RETRACTED: Minimum makespan task scheduling algorithm in cloud computing

RETRACTEDFollowing a rigorous, carefully concerns and considered review of the article published in International Journal of Advances in Intelligent Informatics to article entitled “Minimum makespan task scheduling algorithm in cloud computing” Vol 2, No 3, pp. 123-130, November 2016, DOI: http://dx.doi.org/10.26555/ijain.v2i3.59.This paper has been found to be in violation of the International Journal of Advances in Intelligent Informatics Publication principles and has been retracted.The article contained redundant material, the editor investigated and found that the paper published in International Journal of Grid and Distributed Computing, Vol. 9, No. 11, pp. 61-70, 2016, DOI: http://dx.doi.org/10.14257/ijgdc.2016.9.11.05.The document and its content has been removed from International Journal of Advances in Intelligent Informatics, and reasonable effort should be made to remove all references to this article

Agreement technologies and their use in cloud computing environments

The final publication is available at Springer via http://dx.doi.org/10.1007/s13748-012-0031-9[EN] Nowadays, cloud computing is revolutionizing the services provided through the Internet to adapt itself in order to keep the quality of its services. Recent research foresees the advent of a new discipline of agent-based cloud computing systems that can make decisions about adaption in an uncertain environment. This paper discusses the role of argumentation in the next generation of agreement technologies and its use in cloud computing environments.This work is supported by the Spanish government (MICINN), project reference: TIN2012-36586-C03-01.Heras Barberá, SM.; De La Piedra, F.; Julian Inglada, VJ.; Rodríguez, S.; Botti Navarro, VJ.; Bajo, J.; Corchado, JM. (2012). Agreement technologies and their use in cloud computing environments. Progress in Artificial Intelligence. 1(4):277-290. https://doi.org/10.1007/s13748-012-0031-9S27729014European Comission: The Future of Cloud Computing. Technical report (2010)Barham, P., Dragovic, B., Fraser, K., Hand, S., Harris, T., Ho, A., Neugebauer, R., Pratt, I., Warfield, A.: Xen and the art of virtualization. In: SOSP03 Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles, pp. 164–177. ACM, New York (2003)Wang, L., et. al.: Scientific cloud computing: early definition and experience. In: 10th IEEE International Conference on High Performance Computing and Communications (HPCC-08), pp. 825–830. IEEE Press (2008)Talia, D.: Clouds meet agents: toward intelligent cloud services. Internet Comput. IEEE 16(2), 78–81 (2012). doi: 10.1109/MIC.2012.28Heras, S.: Case-Based Argumentation Framework for Agent Societies. PhD thesis, Universitat Politècnica de València. http://hdl.handle.net/10251/12497 (2011)Ashton, K.: That ‘internet of things’ thing. RFID J. (2009). http://www.rfidjournal.com/article/view/4986Klusch, M.: Information agent technology for the Internet: a Survey. Data Knowl. Eng. 36, 337–372 (2001)Schaffer, H.E.: X as a Service. Cloud Computing, and the Need for Good Judgment IT Professional 11(5), 4–5 (2009). doi: 10.1109/MITP.2009.112Richardson, L., Ruby, S.: RESTful Web Services, Web services for the real world O’Reilly, Media, May, p. 454 (2007)GlusterFS Developers. The Gluster web site. http://www.gluster.org (2012)Chodorow, K., Dirolf, M.: The Definitive Guide. O’Reilly Media, MongoDB (2010)Fuentes-Fernandez, R., Hassan, S., Pavon, J., Galan, J.M., Lopez-Paredes, A.: Metamodels for role-driven agent-based modelling. Comput. Math. Organ. Theory 18(1), 91–112 (2012)Jordán, J., et al.: A customer support application using argumentation in multi-agent systems. In: 14th International Conference on, Information Fusion, pp. 772–778 (2011)Heras, S., Jordán, J., Botti, V., Julián, V.: Argue to agree: a case-based argumentation approach. Int. J. Approx. Reasoning (2012, in press)Walton, D., Reed, C., Macagno, F.: Argumentation Schemes. Cambridge University Press, Cambridge (2008)Bench-Capon, T., Sartor, G.: A model of legal reasoning with cases incorporating theories and values. Artif. Intell. 150(1–2), 97–143 (2003)Dignum, F., Weigand, H.: Communication and deontic logic. In: Information Systems Correctness and Reusability, pp. 242–260. World Scientific, Singapore (1995)Wooldridge, M., Jennings, N.R.: Intelligent agents: theory and practice. Knowl. Eng. Rev. 10(2), 115–152 (1995)Lopez-Rodriguez, I., Hernandez-Tejera, M.: Software agents as cloud computing services. In: 9th International Conference on Practical Applications of Agents and Multiagent Systems. Advances in Intelligent and Soft Computing, vol. 88, pp. 271–276. Springer, Berlin (2011)Sim, K.M.: Towards complex negotiation for cloud economy. In: 5th International Conference on Advances in Grid and Pervasive Computing. LNCS, vol. 6104, pp. 395–406. Springer, Berlin (2010)Aversa, R., et al.: Cloud agency: a mobile agent based cloud system. In: International Conference on Complex, Intelligent and Software Intensive Systems, pp. 132–137. IEEE Computer Society Press, Washington, DC (2010)Cao, B., et al.: A service-oriented qos-assured and multi-agent cloud computing architecture. In: 1st International Conference on Cloud Computing. LNCS, vol. 5931, pp. 644–649. Springer, Berlin (2009)Rahwan, I., Simari, G. (eds.): Argumentation in Artificial Intelligence. Springer, Berlin (2009

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

A State-of-the-art Integrated Transportation Simulation Platform

Nowadays, universities and companies have a huge need for simulation and modelling methodologies. In the particular case of traffic and transportation, making physical modifications to the real traffic networks could be highly expensive, dependent on political decisions and could be highly disruptive to the environment. However, while studying a specific domain or problem, analysing a problem through simulation may not be trivial and may need several simulation tools, hence raising interoperability issues. To overcome these problems, we propose an agent-directed transportation simulation platform, through the cloud, by means of services. We intend to use the IEEE standard HLA (High Level Architecture) for simulators interoperability and agents for controlling and coordination. Our motivations are to allow multiresolution analysis of complex domains, to allow experts to collaborate on the analysis of a common problem and to allow co-simulation and synergy of different application domains. This paper will start by presenting some preliminary background concepts to help better understand the scope of this work. After that, the results of a literature review is shown. Finally, the general architecture of a transportation simulation platform is proposed