A Schedulability Analysis Framework for Real-time Infrastructure Systems Managing Heterogeneous Resources

REACTION 2012. 1st International workshop on Real-time and distributed computing in emerging applications. December 4th, 2012, San Juan, Puerto Rico.Electricity generating systems, such as smart grid systems, and water management systems are infrastructure systems that manage resources critical to human life. In the systems, resources are produced and managed to supply them to various consumers, such as building, car, factory, and household, according to their needs and priorities. Reliable supply of resources depends not only on sufficient production of resources but also on reliable sharing of resource supply facilities. This paper presents a schedulability analysis framework. A prominent characteristic of the framework is that it considers at once the two types of resources, i.e. consumable resources, such as electricity, energy, and water, and sharable resources, such as pipelines, storages, and processors, are considered. To apply a formal approach to schedulability analysis of infrastructure system, this paper classifies the types of resources and real-time jobs for infrastructure systems. Then based on the classification , it presents an architectural model and a schedulability analysis framework.This research was supported by the KAIST High Risk High Return Project (HRHRP)

On-Device Deep Learning Inference for System-on-Chip (SoC) Architectures

As machine learning becomes ubiquitous, the need to deploy models on real-time, embedded systems will become increasingly critical. This is especially true for deep learning solutions, whose large models pose interesting challenges for target architectures at the “edge” that are resource-constrained. The realization of machine learning, and deep learning, is being driven by the availability of specialized hardware, such as system-on-chip solutions, which provide some alleviation of constraints. Equally important, however, are the operating systems that run on this hardware, and specifically the ability to leverage commercial real-time operating systems which, unlike general purpose operating systems such as Linux, can provide the low-latency, deterministic execution required for embedded, and potentially safety-critical, applications at the edge. Despite this, studies considering the integration of real-time operating systems, specialized hardware, and machine learning/deep learning algorithms remain limited. In particular, better mechanisms for real-time scheduling in the context of machine learning applications will prove to be critical as these technologies move to the edge. In order to address some of these challenges, we present a resource management framework designed to provide a dynamic on-device approach to the allocation and scheduling of limited resources in a real-time processing environment. These types of mechanisms are necessary to support the deterministic behavior required by the control components contained in the edge nodes. To validate the effectiveness of our approach, we applied rigorous schedulability analysis to a large set of randomly generated simulated task sets and then verified the most time critical applications, such as the control tasks which maintained low-latency deterministic behavior even during off-nominal conditions. The practicality of our scheduling framework was demonstrated by integrating it into a commercial real-time operating system (VxWorks) then running a typical deep learning image processing application to perform simple object detection. The results indicate that our proposed resource management framework can be leveraged to facilitate integration of machine learning algorithms with real-time operating systems and embedded platforms, including widely-used, industry-standard real-time operating systems

Platform-based Plug and Play of Automotive Safety Features - Challenges and Directions

Optional software-based features are increasingly becoming an important cost driver in automotive systems. These include features pertaining to active safety, infotainment, etc. Currently, these optional features are integrated into the vehicles at the factory during assembly. This severely restricts the flexibility of the customer to select and use features on-demand and therefore, the customer will either have to be satisfied with an available set of feature options or pre-order a car with the required features from the manufacturer resulting in considerable delay. In order to increase flexibility and reduce the delay, it is necessary to provide the option to configure the vehicle on-demand at the dealership or remotely. In this paper, we present our vision and challenges involved in developing a platform infrastructure that allows on-demand deployment of automotive safety features and ensures their correct execution

Context-aware adaptation in DySCAS

DySCAS is a dynamically self-configuring middleware for automotive control systems. The addition of autonomic, context-aware dynamic configuration to automotive control systems brings a potential for a wide range of benefits in terms of robustness, flexibility, upgrading etc. However, the automotive systems represent a particularly challenging domain for the deployment of autonomics concepts, having a combination of real-time performance constraints, severe resource limitations, safety-critical aspects and cost pressures. For these reasons current systems are statically configured. This paper describes the dynamic run-time configuration aspects of DySCAS and focuses on the extent to which context-aware adaptation has been achieved in DySCAS, and the ways in which the various design and implementation challenges are met

Temporal Isolation Among LTE/5G Network Functions by Real-time Scheduling

Radio access networks for future LTE/5G scenarios need to be designed so as to satisfy increasingly stringent requirements in terms of overall capacity, individual user performance, flexibility and power efficiency. This is triggering a major shift in the Telcom industry from statically sized, physically provisioned network appliances towards the use of virtualized network functions that can be elastically deployed within a flexible private cloud of network operators. However, a major issue in delivering strong QoS levels is the one to keep in check the temporal interferences among co-located services, as they compete in accessing shared physical resources. In this paper, this problem is tackled by proposing a solution making use of a real-time scheduler with strong temporal isolation guarantees at the OS/kernel level. This allows for the development of a mathematical model linking major parameters of the system configuration and input traffic characterization with the achieved performance and response-time probabilistic distribution. The model is verified through extensive experiments made on Linux on a synthetic benchmark tuned according to data from a real LTE packet processing scenario

n-Dimensional QoS Framework for Real-Time Service-Oriented Architectures

Service-Orientation has long provided an effective mechanism to integrate heterogeneous systems in a loosely coupled fashion as services. However, with the emergence of Internet of Things (IoT) there is a growing need to facilitate the integration of real-time services executing in non-controlled, non-real-time, environments such as the Cloud. With the need to integrate both cyberphysical systems as hardware-in-the-loop (HIL) components and also with Simulation as a Service (SIMaaS) the execution performance and response-times of the services must be managed. This paper presents a mathematical framework that captures the relationship between the host execution environment and service performance allowing the estimation of Quality of Service (QoS) under dynamic Cloud workloads. A formal mathematical definition is provided and this is evaluated against existing techniques from both the Cloud and Real-Time Service Oriented Architecture (RT-SOA) domains. The proposed approach is evaluated against the existing techniques through simulation and demonstrates a reduction of QoS violation percentage by 22% with respect to response-times as well as reducing the number of Micro-Service (uS) instances with QoS violations by 27%

Contract Aware Components, 10 years after

The notion of contract aware components has been published roughly ten years ago and is now becoming mainstream in several fields where the usage of software components is seen as critical. The goal of this paper is to survey domains such as Embedded Systems or Service Oriented Architecture where the notion of contract aware components has been influential. For each of these domains we briefly describe what has been done with this idea and we discuss the remaining challenges.Comment: In Proceedings WCSI 2010, arXiv:1010.233

A Bandwidth Reservation Mechanism for AXI-Based Hardware Accelerators on FPGAs

Hardware platforms for real-time embedded systems are evolving towards heterogeneous architectures comprising different types of processing cores and dedicated hardware accelerators, which can be implemented on silicon or dynamically deployed on FPGA fabric. Such accelerators typically access a shared memory to exchange a significant amount of data with other processing elements. Existing COTS solutions focus on maximizing the overall throughput of the system, rather than guaranteeing the timing constraints of individual hardware accelerators. This paper presents the AXI budgeting unit (ABU), a hardware-based solution to implement a bandwidth reservation mechanism on top of the AMBA AXI standard infrastructure for hardware accelerators deployed on FPGAs. An accurate and tractable model, as well as the corresponding analysis, are also proposed to bound the response time of hardware accelerators in the presence of ABUs, in order to verify whether they can complete before their deadlines. Finally, a set of experiments are reported to evaluate the proposed approach on a state-of-the-art platform, namely the Zynq-7020 by Xilinx. The resource consumption of the ABU has been quantified to be less than 1% of the total FPGA resources of the Zynq-7020

Formal Verification of Service Level Agreements Through Distributed Monitoring

In this paper, we introduce a formal model of the availability, budget compliance and sustainability of istributed services, where service sustainability is a new concept which arises as the composition of service availability and budget compliance. The model formalizes a distributed platform for monitoring the above service characteristics in terms of a parallel composition of task automata, where dynamically generated tasks model asynchronous events with deadlines. The main result of this paper is a formal model to optimize and reason about service characteristics through monitoring. In particular, we use schedulability analysis of the underlying timed automata to optimize and guarantee service sustainability

A FIFO Spin-based Resource Control Framework for Symmetric Multiprocessing

Managing shared resources in multiprocessor real-time systems can often lead to considerable schedulability sacrifice, and currently there exist no optimal multiprocessor resource sharing solutions. In addition, the choice of task mapping and priority ordering algorithms also has a direct impact on the efficiency of multiprocessor resource sharing. This thesis argues that instead of adopting a single resource sharing protocol with the traditional task mapping (e.g., the task allocation schemes that are based on utilisation only) and priority ordering (e.g., the Deadline Monotonic Priority Ordering) algorithms, the schedulability loss for managing shared resources on multiprocessors can be effectively reduced by applying a combination of appropriately chosen resource sharing protocols with new resource-oriented task allocation schemes and a new search-based priority ordering algorithm (which are independent from multiprocessor resource sharing protocols and the corresponding schedulability tests). In this thesis, a Flexible Multiprocessor Resource Sharing (FMRS) framework is proposed that aims to provide feasible resource sharing, task allocation and priority assignment solutions to fully-partitioned systems with shared resources, where each resource is controlled by a designated locking protocol. To achieve this, the candidate resource sharing protocols for this framework are firstly determined with a new schedulability test developed to support the analysis of systems with multiple locking protocols in use. Then, besides the existing algorithms, three new resource-orientated task allocation schemes and a search-based priority ordering algorithm are developed for the FMRS framework as the task mapping and priority ordering solutions. The choices of which locking protocols, task allocation and priority ordering algorithm should be adopted to a given system are determined off-line via a genetic algorithm. As demonstrated by evaluations, the FMRS framework can facilitate multiprocessor resource sharing and has a better performance than the traditional resource control and task scheduling techniques for fully-partitioned systems