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Gomer Rubio, & Wilton Agila. (2018). Dynamic Modeling of Fuel Cells in a Strategic Context. In 7th International Conference on Renewable Energy Research and Applications, ICRERA 2018. Paris, Francia..
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Carlos Monsalve, Alain April, & Alain Abran. (2011). BPM and requirements elicitation at multiple levels of abstraction: A review. In IADIS International Conference on Information Systems 2011 (pp. 237–242).
Abstract: Business process models can be useful for requirements elicitation, among other things. Software development depends on the quality of the requirements elicitation activities, and so adequately modeling business processes (BPs) is critical. A key factor in achieving this is the active participation of all the stakeholders in the development of a shared vision of BPs.
Unfortunately, organizations often find themselves left with inconsistent BPs that do not cover all the stakeholders’ needs
and constraints. However, consolidation of the various stakeholder requirements may be facilitated through the use of multiple levels of abstraction (MLA). This article contributes to the research into MLA use in business process modeling (BPM) for software requirements by reviewing the theoretical foundations of MLA and their use in various BP-oriented approaches.
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Juan C. Basurto, P. C. and H. C. (2011). A Proximity-Aware Transparent Handoff Mobility Scheme for VoIP Communication over Infrastructure Mesh Networks. In International Congress of Electronic, Electrical and Systems Engineering-INTERCON 2011.
Abstract: Mobility Management plays a key role in Voice-over- IP (VoIP) communications over Wireless Mesh Networks (WMN) as clients should maintain adequate levels of Quality of Service (QoS) as they move across the network. This paper presents PATH, a Proximity-Aware Transparent Handoff mobility scheme for real time voice communications over wireless mesh networks. Our study focuses on Medium Access Control (MAC) layer procedures and relies on gratuitous ARP unicasting in order to provide fast-handoffs. An experimental evaluation has been conducted and its results are shown in this paper.
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Cristina L. Abad, Yi Lu, & Roy H. Campbell. (2011). DARE: Adaptive Data Replication for Efficient Cluster Scheduling. In IEEE International Conference on Cluster Computing, 2011 (pp. 159–168).
Abstract: Placing data as close as possible to computation is a common practice of data intensive systems, commonly referred to as the data locality problem. By analyzing existing production systems, we confirm the benefit of data locality and find that data have different popularity and varying correlation of accesses. We propose DARE, a distributed adaptive data replication algorithm that aids the scheduler to achieve better data locality. DARE solves two problems, how many replicas to allocate for each file and where to place them, using probabilistic sampling and a competitive aging algorithm independently at each node. It takes advantage of existing remote data accesses in the system and incurs no extra network usage. Using two mixed workload traces from Facebook, we show that DARE improves data locality by more than 7 times with the FIFO scheduler in Hadoop and achieves more than 85% data locality for the FAIR scheduler with delay scheduling. Turnaround time and job slowdown are reduced by 19% and 25%, respectively.
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