. In this paper, we consider the problem of achieving a given reliability target for a set of periodic real-time tasks running on a multicore system with minimum energy consumption. Our framework explicitly takes into account the coverage factor of the fault detection techniques and the negative impact of Dynamic Voltage Scaling (DVS) on the rate of transient faults leading to soft errors. We characterize the subtle interplay between the processing frequency, replication level, reliability, fault coverage, and energy consumption on DVS-enabled multicore systems. We first develop static solutions and then propose dynamic adaptation schemes in order to reduce the concurrent execution of the replicas of a given task and to take advantage of early completions. Our simulation results indicate that through our algorithms, a very broad spectrum of reliability targets can be achieved with minimum energy consumption thanks to the judicious task replication and frequency assignment. [ABSTRACT FROM PUBLISHER]

This paper proposes an N-modular redundancy (NMR) technique with low energy-overhead for hard real-time multi-core systems. NMR is well-suited for multi-core platforms as they provide multiple processing units and low-overhead communication for voting. However, it can impose considerable energy overhead and hence its energy overhead must be controlled, which is the primary consideration of this paper. For this purpose the system operation can be divided into two phases: indispensable phase and on-demand phase. In the indispensable phase only half-plus-one copies for each task are executed. When no fault occurs during this phase, the results must be identical and hence the remaining copies are not required. Otherwise, the remaining copies must be executed in the on-demand phase to perform a complete majority voting. In this paper, for such a two-phase NMR, an energy-management technique is developed where two new concepts have been considered: i) Block-partitioned scheduling that enables parallel task execution during on-demand phase, thereby leaving more slack for energy saving, ii) Pseudo-dynamic slack, that results when a task has no faulty execution during the indispensable phase and hence the time which is reserved for its copies in the on-demand phase is reclaimed for energy saving. The energy-management technique has an off-line part that manages static and pseudo-dynamic slacks at design time and an online part that mainly manages dynamic slacks at run-time. Experimental results show that the proposed NMR technique provides up to 29 percent energy saving and is 6 orders of magnitude higher reliable as compared to a recent previous work. [ABSTRACT FROM PUBLISHER]

We propose FIT, a flexible, lightweight, and real-time scheduling system for wireless sensor platforms. There are three salient features of FIT. First, its two-tier hierarchical framework supports customizable application-specific scheduling policies, hence, FIT is very flexible. Second, FIT is lightweight in terms of minimizing the thread number to reduce preemptions and memory consumption while at the same time ensuring system schedulability. We propose a novel Minimum Thread Scheduling Policy (MTSP) exploration algorithm within FIT to achieve this goal. Finally, FIT provides a detailed real-time schedulability analysis method to help check if application's temporal requirements can be met. We implemented FIT on MicaZ motes and carried out extensive evaluations. Results demonstrate that FIT is indeed flexible and lightweight for implementing real-time applications, at the same time, the schedulability analysis provided can predict the real-time behavior. FIT is a promising scheduling system for implementing complex real-time applications in sensor networks. [ABSTRACT FROM AUTHOR]

Energy-efficient task allocation and scheduling schemes with deterministic fault-tolerant capabilities are proposed for symmetric multiprocessor systems executing tasks with hard real-time constraints. The proposed heuristic achieves energy savings by optimally balancing the application workload among processors in a system. Based on the observation that a fault-free operation is expected to remain dominant in the near future and the probability of the worst case faults is low, an optimistic fault-tolerant heuristic is then proposed to achieve optimum energy savings in the absence of faults and meet application timing requirements in the worst case faults at the cost of energy inefficiency. Extensive simulation experiment results show that when compared to the state-of-art schemes, the proposed optimistic heuristic achieves average energy savings of up to 70 percent and exhibits higher tolerance to variations in application utilizations and more resilience to fault occurrences beyond system specification. [ABSTRACT FROM AUTHOR]

Recent technological advances have opened up a wide range of distributed real-time applications involving battery-driven embedded devices with local processing and wireless communication capabilities. Some such applications include: wireless sensor networking, pervasive computing, and wireless industrial automation. Energy management is an integral part of the design and operation of such systems. In this work, we focus on the end-to-end energy management of data aggregation in distributed wireless sensor networks. Performing end-to-end energy management for the data aggregation application poses certain unique challenges particularly when the computational demands on the individual nodes are significant. In this paper, we address the problem of minimizing the total energy consumption of data aggregation with an end-to-end latency constraint while taking into account both the computational and communication workloads in the network. We consider a model where individual nodes support both Dynamic Voltage Scaling (DVS) and Dynamic Modulation Scaling (DMS) power management techniques and explore the energy-time tradeoffs these techniques offer. Specifically, we make the following contributions in this paper. First, we present an analytical problem formulation for the ideal case where each node can scale its frequency and modulation continuously. Second, we present a Mixed Integer Linear Programming (MILP) formulation to obtain the optimal solution for the practical case where only few discrete frequency and modulation levels are supported by each node. Further, we analyze the system-level energy-time tradeoffs considering both the computation and communication workloads by defining a novel energy gain metric. Finally, we present polynomial time heuristic algorithms, which employ the energy-gain metric. We evaluated the performance of the proposed algorithms for a variety of scenarios and our results show that the energy savings obtained by the proposed algorithms are comparable to that of the MILP solution. [ABSTRACT FROM AUTHOR]

Real-time image transmission is crucial to an emerging class of distributed embedded systems operating in open network environments. Examples include avionics mission replanning over Link-16, security systems based on wireless camera networks, and online collaboration using camera phones. Meeting image transmission deadlines is a key challenge in such systems due to unpredictable network conditions. In this paper, we present CAMRIT, a Control-based Adaptive Middleware framework for Real-time Image Transmission in distributed real-time embedded systems. CAMRIT features a distributed feedback control loop that meets image transmission deadlines by dynamically adjusting the quality of image tiles. We derive an analytic model that captures the dynamics of a distributed middleware architecture. A control-theoretic methodology is applied to systematically design a control algorithm with analytic assurance of system stability and performance, despite uncertainties in network bandwidth. Experimental results demonstrate that CAMRIT can provide robust real-time guarantees for a representative application scenario. [ABSTRACT FROM AUTHOR]

Many real-time systems must control their CPU utilizations in order to meet end-to-end deadlines and prevent overload. Utilization control is particularly challenging in distributed real-time systems with highly unpredictable workloads and a large number of end-to-end tasks and processors. This paper presents the decentralized end-to-end utilization control (DEUCON) algorithm, which can dynamically enforce the desired utilizations on multiple processors in such systems. In contrast to centralized control schemes adopted in earlier works, DEUCON features a novel decentralized control structure that requires only localized coordination among neighbor processors. DEUCON is systematically designed based on advances in distributed model predictive control theory. Both control-theoretic analysis and simulations show that DEUCON can provide robust utilization guarantees and maintain global system stability despite severe variations in task execution times. Furthermore, DEUCON can effectively distribute the computation and communication cost to different processors and tolerate considerable communication delay between local controllers. Our results indicate that DEUCON can provide a scalable and robust utilization control for large-scale distributed real-time systems executing in unpredictable environments. [ABSTRACT FROM PUBLISHER]