Future Generation Computer Systems 79 (2018) 111–113
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Editorial
Urgent computing for decision support in critical situations Alexander V. Boukhanovsky a,b , Valeria V. Krzhizhanovskaya a,c , Marian Bubak d a
ITMO University, St. Petersburg, Russia NIAS, Wassenaar, Netherlands University of Amsterdam, Netherlands d Department of Computer Science, AGH University of Science and Technology, Krakow, Poland b c
a b s t r a c t Urgent computing for decision support in critical situations requires a cutting-edge interdisciplinary research combining data-driven modelling, highperformance computing, advanced numerical simulation and visualization. Examples of critical situations arising in complex technical, environmental and social systems include floods, earthquakes, wildfires, terrorist attacks, pandemics, financial crises, etc. Within the ‘‘urgent computing’’ concept, computational modules and data services are integrated in distributed computing environment to facilitate the decision making process, helping to find optimal scenarios under severe time constraints. Urgent computing is the core technology for the Early Warning Systems (EWS) for monitoring, anomaly detection and disaster prediction and prevention. This paper reviews the progress in this challenging field of urgent computing. © 2017 Published by Elsevier B.V.
1. Introduction Decision making in critical situations (like floods, earthquakes, wildfires, pandemics, financial crises or terrorist attacks) is a very complex problem demanding the cutting-edge ICT and computational resources. Recent advances in experimental techniques and new-generation sensors have opened new opportunities for data collection and aggregation in real time [1]. But data analytics alone cannot reduce the uncertainty associated with the multiscale evolutionary variability of real-world complex systems. For reliable decision support, we need data-driven modelling and efficient exploration of multiple simulation scenarios. The ultimate challenge of urgent computing is to run simulations faster than real-time, and to predict the outcome of various scenarios early enough to prevent critical situations or to mitigate their negative consequences. The time scales of the underlying processes vary from days in brewing the social unrest to hours in flood protection systems, to minutes in ship safety, and to seconds in earthquakes or tsunamis. Time available for decision support therefore varies in different application domains. One of the challenges of urgent computing is mapping the data acquisition techniques and computational models to computational infrastructure [2]. This problem gave birth to the Urgent Computing (UC) – a new area of computer science addressing algorithms, methods and tools enabling prioritized, immediate and effective access to large compute and storage systems (computers, grids, clouds) for emergency computations which require critical decision making. This approach provides computational services, E-mail addresses:
[email protected] (A.V. Boukhanovsky),
[email protected] (V.V. Krzhizhanovskaya),
[email protected] (M. Bubak). http://dx.doi.org/10.1016/j.future.2017.11.003 0167-739X/© 2017 Published by Elsevier B.V.
where data services work together in distributed computational environment [3] to help decision makers create an optimal behaviour scenario within a strict time limit. UC is the basic source of solutions for the Early Warning Systems (EWS) which are traditionally targeted for the disaster prediction and prevention in many areas like environmental sciences [4,5], epidemiology, telecommunications, etc. Formerly, the UC-solutions were mainly domain-specific [6]. Development of generic middleware [7] demands deeper studies in several computer science areas such as the methods and algorithms of providing immediate access to large scale compute and storage facilities, data placement, resource management and optimization for urgent computing, solutions enabling collaboration, Service Level Agreements (SLA) and policies [8]. Inspired by this research, the recent UC is the general technology to design EWS for decision support in critical situations comprising complex technical, environmental and social systems. It provides a wider consideration of UC as the main basis of the system (including middleware for UC and EWS), scalable methods, algorithms and domain-specific software for the forecast and exploration of critical situations, techniques for decision support of the user-end in case of emergency (including visualization and virtual reality), and the applications stipulated by real-world customers. A traditional platform for scientific discussion of the UC problems is the series of Urgent Computing Workshops established within the International Conference of Computational Science (ICCS) starting from 2012. It aims to develop a dialogue on the present and future of research and applications associated with the large-scale computations for decision support in critical situations. The Urgent Computing Workshops attract researchers working in three different fields:
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(1) Generic (domain-neutral) methods, algorithms and computational infrastructure for UC [7,9,10]; (2) Application of UC to decision support systems, such as the flood protection in St. Petersburg [6,11,12], the Netherlands [13,14], Slovakia [15], Poland [16,17], Italy [18,19], or USA [20]; (3) Promotion of the expansion of UC and advanced EWS to new domain areas [21,22]. Thus, the workshops created a unique platform for exchange ideas between the domain scientists and ICT specialists for intellectual cross-pollination in the decision support research. The topics associated with computational infrastructures [23] for decision support are leading the discussion during the workshops. The themes concerning leveraging e-Infrastructures for UC, deadline-driven resource management within UC cyberinfrastructure, and even the general definition of UC [24] were considered. Separately, the theoretical problems of dynamic scheduling [25] and load balancing [26] in UC environment were discussed, including the hybrid scheduling algorithms [27], evolutionary replicative data reorganization with prioritization and the quality-based approach to urgent workflows scheduling [28]. The area of UC applications in decision support was traditionally associated with the development of various EWS using the UC paradigm. A set of alternative approaches to the development UC infrastructures for flood decision support was considered for different applications (modelling dike, levees and dams [13,29,30], and flood protection barriers and gates [31,32]). The applications of UC to the city decision support systems (including the problems of critical traffic routing, citizen evacuation and infection spreading) were presented. Special attention during the workshops was paid to the discussion of new applications which may benefit from UC and EWS approaches in a future, such as the urgent information spread in mobile networks, contact dynamics in healthcare [21,33], or short-term predictions of crowd dynamics in mass gatherings [34]. These applications form the informal requirement specifications and demand for the next generation of UC middleware and the infrastructure. This special section contains selected papers from the recent Urgent Computing workshops: [19,23,33,34]. Acknowledgments AVB and VVK acknowledge support by the Russian Science Foundation, Agreement #14–21–00137 (15.08.2014). MB acknowledges support by the ISMOP project (PBS1/B9/18/2013); and by the AGH grant 11.11.230.337. References [1] K. Lee, P.A. Fishwick, Building a model for real-time simulation, Future Gener. Comput. Syst. 17 (5) (2001) 585–600. [2] P. Beckman, I. Beschastnikh, S. Nadella, N. Trebon, Building an infrastructure for urgent computing, High Perform. Comput. Grids Action (2008) 75–95. [3] A. Ferscha, J. Johnson, S.J. Turner, Distributed simulation performance data mining, Future Gener. Comput. Syst. 18 (1) (2001) 157–174. [4] V. Vescoukis, N. Doulamis, S. Karagiorgou, A service oriented architecture for decision support systems in environmental crisis management, Future Gener. Comput. Syst. 28 (3) (2012) 593–604. [5] C. Pettit, S. Williams, I. Bishop, J.-P. Aurambout, A.B.M. Russel, A. Michael, S. Sharma, D. Hunter, P.C. Chan, C. Enticott, A. Borda, D. Abramson, Building an ecoinformatics platform to support climate change adaptation in Victoria, Future Gener. Comput. Syst. 29 (2013) 624–640. Special section: Recent advances in e-Science. [6] A.V. Boukhanovsky, S.V. Ivanov, Urgent computing for operational storm surge forecasting in Saint-Petersburg, Proc. Comput. Sci. 9 (2012) 1704–1712.
[7] P. Beckman, S. Nadella, N. Trebon, I. Beschastnikh, SPRUCE: A system for supporting urgent high-performance computing, in: IFIP International Federation for Information Processing, vol. 239, 2007, pp. 295–311. [8] S.H. Leong, A. Frank, D. Kranzlmüller, Leveraging e-infrastructures for urgent computing, Proc. Comput. Sci. 18 (2013) 2177–2186. [9] D. D’Agostino, A. Clematis, A. Galizia, A. Quarati, E. Danovaro, L. Roverelli, G. Zereik, D. Kranzlmüller, M. Schiffers, N.G. Felde, C. Straube, O. Caumontz, E. Richard, L. Garrote, Q. Harphamk, H.R.A. Jagers, V. Dimitrijević, L. Dekić, E. Fiori, F. Delogu, A. Parodi, The DRIHM project: A flexible approach to integrate HPC, grid and cloud resources for hydro-meteorological research, in: International Conference for High Performance Computing, Networking, Storage and Analysis, SC, 2014, vol. 2015–Janua, pp. 536–546. [10] B. Balis, M. Kasztelnik, M. Malawski, P. Nowakowski, B. Wilk, M. Pawlik, M. 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Ozhigin, B. Lang, R.J. Meijer, Flood early warning system: Design, plementation and computational modules, Proc. Comput. Sci. 4 (2011). [15] V. Šipková, L. Hluchý, M. Dobrucký, J. Bartok, B. Nguyen, Manufacturing of weather forecasting simulations on high performance infrastructures, in: ECW 2016 Environmental Computing Workshop, 2016, pp. 432–439. [16] R. Brzoza-Woch, M. Konieczny, B. Kwolek, P. Nawrocki, T. Szydło, K. Zieliński, Holistic approach to urgent computing for flood decision support, Proc. Comput. Sci. 51 (1) (2015) 2387–2396. [17] B. Balis, T. Bartynski, M. Bubak, D. Harezlak, M. Kasztelnik, M. Malawski, P. Nowakowski, M. Pawlik, B. Wilk, Smart levee monitoring and flood decision support system: Reference architecture and urgent computing management, Proc. Comput. Sci. 108 (2017) 2220–2229. [18] A. Parodi, D. Kranzlmueller, A. Clematis, E. Danovaro, A. Galizia, L. Garrote, M.C. Llasat, O. Caumont, E. Richard, Q. Harpham, F. Siccardi, L. Ferraris, N. Rebora, F. Delogu, E. Fiori, L. Molini, E. Foufoula-Georgiou, D. D’Agostino, DRIHM(2US): an e-Science environment for hydrometeorological research on high impact weather events, Bull. Am. Meteorol. Soc. (2007). [19] S.H. Leong, A. Parodi, D. Kranzlmüller, A robust reliable energy-aware urgent computing resource allocation for flash-flood ensemble forecasting on hpc infrastructures for decision support, Future Gener. Comput. Syst. 68 (2017) 136–149. [20] B. Blanton, J. McGee, J. Fleming, C. Kaiser, H. Kaiser, H. Lander, R. Luettich, K. Dresback, R. Kolar, Urgent computing of storm surge for North Carolina’s coast, Proc. Comput. Sci. 9 (2012) 1677–1686. [21] S. Manos, S. Zasada, P.V. Coveney, Life or death decision-making: The medical case for large-scale, On-Demand Grid Computing, CTWatch Q. 4 (1) (2008). [22] S. Alowayyed, D. Groen, P.V. Coveney, A.G. Hoekstra, Multiscale computing in the exascale era, J. Comput. Sci. (2016) submitted for publication. [23] B. Balis, R. Brzoza-Woch, M. Bubak, M. Kasztelnik, B. Kwolek, P. Nawrocki, P. Nowakowski, T. Szydlo, K. Zielinski, Holistic approach to management of IT infrastructure for environmental monitoring and decision support systems with urgent computing capabilities, Future Gener. Comput. Syst. 79 (2018) 128–143. [24] S.H. Leong, D. Kranzlmüller, Towards a general definition of urgent computing, Proc. Comput. Sci. 51 (1) (2015) 2337–2346. [25] J.J. Durillo, V. Nae, R. Prodan, Multi-objective energy-efficient workflow scheduling using list-based heuristics, Future Gener. Comput. Syst. 36 (2014) 221–236. [26] V.V. Korkhov, J.T. Moscicki, V.V. Krzhizhanovskaya, Dynamic workload balancing of parallel applications with user-level scheduling on the Grid, Future Gener. Comput. Syst. 25 (1) (2009) 28–34. [27] J.M. Cope, N. Trebon, H.M. Tufo, P. Beckman, Robust data placement in urgent computing environments, in: IEEE International Symposium on Parallel & Distributed Processing, IPDPS 2009, pp. 1–13. [28] M. Ficco, B. Di Martino, R. Pietrantuono, S. Russo, Optimized task allocation on private cloud for hybrid simulation of large-scale critical systems, Future. Gener. Comput. Syst. (2016).
Editorial / Future Generation Computer Systems 79 (2018) 111–113 [29] N.B. Melnikova, V.V. Krzhizhanovskaya, P.M. a Sloot, Modeling earthen dikes using real-time sensor data, J. Hydrol. 496 (2013) 154–165. [30] W.D. Fisher, T.K. Camp, V.V. Krzhizhanovskaya, Anomaly detection in earth dam and levee passive seismic data using support vector machines and automatic feature selection, J. Comput. Sci. (2016). [31] C.D. Erdbrink, V.V. Krzhizhanovskaya, P.M.A. Sloot, Free-surface flow simulations for discharge-based operation of hydraulic structure gates, J. Hydroinformatics 16 (1) (2014) 189–206.
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[32] C.D. Erdbrink, V.V. Krzhizhanovskaya, P.M.A. Sloot, Reducing cross-flow vibrations of underflow gates: experiments and numerical studies, J. Fluids Struct. 50 (2014). [33] S.V. Kovalchuk, E. Krotov, P.A. Smirnov, D.A. Nasonov, A.N. Yakovlev, Distributed data-driven platform for urgent decision making in cardiological ambulance control, Futur. Gener. Comput. Syst. 79 (2018) 144–154. [34] V. Karbovskii, D. Voloshin, A. Karsakov, A. Bezgodov, C. Gershenson, Multimodel agent-based simulation environment for mass-gatherings and pedestrian dynamics, Future Gener. Comput. Syst. 79 (2018) 155–165.
