Many modern Cyber Physical Systems (CPSs) are composed of multiple independent periodically executing real-time control tasks having inter-dependent component sub-tasks. Each such control task is therefore usually represented asDirected-acyclic Task Graphs(DTGs). These CPSs are often distributed in nature and are quickly shifting from homogeneous to heterogeneous processing platforms in order to meet ever increasing demands for performance and energy savings, within limited resource budgets. In spite of the practical relevance of the problem in today's CPS design scenario, very few research works in literature have tried to address this due to its inherent computational as well as design complexity. This work endeavors to solve the problem of co-scheduling a set of periodic real-time applications each modelled as an independent DTG, to be executed on a distributed platform consisting of heterogeneous processors communicating using shared buses. Assuming the processing platform to be DVFS (Dynamic Voltage Frequency Scaling) enabled, we attempt to minimize dynamic energy dissipation associated with the execution of all DTGs over an hyperperiod$\mathcal {H}$while ensuring that no DTG instance within$\mathcal {H}$misses its deadline. The problem has first been formally represented as a constraint optimization problem. However, an optimal solution using standard solvers become prohibitively compute as well as memory intensive and doesn't scale even for moderate problem sizes. Hence, in this work, we attempt to develop a three-phase list-based hierarchical scheduling algorithm calledSlack Aware Frequency Level Allocator(SAFLA). The efficacy ofSAFLAhas been critically evaluated through simulation using benchmark DTGs.

Demands for high performance as well as reliability within stringent resource budgets are driving a shift from homogeneous to heterogeneous processing platforms for the implementation of today’s cyber-physical systems (CPSs). These CPSs are typically represented as directed-acyclic task graphs (DTGs) due to the complex interactions between their functional components which are often distributed in nature. This work deals with the problem of scheduling a CPS modeled as DTG. First, we present an optimal solution using integer linear programming (ILP) for the DTGs, to be executed on distributed heterogeneous processors which are interconnected via shared buses. However, this ILP-based optimal solution exhibits high computational complexity and does not scale for moderately large problem sizes. Hence, we propose a low-overhead heuristic algorithm called the contention cognizant task and message scheduler (CC-TMS), which is able to produce satisfactorily efficient as well as fast solutions within a reasonable time. The efficiency of the proposed scheme has been extensively evaluated through simulation-based experiments using benchmark DTGs. Through the case study of a real-worldautomotive traction controller, we demonstrate the practical applicability of our proposed scheme.

Cyber-Physical Systems, including those in the automotive domain, are often designed by assigning to each task an appropriate criticality-based reward value which is acquired by the system on its successful execution. Additionally, each task may have multiple implementations designated as service-levels, with higher service-levels producing more accurate results and contributing to higher rewards for the system. This work proposes strategies for co-scheduling a set of periodic tasks with multiple service-levels, on homogeneous processors and system buses. The problem is modeled as a Multi-dimensional Multiple-Choice Knapsack formulation (MMCKP) with the objective of maximizing overall system level rewards. A Dynamic Programming (DP) solution is proposed to solve the MMCKP. It was observed that although the DP based solution produces optimal results, its complexity is highly sensitive to the number of tasks, processors, buses as well as to the number of task service-levels, which severely restricts scalability of the strategy. Therefore, we have also proposed a fast yet efficient heuristic algorithm called Accurate Low Overhead Level Allocator (ALOLA), which attempts to achieve the same objective. Our simulation based experimental evaluation shows that even on moderately large systems consisting of 90 tasks with 5 service-levels each, 16 processors and 4 buses, while MMCKP incurs a run-time of more than 1 hour 20 minutes and approximately 68 GB main memory, ALOLA takes only about 196 $\mu s$ (speedup of the order of 106times) and less than 1 MB of memory. Moreover, while being fast, ALOLA is also efficient being able to control performance degradations to at most 13% compared to the optimal results produced by MMCKP. We use an automated flight control system employed in modern avionic systems, a real-world application to illustrate the general applicability of our proposed scheme.

Continuous demands for higher performance and reliability within stringent resource budgets is driving a shift from homogeneous to heterogeneous processing platforms for the implementation of today’s cyber-physical systems (CPSs). These CPSs are typically represented as Directed-acyclic Task Graph (DTG) due to the complex interactions between their functional components that are often distributed in nature. In this article, we consider the problem of scheduling a real-time application modelled as a single DTG, where tasks may have multiple implementations designated as quality-levels, with higher quality-levels producing more accurate results and contributing to higher rewards/Quality-of-Service for the system. First, we introduce an optimal solution using Integer Linear Programming (ILP) for a DTG with multiple quality-levels, to be executed on a heterogeneous distributed platform . However, this ILP-based optimal solution exhibits high computational complexity and does not scale for moderately large problem sizes. Hence, we propose two low-overhead heuristic algorithms called Global Slack Aware Quality-level Allocator ( G-SLAQA ) and Total Slack Aware Quality-level Allocator ( T-SLAQA ), which are able to produce satisfactorily efficient as well as fast solutions within a reasonable time. G-SLAQA , the baseline heuristic, is greedier and faster than its counter-part T-SLAQA , whose performance is at least as efficient as G-SLAQA . The efficiency of all the proposed schemes have been extensively evaluated through simulation-based experiments using benchmark and randomly generated DTGs. Through the case study of a real-world automotive traction controller , we generate schedules using our proposed schemes to demonstrate their practical applicability.

Real-time applications in today's distributed cyber-physical control systems are often represented as Precedence-constrained Task Graphs (PTGs) and increasingly implemented on heterogeneous platforms to cater to their high performance demands. Optimal scheduling solutions for such systems can provide advantages in terms of performance, reliability, cost etc. However, existing research works dealing with the optimal scheduling of PTGs, typically assume platforms consisting of homogeneous processing elements which interact through a fully connected network of homogeneous communication channels. In this work, we propose an Integer Linear Programming based optimal solution strategy for scheduling PTGs executing on a distributed platform composed of heterogeneous processing elements and inter-connected through a set of heterogeneous shared buses. Through the real-world case study of an automotive cruise controller, we generate an optimal schedule using our proposed scheme in order to demonstrate its generic applicability. Conducted experiments on benchmark PTGs reveal the practical efficacy of our scheme.

This study sets out to investigate the factors that influence Australian consumers’ post-adoption behaviour towards Internet banking. By integrating the research streams of technology adoption, diffusion of innovations and continuance theory of information systems, technology factors, channel factors, social factors and value-for-money factors were hypothesised to exert an impact on consumers’ post-adoption behaviour towards Internet banking. Data were gathered from 372 Internet banking users through a cross-sectional mall intercept survey. The data were analysed using Partial Least Squares path modelling. The results show that technology factors, channel factors and value-for-money factors partially influence consumers’ post-adoption behaviour towards Internet banking. Social factors were found to be non-significant. The practical implications for retail bank managers in formulating effective service delivery channel management strategies were outlined. The practical, theoretical and methodological implications of the study were discussed, and limitations and avenues for future research were presented.