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A Framework for Heterogeneous Resource Allocation in Sensor-Cloud Environment

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Abstract

With the advent of sensor technologies, cloud applications are able to acquire sensed data from remotely located sensors which are geographically distributed. Cloud computing, when integrated with sensor devices facilitates pervasive and shared access to the sensor data from geographically distributed sensors. There have been some research works targeting integration of cloud and sensors. However, so far integration of sensors and applications are done at the application layer. On the other hand, our objective is to propose a sensor-cloud architecture which creates virtual sensors, i.e. a layer of abstraction at the Infrastructure as a Service level. In this architecture, remotely located sensing resources are treated as citizens of generic resource family just like CPU, memory and storage devices. However, in order to realize this architecture, a resource model is required. The resource model should be sufficiently generic, so that it can incorporate computational, as well as sensing resources. In this paper one such generic resource model along with request, reservation and allocation facilities for both computational and sensing resources is presented.

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References

  1. Galetić, V., Bojić, I., Kušek, M., Ježić, G., Dešić, S., & Huljenić, D. (2011). Basic principles of machine-to-machine communication and its impact on telecommunications industry. In 2011 proceedings of the 34th international convention MIPRO.

  2. Bojic, I., Jezic, G., Katusic, D., Desic, S., Kusek, M., & Huljenic, D. (2012). Communication in machine-to-machine environments. In Proceedings of the fifth Balkan conference in informatics.

  3. Zaslavsky, A., Perera, C., & Georgakopoulos, D. (2013). Sensing as a service and big data. CoRR. arXiv:abs/1301.0159

  4. Alamri, A., Ansari, W. S., Hassan, M. M., Hossain, M. S., Alelaiwi, A., & Hossain, M. A. (2013). A survey on sensor-cloud: architecture, applications, and approaches. International Journal of Distributed Sensor Networks (Vol. 2013). Article ID 917923.

  5. Mukherjee, N., Bhunia, S. S., & Bose, S. (2016). Virtual sensors in remote healthcare delivery: some case studies. In 9th international conference on health informatics (HEALTHINF).

  6. Rajesh, V., Gnanasekar, J. M., Ponmagal, R. S., & Anbalagan, P. (2010) Integration of wireless sensor network with cloud. In Recent trends in information, telecommunication and computing (ITC).

  7. Bose, S., Gupta, A., Adhikari, S., & Mukherjee, N. (2015). Towards a sensor-cloud infrastructure with sensor virtualization. In The second workshop for mobile sensing, computing, and communication 2015 (ACM MobiHoc 2015).

  8. Bose, S., Mukherjee, N., & Mistry, S. (2016). Environment monitoring in smart cities using virtual sensors. In 2016 IEEE 4th international conference on future internet of things and cloud.

  9. Mitton, N., Papavassiliou, S., Puliafito, A., & Trivedi, K. S. (2012). Combining cloud and sensors in a smart city environment. EURASIP Journal on Wireless Communications and Networking, 2012(1), 247. https://doi.org/10.1186/1687-1499-2012-247.

    Article  Google Scholar 

  10. Hassan, M. M., Song, B., & Huh, E. (2009). A framework of sensor-cloud integration opportunities and challenges. In Proceedings of the 3rd international conference on ubiquitous information management and communication.

  11. Kabadayi, S., Pridgen, A., & Julien, C. (2006). Virtual sensors: abstracting data from physical sensors. In Proceedings of the 2006 international symposium on on world of wireless, mobile and multimedia networks (WOWMOM).

  12. Liu, L., Kuo, S., & Zhou, M. (2009). Virtual sensing techniques and their applications. In Proceedings of the 2009 IEEE international conference on networking, sensing and control, ICNSC 2009 (pp. 31–36).

  13. Wu, Z., X, N., Huang, Y., & Gu, Q. (2014). Optimal service distribution in wsn service system subject to data security constraints. Sensors, 14(8), 863–880.

    Google Scholar 

  14. Yuriyama, M., & Kushida, T. (2010). Sensor-cloud infrastructure-physical sensor management with virtualized sensors on cloud computing. In 13th international conference on network-based information systems 2010.

  15. Alaa, M., Zaidan, A. A., Zaidan, B. B., Talal, M., & Kiah, M. L. M. (2017). A review of smart home applications based on Internet of Things. Journal of Network and Computer Applications, 97, 48–65.

    Article  Google Scholar 

  16. Alam, S., Chowdhury, M. M. R., & Noll, J. (2010). SenaaS: An event-driven sensor virtualization approach for internet of things cloud. In Networked embedded systems for enterprise applications (NESEA).

  17. Madria, S., Kumar, V., & Dalvi, R. (2014). Sensor cloud: A cloud of virtual sensors. IEEE Software, 31(2), 70–77.

    Article  Google Scholar 

  18. Hussein, A. J., Riadh, A., Alsultan, M., & Tareq, A. A.-R. (2014). Applications and design for a cloud of virtual sensors. Sensors, 1(2), 3.

    Google Scholar 

  19. Hensel, B., & Kabitzsch, K. (2016). Generator for modular virtual sensors. In IEEE 21st international conference on emerging technologies and factory automation (ETFA), Berlin, 2016 (pp. 1–8).

  20. Kullaa, J. (2017). Development of virtual sensors to increase the sensitivity to damage. In Procedia engineering (Vol. 199, pp. 1937–1942).

  21. Hirsenkorn, N., Hanke, T., Rauch, A., Dehlink, B., Rasshofer, R., & Biebl, E. (2016). Virtual sensor models for real-time applications. Advances in Radio Science, 14, 31–37.

    Article  Google Scholar 

  22. Ackaouy, E. (2006). The Xen credit CPU scheduler. In Proceedings of 2006 Fall Xen Summit

  23. Vakilinia, Shahin, Ali, Mustafa Mehmet, & Qiu, Dongyu. (2015). Modeling of the resource allocation in cloud computing centers. Computer Networks, 91, 453–470.

    Article  Google Scholar 

  24. Kim, A., Lee, J., & Kim, M. (2016). Resource management model based on cloud computing environment. International Journal of Distributed Sensor Networks, 12(11), 35–39.

    Article  Google Scholar 

  25. Zhu, Y., & Wang, Y. (2013). A model of cloud computing resources. In 2013 international conference on computer sciences and applications, Wuhan (pp. 684–686).

  26. Khatua, S., Sur, P. K., Das, R. K., & Mukherjee, N. (2016). Heuristic-based resource reservation strategies for public cloud. IEEE Transactions on Cloud Computing, 4(4), 392–401.

    Article  Google Scholar 

  27. Bose, S., & Mukherjee, N. (2016). SensIaas: A sensor-cloud infrastructure with sensor virtualization. In 2016 IEEE 3rd international conference on cyber security and cloud computing

  28. Bose, S., & Mukherjee, N. (2016). PrIOR: A prime number based I/O redirection algorithm for sensor-cloud infrastructure. In The international conference on high performance computing & simulation (HPCS 2016).

  29. Cherkasova, L., Gupta, D., & Vahdat, A. (2007). Comparison of the Three CPU schedulers in Xen. ACM SIGMETRICS Performance Evaluation Review, 35(2), 42–51.

    Article  Google Scholar 

  30. Kim, Nakku, Cho, J., & Seo, E. (2014). Energy-credit scheduler: An energy-aware virtual machine scheduler for cloud systems. Future Generation Computer Systems, 32, 128–137.

    Article  Google Scholar 

  31. Configuring Resource Throttling for VMM Technet.microsoft.com, 2016. [Online]. https://technet.microsoft.com/en-us/library/jj628160(v=sc.12).aspx. Accessed July 29, 2016.

  32. Tuning Xen for Performance—Xen Wiki.xen.org, 2016. [Online]. http://wiki.xen.org/wiki/Tuning_Xen_for_Performance. Accessed July 29, 2016.

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Acknowledgements

This work is supported by Information Technology Research Academy (ITRA), Government of India under, ITRA-Mobile Grant ITRA/15(59)/ Mobile/RemoteHealth/01.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Sunanda Bose, Debangana Sarkar & Nandini Mukherjee

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  1. Sunanda Bose
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  2. Debangana Sarkar
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  3. Nandini Mukherjee
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Correspondence to Sunanda Bose.

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Bose, S., Sarkar, D. & Mukherjee, N. A Framework for Heterogeneous Resource Allocation in Sensor-Cloud Environment. Wireless Pers Commun 108, 19–36 (2019). https://doi.org/10.1007/s11277-019-06383-1

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