Exascale computing systems (ECS) are anticipated to perform at Exaflop speed (10¹⁸ operations per second) using power consumption <20 MW. This ultrascale performance requires the speedup in the system by thousand-fold enhancement in current Petascale. For future high-performance computing (HPC), power consumption is one of the vital challenges faced to achieve Exaflops through the traditional way of increasing clock-speed. One standard way to attain such significant performance is through massive parallelism. In the early stages, it is hard to decide the promising parallel programming approach that can provide massive parallelism to attain ExaFlops. This article commences with a short description and implementation of algorithms of various hybrid parallel programming models (PPMs) for homogeneous and heterogeneous cluster systems. Furthermore, the authors evaluated performance and power consumption in these hybrid models by implementing in two HPC benchmarking applications such as square matrix multiplication and Jacobi iterative solver for two-dimensional Laplace equation. The results demonstrated that the hybrid of heterogeneous (MPI + X) outperformed to homogeneous parallel programming (MPI + OpenMP) model. This empirical investigation of hybrid PPMs is a leading step for researchers and development communities to select a promising model for emerging ECS.

The field of software-development effort estimation explores ways of defining effort through prediction approaches. Even though this field has a crucial impact on budgeting and project planning in industry, the number of works classifying and examining currently available approaches is still small. This article, therefore, presents a comprehensive overview of these approaches, and pinpoints research gaps, challenges and trends. A systematic mapping of the literature was designed and performed based on well-established practical guidelines. In total, 120 primary studies were selected, analysed and categorised, after applying a careful filtering process from a sample of 3746 candidate studies to answer six research questions. Over 70% of the selected studies adopted multiple effort estimation approaches; over 45% adopted evaluation research as a research method; over 90% of the participants were students, rather than professionals; most studies had their quality assessed as high, and were most commonly published in journals. Our study benefits practitioners and researchers by providing a body of knowledge about the current literature, serving as a starting point for upcoming studies. This article reports challenges worth investigating, regarding the use of cognitive load and team interaction.

An intent-driven system is a compositional system of human actors and machine actors. The aim of intent-driven systems is to capture stakeholders’ intents and transform these into a form that enables computer processing of the intents. Only then are different machine actors able to negotiate with each other on behalf of their respective stakeholders and their intents, and suggest a mutually beneficial collaboration. The aim is to find existing methods/techniques which could be used as building blocks to construct intent-driven systems. This is used to provide insight into what is needed to enable intent-driven systems with the help of these methods/techniques. As a part of a design science study, a Systematic Literature Review is conducted. The existences of methods/techniques which can be used as building blocks to construct intent-driven systems exist in the literature. How these methods/techniques can interact in order to enable realisations of intent-driven systems is not evident in the existing literature. The synthesis shows a need for further research regarding the semantic interchange of information, actor interaction in intent-driven systems, and the governance of intent-driven systems.

Interaction models specify the structure and content of the user interface, the allowed user actions, and the corresponding system responses. There is a need to inspect interaction models, as it avoids the propagation of defects to other artefacts. We created two inspection techniques for interaction models, called MoLVERIC Cards (MCards) and MoLVERIC Check (MCheck). MCards employs a gamification mechanism to motivate practitioners during the inspection. MCheck is a simple technique to be used by practitioners in a traditional way. Both techniques have questions, whose answers assist in identifying defects. We performed three studies to verify whether these techniques support the inspection of interaction models. In the first and second studies, we evaluated MCards and MCheck in comparison with an ad-hoc technique supported by the conventional inspection approach based on defect types. The results of these studies showed that both techniques support the inspection of interaction models. In the third study, we evaluated MCard in comparison with MCheck to understand the participants'; perceptions of both techniques. The study results showed that MCards was considered more suitable for practitioners interested in using dynamic activities, while MCheck was considered more suitable for practitioners who want to use a more traditional technique.

Software rejuvenation is an effective technique to counteract software ageing in continuously-running applications such as web-service-based systems. In a client-server application, where the server is intended to run perpetually, rejuvenation of the server process periodically during the server idle times increases the availability of that service. In these systems, web services are allocated based on the receiver's requirements and server's facilities. Since the selection of a server among candidates while maintaining the optimal quality of service is an NP-hard problem, meta-heuristics seems to be suitable. In this study, the proposed dynamic software rejuvenation as a proactive fault-tolerance technique based on a combination of ant colony optimisation (ACO) and gravitational emulation local search (GELS) so as to determine the optimal times when rejuvenation can be performed and failure rate can be minimised. The newly proposed method combined the public search capabilities of ACO with local search of GELS algorithm in an effort to create a stable algorithm, which can make reaching the global optimum largely possible in the proposed work. The simulation results revealed that the proposed strategy can decrease the failure rate of web services averagely by 28% in comparison with genetic algorithm and decision-tree strategies.