Reliability is widely identified as an increasingly relevant issue in heterogeneous service-oriented systems because processor failure affects the quality of service to users. Replication-based fault-tolerance is a common approach to satisfy application's reliability requirement. This study solves the problem of minimizing redundancy to satisfy reliability requirement for a directed acyclic graph (DAG)-based parallel application on heterogeneous service-oriented systems. We first propose the enough replication for redundancy minimization (ERRM) algorithm to satisfy application's reliability requirement, and then propose heuristic replication for redundancy minimization (HRRM) to satisfy application's reliability requirement with low time complexity. Experimental results on real and randomly generated parallel applications at different scales, parallelism, and heterogeneity verify that ERRM can generate least redundancy followed by HRRM, and the state-of-the-art MaxRe and RR algorithm. In addition, HRRM implements approximate minimum redundancy with a short computation time.

Summary Modern software development for services computing and cloud computing software systems is no longer based on a single database but on existing multidatabases and this convergence needs new software architecture and framework design. Most current popular frameworks are not designed for multidatabases, and many practical problems in development arise. This study designs and implements a software development framework called Java data access service (JDAS) for multidatabases using the object‐oriented programming language Java. The JDAS framework solves related problems that arise when other frameworks are employed in practical software development with multidatabases by presenting and introducing design methods. JDAS consists of the modules of the database abstract, object relational mapping, connection pools management, configuration management, data access service, and inversion of control. Results and case study reveal that the JDAS framework effectively reduces development complexity and improves development efficiency of the software systems with multidatabases. Copyright © 2017 John Wiley & Sons, Ltd.

Industrial embedded systems are cost sensitive, and hardware cost of industrial production should be reduced for high profit. The functional safety requirement must be satisfied according to industrial functional safety standards. This study proposes three hardware cost optimization algorithms for functional safety-critical parallel applications on heterogeneous distributed embedded systems during the design phase. The explorative hardware cost optimization (EHCO), enhanced EHCO (EEHCO), and simplified EEHCO (SEEHCO) algorithms are proposed step by step. Experimental results reveal that EEHCO can obtain minimum hardware cost, whereas SEEHCO is efficient for large-scale parallel applications compared with the existing algorithms.

ISO 26262 is a functional safety standard specifically made for automotive systems, in which the automotive safety integrity level (ASIL) is the representation of the criticality level. Recently, most studies have used ASIL decomposition to reduce the development cost of automotive functions. However, these studies have not paid special attention to the problem that the reliability goal may not be satisfied when ASIL decomposition is performed. In this study, we solve the problem of minimizing the development cost of a distributed automotive function while satisfying its reliability goal during the design phase by presenting two heuristic algorithms, reliabilitycalculation of scheme (RCS) and minimizing development cost with reliability goal (MDCRG). We first use RCS to calculate the reliability value of each ASIL decomposition scheme; then, the MDCRG is used to select the scheme with the minimum development cost while satisfying the reliability goal. Real-life benchmark and simulated functions based on real parameter values are used in experiments, and results show the effectiveness of the proposed algorithms.

Dynamic voltage and frequency scaling (DVFS) is a well-known energy consumption optimization technique in embedded systems and dynamically scaling down the voltage of a chip has been developed to achieve energy-efficient optimization. However, this operation may lead to a sharp rise in transient failures of processors and consequently weaken the reliability of systems. Reliability goal is an important functional safety requirement and must be satisfied for safety-critical applications. In this study, we aim to implement energy-efficient fault-tolerant scheduling for a reliable parallel application on heterogeneous distributed embedded systems, where the parallel application is described by a directed acyclic graph (DAG). An energy-efficient scheduling with a reliability goal (ESRG) algorithm is presented to reduce the energy consumption while satisfying the reliability goal for the parallel application. Considering that the application's reliability goal is unreachable if its reliability goal exceeds a certain threshold via ESRG, we further propose an energy-efficient fault-tolerant scheduling with a reliability goal (EFSRG) algorithm to reduce the energy consumption while satisfying the reliability goal based on an active replication scheme. Experimental results confirm that the energy consumption reduced by the proposed EFSRG algorithm is higher than those reduced by other approaches under different scale conditions.

Heterogeneous processors are increasingly being used in embedded systems where parallel applications with precedence-constrained tasks widely exist. Reliability is an important functional safety requirement and reliability goal should be satisfied for safety-critical parallel applications; meanwhile, resource is limited in embedded systems and it should be minimized. This study solves the problem of resource consumption cost minimization of a reliable parallel application on heterogeneous embedded systems without using fault tolerance. The problem is decomposed into two subproblems, namely, satisfying reliability goal and minimizing resource consumption cost. The first subproblem is solved by transferring the reliability goal of the application to that of each task, and the second subproblem is solved by heuristically assigning each task to the processor with the minimum resource consumption cost while satisfying its reliability goal. Experiments with real parallel applications verify that the proposed algorithm obtains minimum resource consumption costs compared with the state-of-the-art algorithms.

A high-performance scheduling for a DAG (Directed Acyclic Graph) task graph on heterogeneous networked embedded systems or parallel and distributed systems is to maximize concurrency and minimize inter-processor communication. Most of the algorithms using upward rank value for task prioritizing and earliest finish time for processor assignment. But both approaches ignored the heterogeneity of system and could not create accurate and efficient schedules. Yet no one has doubled about and recognized that. A fully heterogeneous task scheduling algorithm is proposed to address the above problems in this paper. The fundamentals of DAG model and corresponding algorithms are investigated. New concepts called Heterogeneous Upward Rank Value (HURV) and Heterogeneous Priority Rank Value (HPRV) are defined. An algorithm called Heterogeneous Select Value (HSV) is proposed in paper. Both benchmark and extensive experimental evaluation demonstrate the significant improvements in proposed algorithm.