The responsiveness of autonomous driving (AD) systems is highly important for safety and performance of the vehicles. It is defined as end-to-end latency from sensing the surrounding environment to executing control signals, which determines how quickly the vehicle changes the driving behavior in response to an external event. The AD software stacks are modeled as a directed acyclic graph (DAG) of multi-rate tasks with variable execution times, where the probabilistic end-toend latencies (PEELs) of multiple task sequences need to be minimized on a multi-core processor. This paper proposes a novel schedule synthesis that reduces the PEELs of task sequences of interest under multi-core partitioned EDF scheduling. In the first stage, we generate a baseline schedule to avoid non-usable idle intervals as much as possible by estimating a lower bound on the total non-usable idle time. A non-usable idle interval is an idle interval that cannot be utilized by tasks preceding the interval, thus worsening the graph schedulability. In the second stage, we formulate the problem of reducing the PEELs of multiple task sequences under the baseline schedule into a multi-objective optimization of task phases and search for an optimal phase combination. Through experiments with a real AD stack called Autoware as well as synthetic task graphs, we show that our synthesis significantly improves the graph schedulability and the resulting end-to-end tail latencies.

Good responsiveness of autonomous vehicle computing systems is crucial to safety and performance of the vehicles. For example, an autonomous vehicle (AV) may cause an accident if the end-to-end latency from sensing a pedestrian to emergency stop is too high. However, the AV software stacks are too complex to probabilistically analye the end-to-end latency on a multi-core system. They consist of a graph of tasks with different periods, and have a large variability in the task execution times, which may lead to the maximum core utilization Umax >= 1.0 on some cores. This paper proposes a novel stochastic analysis of the end-to-end latency over the AV stacks that allows Umax to exceed 1.0 on each core. The proposed analysis models the entire stack as a graph of task graphs under a multi-core partitioned scheduling and provides a probabilistic guarantee that the analyzed latency distribution upper-bounds the one observed from a real system under the assumption of independent task execution times. Using the Autoware stack with inter-task dependent execution times, it is shown that our analysis, combined with a task grouping to mitigate the inter-task correlations, can give a latency distribution for each task path that almost upper-bounds the observed one.

Estimation techniques such as Unscented Kalman filter (UKF) are deployed for accurate joint location estimation in cooperative localization of cyber physical systems (CPS), e.g., to locate each robot in a cooperative mobile robotic network in GPS-denied environments. In order to avoid single point of failure in the centralized implementation of such estimation techniques, the decentralization of estimation algorithms has attracted considerable attention in the past two decades. However, the design of decentralized algorithms with reduced communication cost without loss in accuracy for compute-intensive estimation techniques such as UKF has been challenging. In the decentralized UKF, the tasks are partitioned and computed locally at robot nodes. Data communication overhead is overwhelming due to tight data dependency between the robots' computations. In this paper, we present a CPS framework for UKF decentralization in which computation and communication are tightly intertwined and computation replication is deployed in order to reduce the communication overhead among cooperative mobile robots. We demonstrate and evaluate the performance of our proposed work in a wireless network of 15 Raspberry Pi 3 B, with quad-core 1.2GHz 64bit CPU, emulating a network of mobile robots with onboard computation and communication capabilities. Our experimental results show that the End-to-End execution time of decentralized UKF prediction and update steps with replication are faster by up to 12.29 and 3.57 times, respectively, compared to the partially decentralized UKF algorithm of [1].

In this paper, we present MQMAI, multi-queue command-based data transfer mechanism that enables efficient and scalable access to multiple accelerators on FPGA via PCIe. The proposed framework provides a user-friendly non-blocking API with enhanced I/O parallelism to access FPGA-based accelerators. MQMAI is composed of I/O software stack in host CPU and accelerator controller in FPGA. A round-robin policy is developed to allow efficient run-time access to shared accelerators requested through multiple threads/applications. Our experimental results show that our proposed framework reduces the total latency of multi accelerators access significantly compared to the current state-of-the-art framework, RIFFA, by 18x for 4 threads per accelerator access.

Motion control systems require an isochronal real-time guarantee that each control task should periodically produce outputs with no jitters. However, it is difficult to build up such a tight isochronal system with a multicore architecture and a general-purpose operating system, because the inherent resource sharing principle leads to large jitters to the control tasks. This paper proposes a software pipelining framework for an EtherCAT-based motion controller that achieves a tight isochronal guarantee with that combination. The tight guarantee is possible by multicore partitioning and reservation-aware task phasing, which reduce resource contentions between the tasks on each stage of the pipeline. Through experiments, we show that the proposed pipelining framework gives a tight isochronal guarantee with high scalability in terms of the number of motion transactions. On a real 8-axis motion control platform with two processor cores dedicated to the pipeline and a slight modification of the Linux operating system, it achieves a maximum jitter of 10 μs for four motion transactions with a common period of 1.6 ms, whereas a priority-driven method gives a maximum jitter of a few hundreds of microsecond under the same condition.

This paper proposes a novel technique called mRT-PLRU (Multitasking Real-Time constrained combination of Pinning and LRU), which forms a generic framework to use inexpensive nonvolatile NAND flash memory for storing and executing real-time programs in multitasking environments. In order to execute multiple real-time tasks stored in NAND flash memory with the minimal usage of expensive RAM, the mRT-PLRU is optimally configured in two steps. In the first step, the per-task analysis finds the function of RAM size versus execution time (and the corresponding optimal pinning/LRU combination) for each individual task. Using these functions for all the tasks as inputs, the second-step called a stochastic-analysis-in-loop optimization conducts an iterative convex optimization with the stochastic analysis for the probabilistic schedulability check. As a result, the optimization loop can optimally determine the RAM sizes for multiple tasks such that their deadlines are probabilistically guaranteed with the minimal size of total RAM. The usefulness of the developed technique is intensively verified through both simulation and actual implementation. Our experimental study shows that mRT-PLRU can save up to 80 percent of RAM required by the industry-common shadowing approach.

This paper proposes a systematic design approach for a precision-guaranteed motion control system. We develop a delay-guaranteed motor drive with our new software implementation and real-time Ethernet, which can be used as a building block to build up a multi-axis motion control system. Our drive software implementation provides a probabilistic guarantee on drive-local processing delays to motor actuation, while real-time Ethernet provides a deterministic guarantee on message communication delays from a motion control host to each drive. In the paper, we address the precision of a motion control system in two terms: host cycle time and simultaneous actuation deviation. The host cycle time is a period with which the host can periodically release motor control messages while the average drive utilization does not exceed 1, and the simultaneous actuation deviation is the difference between the earliest and the latest actuation at different drives in response to the same message. In our approach, the main objective is to minimize the periods of tasks in each drive, using our stochastic analysis, which gives us a minimum possible host cycle time. Together with an existing delay analysis of real-time Ethernet, we analyze the end-to-end delay from message release to motor actuation and in turn the simultaneous actuation deviation. Through experiments, we show that for various requirements on the deadline miss probabilities of the tasks, we can successfully reduce the host cycle time and evaluate the resulting distribution of the simultaneous actuation deviation depending on the number of drives.

NAND flash memory has been widely used as a nonvolatile storage for storing data. However, it is challenging to execute program codes on NAND flash memory, since NAND flash memory only supports page-based reads, not byte-level random reads. This paper proposes an automated process to find the optimal paging strategy called RT-PLRU (Real-Time constrained combination of Pinning and LRU) that allows program codes stored in NAND flash memory to be executed satisfying real-time requirements with minimal usage of RAM. Moreover, the proposed process optimally configure the RT-PLRU in a developer-transparent way without giving any burden to the program developer. The developed technique is specifically applied to a media player program targeting a portable media player (PMP). To the best of our knowledge, this is the first effort to use NAND flash memory as a code storage for storing and executing real-time programs with minimal usage of RAM.

For advanced features of next generation vehicles, the real-time programs in automotive embedded systems are dramatically increasing. For such large volume program codes, this paper proposes a novel framework to use high-density and low-cost nonvolatile memory, i.e., NAND flash memory, as a low-cost mean of storing and executing hard real-time programs. Regarding this, one challenge is that NAND flash memory allows only 2KB page-based read operations not per-byte random access, which requires RAM as working storage for code executions. In order to minimize the expensive RAM requirements, the proposed framework optimally partitions the RAM for multiple hard real-time tasks and optimally determines the pinning/LRU combination for each RAM partition such that all task deadlines are deterministically guaranteed. The proposed framework is verified with the actual real-time programs for unmanned autonomous driving. To the best of our knowledge, this is the first work that allows us to use NAND flash memory for hard real-time program executions with the minimal usage of RAM.

This paper proposes a novel technique called mRT-PLRU (multi-tasking real-time constrained combination of pinning and LRU), which forms a generic framework to use inexpensive nonvolatile NAND flash memory for storing and executing real-time programs in multi-tasking environments. In order to execute multiple real-time tasks stored in NAND flash memory with the minimal usage of expensive RAM, the mRT-PLRU is optimally configured in two steps. In the first step, the per-task analysis finds the function of RAM size vs. execution time for each individual task. Using these functions for all the tasks as inputs, the second-step called a stochastic-analysis-in-loop optimization conducts an iterative convex optimization with the stochastic-analysis for the probabilistic schedulability check. As a result, the optimization loop can optimally allocate RAM to multiple tasks such that their deadlines are probabilistically guaranteed with the minimal usage of RAM. Moreover, the mRT-PLRU is optimally configured in a developer-transparent way without giving any burden to the program developer, which is essential for the embedded system industry under a high pressure of time-to-market. The usefulness of the developed technique is intensively verified through both simulation and actual implementation. Our experimental study shows that mRT-PLRU can save up to 80% of RAM required by the industry-common shadowing approach.

This paper proposes a safe stochastic analysis for fixed-priority scheduling, which is applicable to a broader spectrum of periodic tasks than the ones analyzable by any of the existing techniques. The proposed analysis can find a safe upper-bound of deadline miss probability for periodic tasks with (1) arbitrary execution time distributions, (2) varying interrelease times with the period as the minimum, and (3) the maximum utilization factor Umaxthat can be greater than 1. One challenge for this is that the release times of tasks are not known a priori because we are not limiting the interrelease times of each task to a constant, i.e., the period. In such a situation, the relative phases of task instances at run time can be arbitrary. Thus, we need to consider all possible phase combinations among jobs to find the worst case deadline miss probability, which is not tractable. To handle this difficulty, we first derive the worst case phase combination for harmonic task sets. Then, we present a safe way to transform a nonharmonic task set to a harmonic task set such that the deadline miss probabilities obtained with the worst case phase combination for the transformed harmonic task set are guaranteed to be worse than those for the original nonharmonic task set with all possible phase combinations. Therefore, the worst case deadline miss probabilities of the transformed harmonic tasks can be used as safe upper-bounds of deadline miss probabilities of the original nonharmonic tasks. Through experiments, we show that the safe upper-bound computed by the proposed analysis is tight enough for practical uses.

NAND flash memory has been widely used as a non-volatile storage for storing data. However, it requires a large amount of SRAM for executing program codes stored in it since it only supports page-based reads, not byte-level random reads. This paper proposes a new paging mechanism called RT-PLRU (real-time constrained combination of pinning and LRU) that allows program codes stored in NAND flash memory to be executed satisfying real-time requirements with minimal usage of SRAM. Moreover, the RT-PLRU is optimally configured in a developer-transparent way without giving any burden to the program developer. The developed technique is specifically applied to a media player program targeting a PMP (portable medial player). To the best of our knowledge, this is the first effort to use NAND flash memory as a code storage for storing and executing real-time programs with minimal usage of SRAM.

Application launching times in embedded systems are more crucial than in general-purpose systems since the response times of embedded applications are significantly affected by the launching times. As general-purpose operating systems are increasingly used in embedded systems, reducing appli-cation launching times are one of the most influential factors for performance improvement. In order to reduce the application launching times, three factors should be considered at the same time: relocation time, symbol resolution time, and binary loading time. In this paper, we propose a new application execution model using a combination of prelinking and preloading to reduce the relocation, symbol resolution, and binary load overheads at the same time. Such application execution model is realized using fork and dlopen execution model instead of traditional fork and exec execution model. We evaluate the performance effect of the proposed fork and dlopen application execution model on a Linux-based embedded system using XScale processor. By applying the proposed application execution model using both prelinking and preloading, the application launching times are reduced up to 71% and relocation counts are reduced up to 91% in the benchmark programs we used.

This paper presents a simple query-based uplink scheduling scheme in wireless packet systems to provide QoS of uplink multimedia traffic. The query timer in base station controls the periodicity of resource request which is normally realized by frequent random accesses in demand-based resource request schemes. It is shown that the scheme dramatically reduces the number of random accesses by 90% on average, as compared to the demand-based resource request scheme, with similar performance in packet delay, MAC (medium access control) overhead.

This paper proposes a novel task model in which its physical and temporal parameters are specified as time-parameterized functions and their values are finally determined at the actual dispatch time. This model is clearly differentiated from the classical task model where parameters are fixed at the job release time. The new model better suits sensing tasks in tracking applications, since the sensor parameters such as field-of-view and measurement duration can be properly adjusted at the actual sensing time. The new model, however, creates the cyclic dependency between task parameters and scheduling behavior, that is, the task parameters depend on scheduling behavior and the latter in turn depends on the former. This cyclic dependency makes the schedulability check even more difficult. We handle this difficulty by iterative convergence and probabilistic schedulability envelope, which provides an efficient online schedulability check. The experimental study shows that the new model significantly improves the effective capacity of tracking systems without losing track accuracy

Ranging in wireless access is the process to acquire up-link synchronization, control the power between personal subscriber station and base station and perform the bandwidth request to BS. In this paper, we evaluate the capacity of a ranging subchannel in terms of the ranging code error probability versus the number of active users to attempt ranging, referring to specifications for the Korea's 2.3 GHz band wireless broadband portable Internet service based on the IEEE 802.16-2004 standard. We consider the additive white Gaussian noise and the two-ray Rayleigh fading channels. The result shows that the capacity in the case of two-ray Rayleigh fading channel decreases about one-third, comparing with that in the case of additive white Gaussian noise channel.

Ranging in wireless access is the process to acquire up-link synchronization, control the power between personal subscriber station and base station and perform the bandwidth request to BS. In this paper, we evaluate the capacity of a ranging subchannel in terms of the ranging code error probability versus the number of active users to attempt ranging, referring to specifications for the Korea's 2.3 GHz band wireless broadband portable Internet service based on the IEEE 802.16-2004 standard. We consider the additive white Gaussian noise and the two-ray Rayleigh fading channels. The result shows that the capacity in the case of two-ray Rayleigh fading channel decreases about one-third, comparing with that in the case of additive white Gaussian noise channel.

This paper describes a stochastic analysis framework which computes the response time distribution and the deadline miss probability of individual tasks, even for systems with a maximum utilization greater than one. The framework is uniformly applied to fixed-priority and dynamic-priority systems and can handle, tasks with arbitrary relative deadlines and execution time distributions.

In this paper, we consider multiple quality of service (QoS) support by using modified largest weighted delay first (M-LWDF) discipline in the adaptive resource allocation of the orthogonal frequency division multiplexing (OFDM) multiple access (OFDMA) systems. We propose a M-LWDF (M Andrews et al., Feb. 2001) based on subchannel assignment, which can satisfy different QoS requirements of services including delay-sensitive and data rate-sensitive traffics in OFDMA systems. The simulation shows that the proposed M-LWDF based subchannel assignment provides controlled QoS provisions while maintaining both the stability of scheduling discipline and the flexibility of the multiple QoS support.

The exact stochastic analysis of most real-time systems is becoming unaffordable in current practice. On one side, the exact calculation of the response time distribution of the tasks is not possible except for simple periodic and independent task sets. On the other side, in practice, tasks introduce complexities like release jitter, blocking in shared resources, stochastic dependencies, etc, which can not be handled by the periodic and independent task set model. This paper introduces the concept of pessimism in the stochastic analysis of real-time systems in the following sense: the exact probability of missing any deadline is always lower than that derived from the pessimistic analysis. Therefore, if real-time constraints are expressed as probabilities of missing deadlines, the pessimistic stochastic analysis provides safe results. Some applications of the pessimism concept are presented. Firstly, the practical problems that arise in the stochastic analysis of periodic and independent task sets are addressed. Secondly, we extend to the stochastic case some well known techniques of the deterministic analysis, such as the blocking in shared resources, and the task priority assignment.

In this paper, we propose a MAC architecture for IEEE 802.11-like WLANs supporting multimedia services. The main characteristics of the proposed MAC procedure are the period restriction and DCF/SC (distributed coordination function with shortened contention-windows) instead of the PCF (point coordination function) of the IEEE 802.11 standards. We investigate the performance of the proposed MAC mechanism in terms of utilization, throughput, latency and jitter under two multimedia network environments considered by the IEEE 802.11-task group E. These environments are home and enterprise. In the simulated performance, this novel approach, which preserves the traffic classification but increases the channel utilization, thus guarantees QoS (quality of service) of multimedia traffic.

Task-level or job-level isolation is commonly used in real-time systems to prevent a job that overruns from affecting jobs belonging to other tasks (task-level isolation) or to the same task (job-level isolation). Although such an isolation provides the desired protection and simplifies analysis, it often results in degraded performance (in terms of deadline meeting) because it limits the exploitation of residual processor time resulting from jobs that underruns. We propose a new overrun handling method called randomized dropping that relaxes the isolation to make effective use of such residual processor time. We apply the proposed randomized dropping to the EDF scheduling and give an analysis technique to compute the probability of deadline meeting. Experimental results show that the proposed overrun handling method outperforms previous approaches based task-level or job-level isolation.

This paper describes a stochastic analysis method for general periodic real-time systems. The proposed method accurately computes the response time distribution of each task in the system, thus making it possible to determine the deadline miss probability of individual tasks, even for systems with maximum utilization factor greater than one. The method uniformly covers both fixed-priority scheduling (such as rate monotonic) as well as dynamic-priority scheduling (such as earliest deadline first) and can handle arbitrary relative deadlines and execution time distributions. The accuracy of the method is proven by comparing the results from the analysis with those obtained from simulations, as well as other methodologies in the literature.

Survivability is one of the important issues in broadband networks, since even a single network element failure may cause a serious amount of data loss. There have been two main approaches for connection restoration: dynamic approach and backup approach. We discuss the trade-offs between the two approaches and propose a new restoration mechanism that centers around the concept of the virtual backup network, which provides backup path sharing as well as spare bandwidth sharing. The proposed mechanism can take advantage of the two existing approaches together with special interests in resource efficiency and routing flexibility.