Memory-processor co-scheduling in fixed priority systems

A major obstacle towards the adoption of multi-core platforms for real-time systems is given by the difficulties in characterizing the interference due to memory contention. The simple fact that multiple cores may simultaneously access shared memory and communication resources introduces a significant pessimism in the timing and schedulability analysis. To counter this problem, predictable execution models have been proposed splitting task executions into two consecutive phases: a memory phase in which the required instruction and data are pre-fetched to local memory (M-phase), and an execution phase in which the task is executed with no memory contention (C-phase). Decoupling memory and execution phases not only simplifies the timing analysis, but it also allows a more efficient (and predictable) pipelining of memory and execution phases through proper co-scheduling algorithms. In this paper, we take a further step towards the design of smart co-scheduling algorithms for sporadic real-time tasks complying with the M/C (memory-computation) model. We provide a theoretical framework that aims at tightly characterizing the schedulability improvement obtainable with the adopted M/C task model on a single-core systems. We identify a tight critical instant for M/C tasks scheduled with fixed priority, providing an exact response-time analysis with pseudo-polynomial complexity. We show in our experiments that a significant schedulability improvement may be obtained with respect to classic execution models, placing an important building block towards the design of more efficient partitioned multi-core systems

An Interacting Multiple Model Approach for Target Intent Estimation at Urban Intersection for Application to Automated Driving Vehicle

Research shows that urban intersections are a hotspot for traffic accidents which cause major human injuries. Predicting turning, passing, and stop maneuvers against surrounding vehicles is considered to be fundamental for advanced driver assistance systems (ADAS), or automated driving systems in urban intersections. In order to estimate the target intent in such situations, an interacting multiple model (IMM)-based intersection-target-intent estimation algorithm is proposed. A driver model is developed to represent the driver’s maneuvering on the intersection using an IMM-based target intent classification algorithm. The performance of the intersection-target-intent estimation algorithm is examined through simulation studies. It is demonstrated that the intention of a target vehicle is successfully predicted based on observations at an individual intersection by proposed algorithms. Document type: Articl

The Fuzzy Feedback Scheduling of Real-Time Middleware in Cyber-Physical Systems for Robot Control

Cyber-physical systems for robot control integrate the computing units and physical devices, which are real-time systems with periodic events. This work focuses on CPS task scheduling in order to solve the problem of slow response and packet loss caused by the interaction between each service. The two-level fuzzy feedback scheduling scheme is designed to adjust the task priority and period according to the combined effects of the response time and packet loss. Empirical results verify the rationality of the cyber-physical system architecture for robot control and illustrate the feasibility of the fuzzy feedback scheduling method

Memory-Processor Co-Scheduling in Fixed Priority Systems

ABSTRACT A major obstacle towards the adoption of multi-core platforms for real-time systems is given by the difficulties in characterizing the interference due to memory contention. The simple fact that multiple cores may simultaneously access shared memory and communication resources introduces a significant pessimism in the timing and schedulability analysis. To counter this problem, predictable execution models have been proposed splitting task executions into two consecutive phases: a memory phase in which the required instruction and data are pre-fetched to local memory (Mphase), and an execution phase in which the task is executed with no memory contention (C-phase). Decoupling memory and execution phases not only simplifies the timing analysis, but it also allows a more efficient (and predictable) pipelining of memory and execution phases through proper co-scheduling algorithms. In this paper, we take a further step towards the design of smart co-scheduling algorithms for sporadic real-time tasks complying with the M/C (memory-computation) model. We provide a theoretical framework that aims at tightly characterizing the schedulability improvement obtainable with the adopted M/C task model on a single-core systems. We identify a tight critical instant for M/C tasks scheduled with fixed priority, providing an exact response-time analysis with pseudo-polynomial complexity. We show in our experiments that a significant schedulability improvement may be obtained with respect to classic execution models, placing an important building block towards the design of more efficient partitioned multi-core systems

Process Algebraic Approach to the Schedulability Analysis and Workload Abstraction of Hierarchical Real-Time Systems

Real-time embedded systems have increased in complexity. As microprocessors become more powerful, the software complexity of real-time embedded systems has increased steadily. The requirements for increased functionality and adaptability make the development of real-time embedded software complex and error-prone. Component-based design has been widely accepted as a compositional approach to facilitate the design of complex systems. It provides a means for decomposing a complex system into simpler subsystems and composing the subsystems in a hierarchical manner. A system composed of real-time subsystems with hierarchy is called a hierarchical real-time system This paper describes a process algebraic approach to schedulability analysis of hierarchical real-time systems. To facilitate modeling and analyzing hierarchical real-time systems, we conservatively extend an existing process algebraic theory based on ACSR-VP (Algebra of Communicating Shared Resources with Value-Passing) for the schedulability of real-time systems. We explain a method to model a resource model in ACSR-VP which may be partitioned for a subsystem. We also introduce schedulability relation to define the schedulability of hierarchical real-time systems and show that satisfaction checking of the relation is reducible to deadlock checking in ACSR-VP and can be done automatically by the tool support of ERSA (Verification, Execution and Rewrite System for ACSR). With the schedulability relation, we present algorithms for abstracting real-time system workloads

Trustworthiness in Mobile Cyber Physical Systems

Computing and communication capabilities are increasingly embedded in diverse objects and structures in the physical environment. They will link the ‘cyberworld’ of computing and communications with the physical world. These applications are called cyber physical systems (CPS). Obviously, the increased involvement of real-world entities leads to a greater demand for trustworthy systems. Hence, we use "system trustworthiness" here, which can guarantee continuous service in the presence of internal errors or external attacks. Mobile CPS (MCPS) is a prominent subcategory of CPS in which the physical component has no permanent location. Mobile Internet devices already provide ubiquitous platforms for building novel MCPS applications. The objective of this Special Issue is to contribute to research in modern/future trustworthy MCPS, including design, modeling, simulation, dependability, and so on. It is imperative to address the issues which are critical to their mobility, report significant advances in the underlying science, and discuss the challenges of development and implementation in various applications of MCPS

The Gamification of Crowdsourcing Systems: Empirical Investigations and Design

Recent developments in modern information and communication technologies have spawned two rising phenomena, gamification and crowdsourcing, which are increasingly being combined into gamified crowdsourcing systems. While a growing number of organizations employ crowdsourcing as a way to outsource tasks related to the inventing, producing, funding, or distributing of their products and services to the crowd – a large group of people reachable via the internet – crowdsourcing initiatives become enriched with design features from games to motivate the crowd to participate in these efforts. From a practical perspective, this combination seems intuitively appealing, since using gamification in crowdsourcing systems promises to increase motivations, participation and output quality, as well as to replace traditionally used financial incentives. However, people in large groups all have individual interests and motivations, which makes it complex to design gamification approaches for crowds. Further, crowdsourcing systems exist in various forms and are used for various tasks and problems, thus requiring different incentive mechanisms for different crowdsourcing types. The lack of a coherent understanding of the different facets of gamified crowdsourcing systems and the lack of knowledge about the motivational and behavioral effects of applying various types of gamification features in different crowdsourcing systems inhibit us from designing solutions that harness gamification’s full potential. Further, previous research canonically uses competitive gamification, although crowdsourcing systems often strive to produce cooperative outcomes. However, the potentially relevant field of cooperative gamification has to date barely been explored. With a specific focus on these shortcomings, this dissertation presents several studies to advance the understanding of using gamification in crowdsourcing systems