286,711 research outputs found

    A flexible service selection for executing virtual services

    Full text link
    [EN] With the adoption of a service-oriented paradigm on the Web, many software services are likely to fulfil similar functional needs for end-users. We propose to aggregate functionally equivalent software services within one single virtual service, that is, to associate a functionality, a graphical user interface (GUI), and a set of selection rules. When an end user invokes such a virtual service through its GUI to answer his/her functional need, the software service that best responds to the end-user s selection policy is selected and executed and the result is then rendered to the end-user through the GUI of the virtual service. A key innovation in this paper is the flexibility of our proposed service selection policy. First, each selection policy can refer to heterogeneous parameters (e.g., service price, end-user location, and QoS). Second, additional parameters can be added to an existing or new policy with little investment. Third, the end users themselves define a selection policy to apply during the selection process, thanks to the GUI element added as part of the virtual service design. This approach was validated though the design, implementation, and testing of an end-to-end architecture, including the implementation of several virtual services and utilizing several software services available today on the Web.This work was partially supported in part by SERVERY (Service Platform for Innovative Communication Environment), a CELTIC project that aims to create a Service Marketplace that bridges the Internet and Telco worlds by merging the flexibility and openness of the former with the trustworthiness and reliability of the latter, enabling effective and profitable cooperation among actors.Laga, N.; Bertin, E.; Crespi, N.; Bedini, I.; Molina Moreno, B.; Zhao, Z. (2013). A flexible service selection for executing virtual services. World Wide Web. 16(3):219-245. doi:10.1007/s11280-012-0184-2S219245163Aggarwal, R., Verma, K., Miller, J., and Milnor, W.: Constraint Driven Web Service Composition in METEOR-S. In Proceedings of the 2004 IEEE international Conference on Services Computing (September 2004). IEEE Computer Society, Washington, DC, 23–30.Apple Inc. Apple app store.: Available at: www.apple.com/iphone/appstore/ , accessed on May 22nd, 2012.Atzeni, P., Catarci, T., Pernici, B.: Multi-Channel adaptive information Systems. World Wide Web 10(4), 345–347 (2007)Baresi, L., Bianchini, D., Antonellis, V.D., Fugini, M.G., Pernici, B., Plebani, P.: Context-aware Composition of e-Service. In Technologies for E-Services: Third International Workshop, vol. 2819, 28–41, TES 2003, Berlin, German, 2003.Ben Hassine, A., Matsubara, S., Ishida, T.: In Proceedings of the 5th international conference on The Semantic Web (ISWC’06), Isabel Cruz, Stefan Decker, Dean Allemang, Chris Preist, and Daniel Schwabe (Eds.). Springer-Verlag, Berlin, Heidelberg, 130–143 (2006).Blum, N., Dutkowski, S., Magedanz, T.: InSeRt - An Intent-based Service Request API for Service Exposure in Next Generation Networks. In Proceedings of 32nd Annual IEEE Software Engineering Workshop. Porto Sani Resort, Kassandra, Greece, 2008 pp21–30.Boussard, M., Fodor, S., Crespi, N., Iribarren, V., Le Rouzic, J.P., Bedini, I., Marton, G., Moro Fernandez, D., Lorenzo Duenas, O., Molina, B.: SERVERY: the Web-Telco marketplace. ICT-Mobile Summit 2009, Santander (2009)Cabrera, Ó., Oriol, M., Franch, X., Marco, J., López, L., Fragoso, O., Santaolaya, R.: WeSSQoS: A Configurable SOA System for Quality-aware Web Service Selection. CoRR 2011, abs/1110.5574.Casati, F., Ilnicki, S., Jin, L., Krishnamoorthy, V., Shan, M.: Adaptive and Dynamic Service Composition in eFlow. Lecture Notes in Computer Science, Volume 1789/2000, 13–31, 2000.Cibrán, M. A., Verheecke, B., Vanderperren, W., Suvée, D., and Jonckers, V.: “Aspect-oriented Programming for Dynamic Web Service Selection, Integration and Management.” In Proc. World Wide Web 2007, pp. 211–242.Crespi, N., Boussard, M. Fodor, S.: Converging Web 2.0 with telecommunications. eStrategies Projects, Vol. 10, 108–109. British Publishers, ISSN 1758–2369, June 2009.Dey, A.K., Salber, D., Abowd, G.D.: A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Hum. Comput. Interact. 16, 1–67 (2001)Ding, Q., Li, X., and Zhou, X.: Reputation Based Service Selection in Grid Environment. In Proceedings of the 2008 international Conference on Computer Science and Software Engineering - Volume 03 (December. 2008). CSSE. IEEE Computer Society, Washington, DC, 58–61.Fielding, R.T.: Architectural Styles and the Design of Network-based Software Architectures. Thesis dissertation, 2000.Franch, X., Grünbacher, P., Oriol, M., Burgstaller, B., Dhungana, D., López, L., Marco, J., Pimentel, J.: Goal-driven Adaptation of Service-Based Systems from Runtime Monitoring Data. REFS 2011.Frolund, S., Koisten, J.: QML: A Language for Quality of Service Specification. HP Labs technical reports. Available at http://www.hpl.hp.com/techreports/98/HPL-98-10.html , accessed on May 22nd, 2012.Google. Android market.: Available at: www.android.com/market/ , accessed on May 22nd, 2012.Google. Intents and Intent Filters.: Available at http://developer.android.com/guide/topics/intents/intents-filters.html , accessed on May 22nd, 2012.Gu, X., Nahrstedt, K., Yuan, W., Wichadakul, D., Xu, D.: An Xml-Based Quality of Service Enabling Language for the Web. Technical Report. UMI Order Number: UIUCDCS-R-2001-2212., University of Illinois at Urbana-Champaign.Laga, N., Bertin, E., and Crespi, N.: Building a User Friendly Service Dashboard: Automatic and Non-intrusive Chaining between Widgets. In Proceedings of the 2009 Congress on Services - I (July 06–10, 2009). SERVICES. IEEE Computer Society, Washington, DC, 484–491.Laga, N., Bertin, E., and Crespi, N.: Business Process Personalization Through Web Widgets. In Proceedings of the 2010 IEEE international Conference on Web Services (July 05–10, 2010). ICWS. IEEE Computer Society, Washington, DC, 551–558.Liu, Y., Ngu, A. H., and Zeng, L. Z.: QoS computation and policing in dynamic web service selection. In Proceedings of the 13th international World Wide Web Conference on Alternate Track Papers &Amp; Posters (New York, NY, USA, May 19–21, 2004). WWW Alt. ’04. ACM, New York, NY, 66–73.Malik, Z., Bouguettaya, A.: Rater credibility assessment in Web services interactions. World Wide Web 12(1), 3–25 (2009)Martin, D. et al.: OWL-S: Semantic Markup for Web Services. W3C member submission, available at http://www.w3.org/Submission/2004/SUBM-OWL-S-20041122/ , accessed on May 22nd, 2012.Nestler, T., Namoun, A., Schill, A.: End-user development of service-based interactive web applications at the presentation layer. EICS 2011: 197–206.Newcomer, E.: Understanding Web Services: XML, Wsdl, Soap, and UDDI. Addison, Wesley, Boston, Mass., May 2002.O’Reilly, T.: What Is Web 2.0, Design Patterns and Business Models for the Next Generation of Software.Piessens, F., Jacobs, B., Truyen, E., Joosen, W.: Support for Metadata-driven Selection of Run-time Services in .NET is Promising but Immature. vol. 3, no. 2, Special issue: .NET: The Programmer’s Perspective: ECOOP Workshop, 27–35. 2003.Rasch, K;, Li, F., Sehic, S., Ayani R., and Dustdar, S.: “Context-driven personalized service discovery in pervasive environments,” in Proc World Wide Web, 2011, pp. 295–319.Reichl, P.: From ‘Quality-of-Service’ and ‘Quality-of-Design’ to ‘Quality-of-Experience’: A holistic view on future interactive telecommunication ser-vices. In 15th International Conference on Software, Telecommunications and Computer Networks, 2007. Soft-COM 2007. Sept. 2007. vol., no.,1–6, 27–29.Rolland, C., Kaabi, R.S., Kraiem, N.: On ISOA: Intentional Services Oriented Architecture. In Advanced Information Systems Engineering, volume 4495/2007, 158–172, June 2007.Sanchez, A., Carro, B., Wesner, S.: Telco services for end customers: European Perspective. In Communications Magazine. IEEE 46(2), 14–18 (2008)Santhanam, G. R., Basu, S., and Honavar, V.: On Utilizing Qualitative Preferences in Web Service Composition: A CP-net Based Approach. In Proceedings of IEEE Congress on Services, Services - Part I, vol., no.,538–544, 2008.Spanoudakis, G., Mahbub, K., Zisman, A.: A Platform for Context Aware Runtime Web Service Discovery. In Proc IEEE ICWS, 2007, pp233-240.Tsesmetzis, D., Roussaki, I., Sykas, E.: Modeling and Simulation of QoS-aware Web Service Selection for Provider Profit Maximization. Simulation 83(1), 93–106 (2007)Wang, P., Chao, K., Lo, C., Farmer, R., and Kuo, P.: A Reputation-Based Service Selection Scheme. In Proceedings of the 2009 IEEE international Conference on E-Business Engineering (October 21–23, 2009). ICEBE. IEEE Computer Society, Washington, DC, 501–506.Wang, H., Yang, D., Zhao, Y., and Gao, Y.: Multiagent System for Reputation--based Web Services Selection. In Proceedings of the Sixth international Conference on Quality Software (October 27–28, 2006). QSIC. IEEE Computer Society, Washington, DC, 429–434.Wholesale Applications Community.: WAC Informational Whitepaper. Available at http://www.wholesaleappcommunity.com/About-Wac/BACKGROUND%20TO%20WAC/whitepaper.pdf , accessed on May 22nd, 2012.Windows Marketplace.: Available at http://marketplace.windowsphone.com/default.aspx , accessed on May 22nd, 2012.Xu, Z., Martin, P., Powley, W., Zulkernine, F.: Reputation-Enhanced QoS-based Web Services Discovery. Web Services, 2007. In proceedings of IEEE International Conference on Web Services, ICWS 2007. 249, 256, 9–13 July 2007.Yu, Q., Bouguettaya,A.: “Multi-attribute optimization in service selection”. In Proc World Wide Web,2012, pp. 1–31.Yu, T., Zhang, Y., Lin, K. Efficient algorithms for Web services selection with end-to-end QoS constraints. ACM Transaction Web 1, 1. Article 6, 26 pages. (May 2007),

    Combined Scheduling of Time-Triggered Plans and Priority Scheduled Task Sets

    Full text link
    © Owner/Author (2016). This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM SIGAda Ada Letters, 36(1), 68-76, http://dx.doi.org/10.1145/10.1145/2971571.2971580.[EN] Preemptive, priority-based scheduling on the one hand, and time-triggered scheduling on the other, are the two major techniques in use for development of real-time and embedded software. Both have their advantages and drawbacks with respect to the other, and are commonly adopted in mutual exclusion. In a previous paper, we proposed a software architecture that enables the combined and controlled execution of time-triggered plans and priority-scheduled tasks. The goal was to take advantage of the best of both approaches by providing deterministic, jitter-controlled execution of time-triggered tasks (e.g., control tasks), coexisting with a set of priority-scheduled tasks, with less demanding jitter requirements. In this paper, we briefly describe the approach, in which the time-triggered plan is executed at the highest priority level, controlled by scheduling decisions taken only at particular points in time, signalled by recurrent timing events. The rest of priority levels are used by a set of concurrent tasks scheduled by static or dynamic priorities. We also discuss several open issues such as schedulability analysis, use of the approach in multiprocessor architectures, usability in mixed-criticality systems and needed changes to make this approach Ravenscar compliant.This work has been partly supported by the Spanish Government’s project M2C2 (TIN2014-56158-C4-1-P-AR) and the European Commission’s project EMC2 (ARTEMIS-JU Call 2013 AIPP-5, Contract 621429).Real Sáez, JV.; Sáez Barona, S.; Crespo Lorente, A. (2016). Combined Scheduling of Time-Triggered Plans and Priority Scheduled Task Sets. Ada Letters. 36(1):68-76. https://doi.org/10.1145/2971571.2971580S6876361T. P. Baker and A. Shaw. The cyclic executive model and Ada. In Proceedings IEEE Real Time Systems Symposium 1988, Huntsville, Alabama, pages 120--129, 1988.P. Balbastre, I. Ripoll, J. Vidal, and A. Crespo. A Task Model to Reduce Control Delays. Real-Time Systems, 27(3):215--236, September 2004.A. Burns and R. Davis. Mixed Criticality Systems - A Review. Technical report, Depatment of Computer Science, University of York, 2013.A. Cervin. Integrated Control and Real-Time Scheduling. PhD thesis, Lund Institute of Technology, April 2003.R. Dobrin. Combining Offline Schedule Construction and Fixed Priority Scheduling in Real-Time Computer Systems. PhD thesis, Mälardalen University, 2005.S. Hong, X. Hu, and M. Lemmon. Reducing Delay Jitter of Real-Time Control Tasks through Adaptive Deadline Adjustments. In IEEE Computer Society, editor, 22nd Euromicro Conference on Real-Time Systems -- ECRTS, pages 229--238, 2010.J. W. S. Liu. Real-Time Systems. Prentice-Hall Inc., 2000.J. Palencia and M. González-Harbour. Schedulability Analysis for Tasks with Static and Dynamic Offsets. In 9th IEEE Real-Time Systems Symposium, 1998.M. J. Pont. The Engineering of Reliable Embedded Systems: LPC1769 edition. Number ISBN: 978-0-9930355-0-0. SafeTTy Systems Limited, 2014.J. Real and A. Crespo. Incorporating Operating Modes to an Ada Real-Time Framework. Ada Letters, 30(1):73--85, April 2010.J. Real, S. Sáez, and A. Crespo. Combining time-triggered plans with priority scheduled task sets. In M. Bertogna and L. M. Pinho, editors, Reliable Software Technologies -- Ada-Europe 2016, volume 9695 of Lecture Notes in Computer Science. Springer, June 2016.S. Sáez, J. Real, and A. Crespo. An integrated framework for multiprocessor, multimoded real-time applications. In M. Brorsson and L. Pinho, editors, Reliable Software Technologies -- Ada-Europe 2012, volume 7308, pages 18--34. Springer-Verlag, June 2012.S. Sáez, J. Real, and A. Crespo. Implementation of Timing-Event Anities in Ada/Linux. Ada Letters, 35(1), April 2015.A. J. Wellings and A. Burns. A Framework for Real-Time Utilities for Ada 2005. Ada Letters, XXVII(2), August 2007

    Real-time agreement and fulfilment of SLAs in Cloud Computing environments

    Full text link
    A Cloud Computing system must readjust its resources by taking into account the demand for its services. This raises the need for designing protocols that provide the individual components of the Cloud architecture with the ability to self-adapt and to reach agreements in order to deal with changes in the services demand. Furthermore, if the Cloud provider has signed a Service Level Agreement (SLA) with the clients of the services that it offers, the appropriate agreement mechanism has to ensure the provision of the service contracted within a specified time. This paper introduces real-time mechanisms for the agreement and fulfilment of SLAs in Cloud Computing environments. On the one hand, it presents a negotiation protocol inspired by the standard WSAgreement used in web services to manage the interactions between the client and the Cloud provider to agree the terms of the SLA of a service. On the other hand, it proposes the application of a real-time argumentation framework for redistributing resources and ensuring the fulfilment of these SLAs during peaks in the service demand.This work is supported by the Spanish government Grants CONSOLIDER-INGENIO 2010 CSD2007-00022, TIN2011-27652-C03-01, TIN2012-36586-C03-01 and TIN2012-36586-C03-03.De La Prieta, F.; Heras Barberá, SM.; Palanca Cámara, J.; Rodríguez, S.; Bajo, J.; Julian Inglada, VJ. (2014). Real-time agreement and fulfilment of SLAs in Cloud Computing environments. AI Communications. 1-24. doi:10.3233/AIC-140626S124[1]V. Aleven and K.D. Ashley, Teaching case-based argumentation through a model and examples, empirical evaluation of an intelligent learning environment, in: Artificial Intelligence in Education, AIED-97, Frontiers in Artificial Intelligence and Applications, Vol. 39, IOS Press, 1997, pp. 87–94.[2]M. Alhamad, W. Perth, T. Dillon and E. Chang, Conceptual SLA framework for cloud computing, in: 4th IEEE International Conference on Digital Ecosystems and Technologies (DEST), IEEE Press, 2010, pp. 606–610.Armbrust, M., Stoica, I., Zaharia, M., Fox, A., Griffith, R., Joseph, A. D., … Rabkin, A. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50. doi:10.1145/1721654.1721672Ashley, K. D. (1991). Reasoning with cases and hypotheticals in HYPO. International Journal of Man-Machine Studies, 34(6), 753-796. doi:10.1016/0020-7373(91)90011-u[6]P. Barham, B. Dragovic, K. Fraser, S. Hand, T. Harris, A. Ho, R. Neugebauer, I. Pratt and A. Warfield, Xen and the art of virtualization, in: 9th ACM Symposium on Operating Systems Principles (SOSP-03), ACM Press, 2003, pp. 164–177.Beloglazov, A., Abawajy, J., & Buyya, R. (2012). Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing. Future Generation Computer Systems, 28(5), 755-768. doi:10.1016/j.future.2011.04.017[8]A. Beloglazov and R. Buyya, Energy efficient allocation of virtual machines in cloud data centers, in: 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, IEEE Computer Society, 2010, pp. 577–578.[9]A. Beloglazov and R. Buyya, Energy efficient resource management in virtualized cloud data centers, in: 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, IEEE Computer Society, 2010, pp. 826–831.Bench-Capon, T., & Sartor, G. (2003). A model of legal reasoning with cases incorporating theories and values. Artificial Intelligence, 150(1-2), 97-143. doi:10.1016/s0004-3702(03)00108-5[11]T.J. Bench-Capon, Specification and implementation of Toulmin dialogue game, in: International Conferences on Legal Knowledge and Information Systems, JURIX-98, Frontiers of Artificial Intelligence and Applications, IOS Press, 1998, pp. 5–20.[12]R. Buyya, R. Ranjan and R.N. Calheiros, Intercloud: Utility-oriented federation of cloud computing environments for scaling of application services, in: 10th International Conference on Algorithms and Architectures for Parallel Processing – Volume Part I, ICA3PP’10, Springer-Verlag, 2010, pp. 13–31.[13]R. Buyya, C.S. Yeo and S. Venugopal, Market-oriented cloud computing: Vision, hype, and reality for delivering it services as computing utilities, in: High Performance Computing and Communications, 2008. HPCC’08. 10th IEEE International Conference, September 2008, IEEE, 2008, pp. 5–13.Chen, C., Li, S. S., Chen, B., & Wen, D. (2011). Agent Recommendation for Agent-Based Urban-Transportation Systems. IEEE Intelligent Systems, 26(6), 77-81. doi:10.1109/mis.2011.94[15]Y.Y. Cheng, M. Low, S. Zhou, W. Cai and C.S. Choo, Evolving agent-based simulations in the clouds, in: 3rd International Workshop on Advanced Computational Intelligence (IWACI), 2010, pp. 244–249.[16]F. Dignum and H. Weigand, Communication and Deontic Logic, in: Information Systems – Correctness and Reusability. Selected Papers from the IS-CORE Workshop, R. Wieringa and R. Feenstra, eds, World Scientific Publishing Co., 1995, pp. 242–260.Erdogmus, H. (2009). Cloud Computing: Does Nirvana Hide behind the Nebula? IEEE Software, 26(2), 4-6. doi:10.1109/ms.2009.31[19]J.O. Fitó, I. Goiri and J. Guitart, SLA-driven elastic cloud hosting provider, in: 18th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), IEEE Computer Society, 2010, pp. 111–118.Fuentes-Fernández, R., Hassan, S., Pavón, J., Galán, J. M., & López-Paredes, A. (2012). Metamodels for role-driven agent-based modelling. Computational and Mathematical Organization Theory, 18(1), 91-112. doi:10.1007/s10588-012-9110-5Heras, S., Botti, V., & Julián, V. (2009). Challenges for a CBR framework for argumentation in open MAS. The Knowledge Engineering Review, 24(4), 327-352. doi:10.1017/s0269888909990178Heras, S., Jordán, J., Botti, V., & Julián, V. (2013). Argue to agree: A case-based argumentation approach. International Journal of Approximate Reasoning, 54(1), 82-108. doi:10.1016/j.ijar.2012.06.005[24]M. Jensen, J. Schwenk, N. Gruschka and L. Iacono, On technical security issues in cloud computing, in: IEEE International Conference on Cloud Computing, IEEE Press, 2009, pp. 109–116.Kakas, A., Maudet, N., & Moraitis, P. (2005). Modular Representation of Agent Interaction Rules through Argumentation. Autonomous Agents and Multi-Agent Systems, 11(2), 189-206. doi:10.1007/s10458-005-2176-4[26]M.J. Kim, H.G. Yoon and H.K. Lee, MAV: An intelligent Multi-agent model based on Cloud computing for resource virtualization, in: Computers, Networks, Systems, and Industrial Engineering, Studies in Computational Intelligence, Vol. 365, Springer, 2011, pp. 99–111.Kraus, S., Sycara, K., & Evenchik, A. (1998). Reaching agreements through argumentation: a logical model and implementation. Artificial Intelligence, 104(1-2), 1-69. doi:10.1016/s0004-3702(98)00078-2[28]W.-Y. Lin, G.-Y. Lin and H.-Y. Wei, Dynamic auction mechanism for cloud resource allocation, in: 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, CCGRID’10, IEEE Computer Society, Washington, DC, USA, 2010, pp. 591–592.[29]S. Liu, G. Quan and S. Ren, On-line scheduling of real-time services for cloud computing, in: 6th World Congress on Services, SERVICES’10, IEEE Computer Society, 2010, pp. 459–464.Navarro, M., Heras, S., Botti, V., & Julián, V. (2013). Towards real-time agreements. Expert Systems with Applications, 40(10), 3906-3917. doi:10.1016/j.eswa.2012.12.087Ontañón, S., & Plaza, E. (2011). An argumentation framework for learning, information exchange, and joint-deliberation in multi-agent systems1. Multiagent and Grid Systems, 7(2-3), 95-108. doi:10.3233/mgs-2011-0169Palanca, J., Navarro, M., García-Fornes, A., & Julian, V. (2013). Deadline prediction scheduling based on benefits. Future Generation Computer Systems, 29(1), 61-73. doi:10.1016/j.future.2012.05.007[33]C. Pautasso, O. Zimmermann and F. Leymann, Restful web services vs. “big”’ web services: making the right architectural decision, in: Proceedings of the 17th International Conference on World Wide Web, WWW’08, ACM, New York, NY, USA, 2008, pp. 805–814.[34]J. Peng, X. Zhang, Z. Lei, B. Zhang, W. Zhang and Q. Li, Comparison of several cloud computing platforms, in: 2nd International Symposium on Information Science and Engineering, ISISE’09, IEEE Computer Society, 2009, pp. 23–27.Prakken, H., & Sartor, G. (1998). Artificial Intelligence and Law, 6(2/4), 231-287. doi:10.1023/a:1008278309945[36]I. Rahwan and G. Simari, eds, Argumentation in Artificial Intelligence, Springer, 2009.Ross, J. W., & Westerman, G. (2004). Preparing for utility computing: The role of IT architecture and relationship management. IBM Systems Journal, 43(1), 5-19. doi:10.1147/sj.431.0005Schaffer, H. E. (2009). X as a Service, Cloud Computing, and the Need for Good Judgment. IT Professional, 11(5), 4-5. doi:10.1109/mitp.2009.112[39]K.M. Sim, Agent-based cloud commerce, in: IEEE International Conference on Industrial Engineering and Engineering Management, IEEE Press, 2009, pp. 717–721.Soh, L.-K., & Tsatsoulis, C. (2005). A Real-Time Negotiation Model and A Multi-Agent Sensor Network Implementation. Autonomous Agents and Multi-Agent Systems, 11(3), 215-271. doi:10.1007/s10458-005-0539-5Talia, D. (2012). Clouds Meet Agents: Toward Intelligent Cloud Services. IEEE Internet Computing, 16(2), 78-81. doi:10.1109/mic.2012.28Tolchinsky, P., Modgil, S., Atkinson, K., McBurney, P., & Cortés, U. (2011). Deliberation dialogues for reasoning about safety critical actions. Autonomous Agents and Multi-Agent Systems, 25(2), 209-259. doi:10.1007/s10458-011-9174-5[44]A. Toniolo, T. Norman and K. Sycara, An empirical study of argumentation schemes in deliberative dialogue, in: 20th European Conference on Artificial Intelligence, ECAI-12, Frontiers in Artificial Intelligence and Applications, Vol. 242, IOS Press, 2012, pp. 756–761.[45]W.-T. Tsai, Q. Shao, X. Sun and J. Elston, Real-time service-oriented cloud computing, in: IEEE 6th World Congress on Services, SERVICES’10, IEEE Press, 2010, pp. 473–478.[46]D. Walton, C. Reed and F. Macagno, Argumentation Schemes, Cambridge University Press, 2008.[47]L. Wang, J. Tao, M. Kunze, A. Castellanos, D. Kramer and W. Karl, Scientific cloud computing: Early definition and experience, in: 10th IEEE International Conference on High Performance Computing and Communications (HPCC-08), IEEE Press, 2008, pp. 825–830.[48]Y.O. Yazir, C. Matthews, R. Farahbod, S. Neville, A. Guitouni, S. Ganti and Y. Coady, Dynamic resource allocation in computing clouds using distributed multiple criteria decision analysis, in: IEEE 3rd International Conference on Cloud Computing (CLOUD), IEEE Computer Society, 2010, pp. 91–98.[49]Y. Yu, S. Ren, N. Chen and X. Wang, Profit and penalty aware (pp-aware) scheduling for tasks with variable task execution time, in: ACM Symposium on Applied Computing, SAC’10, ACM, 2010, pp. 334–339

    Software Engineering Timeline: major areas of interest and multidisciplinary trends

    Get PDF
    Ingeniería del software. EvolucionSociety today cannot run without software and by extension, without Software Engineering. Since this discipline emerged in 1968, practitioners have learned valuable lessons that have contributed to current practices. Some have become outdated but many are still relevant and widely used. From the personal and incomplete perspective of the authors, this paper not only reviews the major milestones and areas of interest in the Software Engineering timeline helping software engineers to appreciate the state of things, but also tries to give some insights into the trends that this complex engineering will see in the near future
    corecore