References of "Future Generation Computer Systems"
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See detailSelf-improving System Integration: Mastering Continuous Change
Bellman, Kirstie; Botev, Jean UL; Diaconescu, Ada et al

in Future Generation Computer Systems (2020)

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See detailEnhanced Lightning Network (off-chain)-based micropayment in IoT ecosystems
Robert, Jérémy UL; Kubler, Sylvain; Ghatpande, Sankalp UL

in Future Generation Computer Systems (2020)

Information is being seen as the new “oil” for companies. Trading and negotiating personal data, which includes data generated by owned smart devices, is gaining attention and acceptance in the Internet ... [more ▼]

Information is being seen as the new “oil” for companies. Trading and negotiating personal data, which includes data generated by owned smart devices, is gaining attention and acceptance in the Internet of Things (IoT) era. There is a global trend to move towards open innovation ecosystems that allow data owners to have better control over their data and privacy, choosing if/what and with whom to share/trade specific data streams. Nonetheless, this requires the design of IoT ecosystems that integrate automatic enforcing mechanisms to guarantee the delivery of the negotiated data, or still the capability of making near-instantaneous payments for the data (in the form of micro-units). This paper discusses the requirements that need to be fulfilled to properly support (micro)-payment in IoT, and further the extent to which different blockchain technologies can fulfill those requirements. Based on this analysis, our paper progresses the current state-of-the-art in three-respect: (i) by carrying out a benchmark performance analysis between LN and other-like solutions; (ii) by integrating the Lightning Network (LN) off-chain technology within an existing IoT ecosystem, developed as part of the bIoTope H2020 project, and (iii) by designing a novel algorithm for payment channel fee reduction. Experiments carried out in this paper show that LN outperforms traditional blockchain solutions under IoT-specific constraints and objectives, and that an optimal parameter setting of the proposed algorithm can be identified. [less ▲]

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See detailNorm-based deontic logic for access control, some computational results
Sun, Xin; Robaldo, Livio UL

in Future Generation Computer Systems (2017)

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See detailHopfield neural network for simultaneous job scheduling and data replication in grids
Taheri, Javid; Zomaya, Albert; Bouvry, Pascal UL et al

in Future Generation Computer Systems (2013), 29(8), 1885-1900

This paper presents a novel heuristic approach, named JDS-HNN, to simultaneously schedule jobs and replicate data files to different entities of a grid system so that the overall makespan of executing all ... [more ▼]

This paper presents a novel heuristic approach, named JDS-HNN, to simultaneously schedule jobs and replicate data files to different entities of a grid system so that the overall makespan of executing all jobs as well as the overall delivery time of all data files to their dependent jobs is concurrently minimized. JDS-HNN is inspired by a natural distribution of a variety of stones among different jars and utilizes a Hopfield Neural Network in one of its optimization stages to achieve its goals. The performance of JDS-HNN has been measured by using several benchmarks varying from medium- to very-large-sized systems. JDS-HNN’s results are compared against the performance of other algorithms to show its superiority under different working conditions. These results also provide invaluable insights into scheduling and replicating dependent jobs and data files as well as their performance related issues for various grid environments. [less ▲]

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See detailA parallel hybrid genetic algorithm for protein structure prediction on the computational grid
Tantar, Alexandru-Adrian UL; Melab, N.; Talbi, E.-G. et al

in Future Generation Computer Systems (2007), 23(3), 398-409

Solving the structure prediction problem for complex proteins is difficult and computationally expensive. In this paper, we propose a bicriterion parallel hybrid genetic algorithm (GA) in order to ... [more ▼]

Solving the structure prediction problem for complex proteins is difficult and computationally expensive. In this paper, we propose a bicriterion parallel hybrid genetic algorithm (GA) in order to efficiently deal with the problem using the computational grid. The use of a near-optimal metaheuristic, such as a GA, allows a significant reduction in the number of explored potential structures. However, the complexity of the problem remains prohibitive as far as large proteins are concerned, making the use of parallel computing on the computational grid essential for its efficient resolution. A conjugated gradient-based Hill Climbing local search is combined with the GA in order to intensify the search in the neighborhood of its provided configurations. In this paper we consider two molecular complexes: the tryptophan-cage protein (Brookhaven Protein Data Bank ID 1L2Y) and alpha-cyclodextrin. The experimentation results obtained on a computational grid show the effectiveness of the approach. (c) 2006 Elsevier B.V. All rights reserved. [less ▲]

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