Junsung Kim

Parallel scheduling for cyber-physical systems: analysis and case study on a self-driving car

As the complexity of software for Cyber-Physical Systems (CPS) rapidly increases, multi-core processors and parallel programming models such as OpenNP become appealing to CPS developers for guaranteeing timeliness. Hence, a parallel task on multi-core processors is expected to become a vital component in CPS such as a self-driving car, where tasks must be scheduled in real-time. In this paper, we extend the fork-join parallel task model to be scheduled in real-time, where the number of parallel threads can vary depending on the physical attributes of the system. To efficiently schedule the proposed task model, we develop the task stretch* transform. Using this transform for global Deadline Monotonic scheduling for fork-join real-time tasks, we achieve a resource augmentation bound of 3.73. In other words, any task set that is feasible on m unit-speed processors can be scheduled by the proposed algorithm on m processors that are 3.73 times faster. The proposed scheme is implemented on Linux/RK as a proof of concept, and ported to Boss, the self-driving vehicle that won the 2007 DARPA Urban Challenge. We evaluate our scheme on Boss by showing its driving quality, i.e., curvature and velocity profiles of the vehicle.

Influence of bacterial extracellular polymeric substances on the formation of carbonaceous and nitrogenous disinfection byproducts.

Considering the regulatory presence of residual chlorine in water distribution systems, untreated organic matter may not be the sole contributor to disinfection byproduct (DBP) formation, given the presence of microbial biofilm with extracellular polymeric substances (EPS). This study investigated the influence of bacterial EPS on the formation of carbonaceous DBPs (C-DBPs) and nitrogenous DBPs (N-DBPs), reacting chlorine with Pseudomonas strains that produce different quantities and composition of EPS. When biomass is reacted in excess to chlorine, both C-DBPs and N-DBPs were produced without preference for speciation. However, under an excess of chlorine compared to biomass, increased EPS content led to enhanced formation of DBPs. The DBP yield of haloacetic acids (HAAs) was higher than that of trihalomethanes where dichloroacetic acid was dominant in HAA species. Additionally, chemical composition of EPS influenced the yields of DBPs. The N-DBP yield from P. putida EPS was two times higher than that of P. aeruginosa EPS, which suggested that higher organic nitrogen content in EPS contributes to higher N-DBP yield. Moreover, time-based experiments revealed that DBP formation from biomass occurs rapidly, reaching a maximum in less than four hours. Combined results suggest that bacterial EPS have significant roles in both the formation and fate of DBPs.

Nano-CF: A coordination framework for macro-programming in Wireless Sensor Networks

Wireless Sensor Networks (WSN) are being used for a number of applications involving infrastructure monitoring, building energy monitoring and industrial sensing. The difficulty of programming individual sensor nodes and the associated overhead have encouraged researchers to design macro-programming systems which can help program the network as a whole or as a combination of subnets. Most of the current macro-programming schemes do not support multiple users seamlessly deploying diverse applications on the same shared sensor network. As WSNs are becoming more common, it is important to provide such support, since it enables higher-level optimizations such as code reuse, energy savings, and traffic reduction. In this paper, we propose a macro-programming framework called Nano-CF, which, in addition to supporting in-network programming, allows multiple applications written by different programmers to be executed simultaneously on a sensor networking infrastructure. This framework enables the use of a common sensing infrastructure for a number of applications without the users being concerned about the applications already deployed on the network. The framework also supports timing constraints and resource reservations using the Nano-RK operating system. Nano-CF is efficient at improving WSN performance by (a) combining multiple user programs, (b) aggregating packets for data delivery, and (c) satisfying timing and energy specifications using Rate-Harmonized Scheduling. Using representative applications, we demonstrate that Nano-CF achieves 90% reduction in Source Lines-of-Code (SLoC) and 50% energy savings from aggregated data delivery.

Segment-Fixed Priority Scheduling for Self-Suspending Real-Time Tasks

Recent trends in System-on-a-Chip show that an increasing number of special-purpose processors are being added to improve the efficiency of common operations. Unfortunately, the use of these processors may introduce suspension delays incurred by communication, synchronization and external I/O operations. When these processors are used in real-time systems, conventional schedulability analyses incorporate these delays in the worst-case execution/response time, hence significantly reducing the schedulable utilization. In this paper, we provide schedulability analyses and propose segment-fixed priority scheduling for self-suspending tasks. We model the tasks as segments of execution separated by suspensions. We start from providing response-time analyses for self-suspending tasks under Rate Monotonic Scheduling (RMS). While RMS is shown to not be optimal, it can be used effectively in some special cases that we have identified. We then derive a utilization bound for the cases as a function of the ratio of the suspension duration to the period of the tasks. For general cases, we develop a segment-fixed priority scheduling scheme. Our scheme assigns individual segments different priorities and phase offsets that are used for phase enforcement to control the unexpected self-suspending nature. With the exact schedulability analysis designed for our scheme, our experiments show that the proposed scheme provides up to 40 times more schedulable utilization than RMS.

Energy-Aware Partitioned Fixed-Priority Scheduling for Chip Multi-processors

Energy management is becoming an increasingly important problem in application domains ranging from embedded devices to data centers. In many such systems, multi-core processors are projected as a promising technology to achieve improved performance with a lower power envelope. Managing the application power consumption under timing constraints poses significant challenges in these emerging platforms. In this paper, we study the energy-efficient scheduling of periodic real time tasks with implicit deadlines on chip multi-core processors (CMPs). We specifically consider processors with a single voltage and clock frequency domain, such as the state-of-the-art embedded multi-core NVIDIA Tegra 2processor and enterprise-class processors such as IntelsItanium 2, i5, i7 and IBMs Power 6 and Power 7series. The major contributions of this work are (i)we prove that Worst-Fit-Decreasing (WFD) task partitioning when Rate-Monotonic Scheduling (RMS) is used has an approximation ratio of 1.71 for the problem of minimizing the schedulable operating frequency with partitioned fixed-priority scheduling, (ii) we illustrate the major shortcoming of WFD with RMS resulting from not considering task periods during allocation, and(iii) we propose a Single-clock domain multi-processor Frequency Assignment Algorithm (SFAA) that determines a globally energy-efficient frequency while including task period relationships. Our evaluation results show that SFAA provides significant energy gains when compared to WFD. In fact SFAA is shown to save up to 55% more power compared to WFD for an octa-core processor.

SAFER: System-level Architecture for Failure Evasion in Real-time Applications

Recent trends towards increasing complexity in distributed embedded real-time systems pose challenges in designing and implementing a reliable system such as a self-driving car. The conventional way of improving reliability is to use redundant hardware to replicate the whole (sub)system. Although hardware replication has been widely deployed in hard real-time systems such as avionics, space shuttles and nuclear power plants, it is significantly less attractive to many applications because the amount of necessary hardware multiplies as the size of the system increases. The growing needs of flexible system design are also not consistent with hardware replication techniques. To address the needs of dependability through redundancy operating in real-time, we propose a layer called SAFER(System-level Architecture for Failure Evasion in Real-time applications) to incorporate configurable task-level fault-tolerance features to tolerate fail-stop processor and task failures for distributed embedded real-time systems. To detect such failures, SAFER monitors the health status and state information of each task and broadcasts the information. When a failure is detected using either time-based failure detection or event-based failure detection, SAFER reconfigures the system to retain the functionality of the whole system. We provide a formal analysis of the worst-case timing behaviors of SAFER features. We also describe the modeling of a system equipped with SAFER to analyze timing characteristics through a model-based design tool called SysWeaver. SAFER has been implemented on Ubuntu 10.04 LTS and deployed on Boss, an award-winning autonomous vehicle developed at Carnegie Mellon University. We show various measurements using simulation scenarios used during the 2007 DARPA Urban Challenge. Finally, we present a case study of failure recovery by SAFER when node failures are injected.

pCOMPATS: Period-Compatible Task Allocation and Splitting on Multi-core Processors

Extensive research is underway to build chips with potentially hundreds of cores. In this paper, we consider the problem of scheduling periodic real-time tasks on multi-core processors. We develop a task partitioning algorithm called Period-Compatible-Allocation and Task-Splitting (pCOMPATS) for fixed-priority scheduling of preemptive hard real-time tasks where the utilization of each of the tasks is less than 50%. pCOMPATS clusters compatible tasks together with task splitting to improve the achievable utilization. We show that as the number of cores increases, the least upper bound on schedulable utilization achieved using pCOMPATS approaches 100% per core. To the best of our knowledge, this is the first result that shows that the utilization bound improves as the number of processing cores in the system increases. We refer to tasks having utilization greater than or equal to 50% as heavy tasks and provide a task-partitioning algorithm called pCOMPATS-HT for allocating such tasks. We show that the upper bound on schedulable utilization when tasks are scheduled using pCOMPATS-HT is at most 72%. We also evaluate the performance of pCOMPATS and other well-known partitioning techniques, and show that using pCOMPATS provides much better schedulable utilization in the average case. We characterize the overhead of pCOMPATS using measurements on an Intel Core i7 processor running Linux/RK. The overheads are seen to be low on the platform, making pCOMPATS to be practical. Our results are especially useful in the context of future many-core processors with dozens to hundreds of cores per processor.

R-BATCH: Task Partitioning for Fault-tolerant Multiprocessor Real-Time Systems

Many emerging embedded real-time applications such as SCADA (Supervisory Control and Data Acquisition), autonomous vehicles and advanced avionics, require a high degree of dependability. Dealing with tasks having both hard real-time requirements and high reliability constraints is a key challenge faced in such systems. This paper addresses the problem of guaranteeing reliability requirements with bounded recovery times on fail-stop processors in fault-tolerant multiprocessor real-time systems. We classify tasks based on their recovery-time requirements into (i) Hard Recovery, (ii) Soft Recovery, and (iii) Best-Effort Recovery tasks. Then, the notion of a Hot Standby for Hard Recovery tasks along with a Cold Standby for Soft Recovery and Best-Effort Recovery tasks is introduced. In order to maximize the benefits of using a Hot Standby, replicas should not be co-located on the same processor. For this purpose, we propose a task allocation algorithm for Hot Standby replicas called R-BFD (Reliable Best-Fit Decreasing) that uses 37% fewer number of processors than BFD-P (Best-Fit Decreasing augmented with placement constraints). For tasks with more relaxed recovery-time constraints, however, additional optimization can be applied by using a Cold Standby that gets activated only when failures occur. Given a system reliability requirement and hence a maximum number of processor failures to tolerate, the required resource overprovisioning for Cold Standby replicas from multiple processors can be consolidated. An algorithm called R-BATCH (Reliable Bin-packing Algorithm for Tasks with Cold standby and Hot standby) reduces the required number of processors by up to 45% compared to R-BFD-based pure Hot Standby replication technique.

Towards dependable autonomous driving vehicles: a system-level approach

Autonomous driving technologies have been emerging over the past few years, and semi-autonomous driving functionalities have been deployed to vehicles available in the market. Since autonomous driving is realized by the intelligent processing of data from various types of sensors such as LIDAR, radar, camera, etc., the complexity of designing a dependable real-time autonomous driving system is rather high. Although there has been much research on building a reliable real-time system using hardware replication, the resulting systems tend to add significant extra cost due to hardware replication. Therefore, an alternative solution would be helpful in building an autonomous vehicle in a cost-effective way. An autonomous driving system is different from the conventional reliable real-time system because it requires (1) flexible design, (2) adaptive graceful degradation and (3) effective use of different modalities of sensors and actuators. To address these characteristics, we summarize SAFER (System-level Architecture for Failure Evasion in Real-time applications) our previous work on flexible system design. We then present a conceptual framework for autonomous vehicles to provide adaptive graceful degradation and support for using different types of sensors/actuators when a failure happens. We motivate our proposed framework with various scenarios, and we describe how SAFER can be extended to support the proposed conceptual framework.

An AUTOSAR-Compliant Automotive Platform for Meeting Reliability and Timing Constraints

High demands on advanced safety and driving functions, such as active safety and lane departure warnings, increase a vehicle’s dependency on automotive electrical/electronic architectures. Hard real-time requirements and high reliability constraints must be satisfied for the correct functioning of these safety-critical features, which can be achieved by using the AUTOSAR (Automotive Open System Architecture) standard. The AUTOSAR standard was introduced to simplify automotive system design while offering inter-operability, scalability, extensibility, and flexibility. The current version of AUTOSAR does not assist in the replication of tasks for recovering from task failures. Instead, the standard assumes that architecture designers will introduce custom extensions to meet such reliability needs. The introduction of affordable techniques with predictable properties for meeting reliability requirements will prove to be very valuable in future versions of AUTOSAR. In this paper, we propose a new Software-Component (SW-C) allocation algorithm called R-FLOW (Reliable application-FLOWaware SW-C partitioning algorithm) for fail-stop processors to support fault-tolerance with bounded recovery times, and we integrate the R-FLOW algorithm into AUTOSAR. R-FLOW leverages different types of replication schemes to satisfy reliability and timing constraints, while offering a high degree of resource utilization and flexibility. Specifically, R-FLOW classifies real-time periodic tasks into Hard Recovery tasks, Soft Recovery tasks, and Best-Effort Recovery tasks. Hot Standbys are used for recovering from failures of hard recovery tasks, whereas Cold Standbys are utilized for recovering from failures of soft recovery and best-effort recovery tasks. With this goal in mind, we design and implement our proposed architecture within the guidelines of the current AUTOSAR framework. We have built an at-scale prototyping platform, comprising of Freescale HCS12X processing boards, a dualchannel FlexRay bus, and a CAN network. Our proposed architecture is evaluated on this platform using reliability and timeliness metrics in the context of different fault scenarios.