Performance-Aware Cost-Effective Resource Provisioning for Future Grid IoT-Cloud System
Publication: Journal of Energy Engineering
Volume 145, Issue 5
Abstract
The rise of the future grid (FG) largely depends on the efficient integration of Internet of Things (IoT) and Cloud computing technologies. By utilizing information and control flows, FG can deliver power more effectively and be capable to handle events occurring anywhere in the grid network. However, maintaining such functions consumes a great deal of computational resource which brings an enormous operational cost to the grid owner. In this paper, we propose an integrated task scheduling and resource provisioning model for dynamically operating an IoT-Cloud system to reduce the overall operational cost. Our proposed approach uses a bipartite graph to model the communication pattern between sensor groups and decentralized cloud data centers and a Pareto distribution-based method to estimate the required resources considering capacity limitation and failure of the system in each data center. We formulate the integrated model as a constraint optimization problem over all sensor groups and data centers. We solve the problem with genetic algorithms due to problem complexity, and our extensive computer simulations and comparisons demonstrate the correctness and effectiveness of the proposed model in minimizing operational cost while satisfying system performance requirements.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
This work was supported in part by the International Joint Project through the Royal Society of the United Kingdom and in part by the National Natural Science Foundation of China under Grant Nos. 61611130209, 61472051, and 61702060.
References
Aazam, M., P. P. Hung, and E. N. Huh. 2014. “Smart gateway based communication for cloud of things.” In Proc., IEEE 9th Int. Conf. on Intelligent Sensors, Sensor Networks and Information Processing, 1–6. Washington, DC: IEEE.
Amekraz, Z., and M. Youssef. 2017. “Prediction of amazon spot price based on chaos theory using anfis model.” In Proc., 2016 IEEE/ACS 13th Int. Conf. of Computer Systems and Applications (AICCSA), 1–6. Washington, DC: IEEE.
Barcelo, M., A. Correa, J. Llorca, A. Tulino, J. L. Vicario, and A. Morell. 2016. “Iot-cloud service optimization in next generation smart environments.” IEEE J. Sel. Areas Commun. 34 (12): 4077–4090. https://doi.org/10.1109/JSAC.2016.2621398.
Botta, A., W. D. Donato, V. Persico, and A. Pescape. 2014. “On the integration of cloud computing and internet of things.” In Proc., Int. Conf. on Future Internet of Things and Cloud, 23–30. Washington, DC: IEEE.
Bui, N., A. P. Castellani, P. Casari, and M. Zorzi. 2012. “The internet of energy: A web-enabled smart grid system.” IEEE Network 26 (4): 39–45. https://doi.org/10.1109/MNET.2012.6246751.
Cecati, C., C. Citro, and P. Siano. 2011. “Combined operations of renewable energy systems and responsive demand in a smart grid.” IEEE Trans. Sustainable Energy 2 (4): 468–476. https://doi.org/10.1109/TSTE.2011.2161624.
Chatterjee, S., R. Ladia, and S. Misra. 1939. “Dynamic optimal pricing for heterogeneous service-oriented architecture of sensor-cloud infrastructure.” IEEE Trans. Services Comput. 10 (2): 203–216. https://doi.org/10.1109/TSC.2015.2453958.
Chavali, P., and A. Nehorai. 2012. “Managing multi-modal sensor networks using price theory.” IEEE Trans. Sig. Process 60 (9): 4874–4887. https://doi.org/10.1109/TSP.2012.2203127.
Chekired, D. A., L. Khoukhi, and H. T. Mouftah. 2018. “Decentralized cloud-sdn architecture in smart grid: A dynamic pricing model.” IEEE Trans. Ind. Inf. 14 (3): 1220–1231. https://doi.org/10.1109/TII.2017.2742147.
Cheng, Z., M. Perillo, and W. B. Heinzelman. 2008. “General network lifetime and cost models for evaluating sensor network deployment strategies.” IEEE Trans. Mobile Comput. 7 (4): 484–497. https://doi.org/10.1109/TMC.2007.70784.
Crovella, M. E. 2000. Performance evaluation with heavy tailed distributions. Berlin: Springer.
Dai, Y. 2009. “Cloud service reliability: Modeling and analysis.” In Proc., 15th IEEE Pacific Rim Int. Symp. on Dependable Computing, 784–789. Washington, DC: IEEE.
Deng, S., L. Huang, J. Taheri, and A. Y. Zomaya. 2015. “Computation offloading for service workflow in mobile cloud computing.” IEEE Trans. Parallel Distrib. Syst. 26 (12): 3317–3329. https://doi.org/10.1109/TPDS.2014.2381640.
Ghosh, R., K. S. Trivedi, V. K. Naik, and S. K. Dong. 2010. “End-to-end performability analysis for infrastructure-as-a-service cloud: An interacting stochastic models approach.” In Proc., IEEE Pacific Rim Int. Symp. on Dependable Computing, 125–132. Washington, DC: IEEE.
Hajjat, M., X. Sun, Y. W. E. Sung, D. Maltz, S. Rao, K. Sripanidkulchai, and M. Tawarmalani. 2010. “Cloudward bound: Planning for beneficial migration of enterprise applications to the cloud.” Acm Sigcomm Comput. Commun. Rev. 40 (4): 243–254. https://doi.org/10.1145/1851275.1851212.
He, Q., J. Han, Y. Yang, H. Jin, J. G. Schneider, and S. Versteeg. 2014. “Formulating cost-effective monitoring strategies for service-based systems.” IEEE Trans. Software Eng. 40 (5): 461–482. https://doi.org/10.1109/TSE.2013.48.
He, Q., X. Xie, Y. Wang, D. Ye, F. Chen, H. Jin, and Y. Yang. 2017. “Localising runtime anomalies in service-oriented systems.” IEEE Trans. Services Comput. 10 (1): 94–106. https://doi.org/10.1109/TSC.2016.2593462.
Huang, Y. F., S. Werner, J. Huang, N. Kashyap, and V. Gupta. 2012. “State estimation in electric power grids: Meeting new challenges presented by the requirements of the future grid.” Sig. Process. Mag. IEEE 29 (5): 33–43. https://doi.org/10.1109/MSP.2012.2187037.
Javadi, B., R. K. Thulasiramy, and R. Buyya. 2012. “Statistical modeling of spot instance prices in public cloud environments.” In Proc., IEEE Int. Conf. on Utility and Cloud Computing, 219–228. Washington, DC: IEEE.
Khurana, H., M. Hadley, N. Lu, and D. A. Frincke. 2010. “Smart-grid security issues.” IEEE Secur. Privacy 8 (1): 81–85. https://doi.org/10.1109/MSP.2010.49.
Li, W., L. W. Y. Xia, Y. Wang, K. Guo, X. Luo, M. Lin, and W. Zheng. 2016. On stochastic performance and cost-aware optimal capacity planning of unreliable infrastructure-as-a-service cloud. Berlin: Springer International Publishing.
Luo, F., Z. Y. Dong, Y. Chen, Y. Xu, K. Meng, and K. P. Wong. 2012. “Hybrid cloud computing platform: The next generation it backbone for smart grid.” In Proc., Power and Energy Society General Meeting, 1–7. Washington, DC: IEEE.
Luo, F., Z. Y. Dong, J. Zhao, X. Zhang, W. Kong, and Y. Chen. 2015. “Enabling the big data analysis in the smart grid.” In Proc., Power and Energy Society General Meeting, 1–5. Washington, DC: IEEE.
Luo, F., J. Zhao, J. Qiu, J. Foster, Y. Peng, and Z. Y. Dong. 2014. “Assessing the transmission expansion cost with distributed generation: An Australian case study.” IEEE Trans. Smart Grid 5 (4): 1892–1904. https://doi.org/10.1109/TSG.2014.2314451.
Marathe, A., R. Harris, D. K. Lowenthal, B. R. D. Supinski, B. Rountree, and M. Schulz. 2016. “Exploiting redundancy and application scalability for cost-effective, time-constrained execution of hpc applications on amazon ec2.” Proc., IEEE Trans. Parallel Distrib. Syst. 27 (9): 2574–2588. https://doi.org/10.1109/TPDS.2015.2508457.
Nakashima, T., and M. Tsuichihara. 2005. “Stability in web server performance with heavy-tailed distribution.” In Proc., Int. Conf. of Knowledge-Based Intelligent Information and Engineering Systems, 575–581. Berlin: Springer.
Ng, S. K., and W. K. G. Seah. 2010. “Game-theoretic approach for improving cooperation in wireless multihop networks.” IEEE Trans. on Syst. Man Cybern. Part B 40 (3): 559–574. https://doi.org/10.1109/TSMCB.2010.2042593.
Nuyens, M. 2003. “The maximum queue length for heavy tailed service times.” Queueing Syst. 47 (1–2): 107–116. https://doi.org/10.1023/B:QUES.0000032803.93977.bd.
Poola, D., K. Ramamohanarao, and R. Buyya. 2014. “Fault-tolerant workflow scheduling using spot instances on clouds.” Procedia Comput. Sci. 29: 523–533. https://doi.org/10.1016/j.procs.2014.05.047.
Qiu, X., H. Li, C. Wu, Z. Li, and F. C. Lau. 2012. “Cost-minimizing dynamic migration of content distribution services into hybrid clouds.” IEEE Trans. Parallel Distrib. Syst. 26 (12): 3330–3345. https://doi.org/10.1109/TPDS.2014.2371831.
Singh, B., S. Dhawan, A. Arora, and A. Patail. 2013. “A view of cloud computing.” Int. J. Comput. Technol. 4 (2–1): 50–58.
Singh, V. K., and K. Dutta. 2015. “Dynamic price prediction for amazon spot instances.” In Proc., Hawaii Int. Conf. on System Sciences, 1513–1520. Washington, DC: IEEE.
Sowmya, K., and R. P. Sundarraj. 2013. “Strategic bidding for cloud resources under dynamic pricing schemes.” In Proc., Int. Symp. on Cloud and Services Computing, 25–30. Washington, DC: IEEE.
Vatamidou, E., I. J. B. F. Adan, M. Vlasiou, and B. Zwart. 2013. “Corrected phase-type approximations for the workload of the map/g/1 queue with heavy-tailed service times.” ACM Sigmetrics Perform. Eval. Rev. 41 (2): 53–55. https://doi.org/10.1145/2518025.2518036.
Vogler, M., J. Schleicher, C. Inzinger, and S. Nastic. 2015. “Leonore: Large-scale provisioning of resource-constrained iot deployments.” In Proc., IEEE Symp. on Service-Oriented System Engineering, 78–87. Washington, DC: IEEE.
Wang, Y., Q. He, D. Ye, and Y. Yang. 2018. “Formulating criticality-based cost-effective fault tolerance strategies for multi-tenant service-based systems.” IEEE Trans. Software Eng. 44 (3): 291–307. https://doi.org/10.1109/TSE.2017.2681667.
Wee, S. 2011. “Debunking real-time pricing in cloud computing.” In Proc., IEEE/ACM Int. Symp. on Cluster, Cloud and Grid Computing, 585–590. Washington, DC: IEEE.
Wu, Q., and F. Ishikawa. 2015. “Heterogeneous virtual machine consolidation using an improved grouping genetic algorithm.” In Proc., IEEE Int. Conf. on High Performance Computing and Communications, 397–404. Washington, DC: IEEE.
Xia, Y., Y. Liu, J. Liu, and Q. Zhu. 2012a. “Modeling and performance evaluation of bpel processes: A stochastic-petri-net-based approach.” IEEE Trans. Syst. Man Cybern. Part A Syst. Humans 42 (2): 503–510. https://doi.org/10.1109/TSMCA.2011.2164064.
Xia, Y., N. Wan, G. Dai, X. Luo, and T. Sun. 2012b. “A non-Markovian stochastic petri net-based approach to performance evaluation of ontology-based service composition.” Concurrency Comput. Pract. Exp. 24 (18): 2255–2267. https://doi.org/10.1002/cpe.2808.
Xia, Y., W. Zhang, M. Zhou, L. Wu, X. Luo, S. Pang, and Q. Zhu. 2017. “Percentile performance estimation of unreliable iaas clouds and their cost-optimal capacity decision.” IEEE Access 5: 1. https://doi.org/10.1109/ACCESS.2017.2666793.
Xia, Y., M. Zhou, X. Luo, S. Pang, Q. Zhu, and J. Li. 2015. “Stochastic modeling and performance analysis of migration-enabled and error-prone clouds.” IEEE Trans. Ind. Inf. 11 (2): 495–504. https://doi.org/10.1109/TII.2015.2405792.
Xia, Y., M. C. Zhou, X. Luo, Q. Zhu, J. Li, and Y. Huang. 2014. “Stochastic modeling and quality evaluation of infrastructure-as-a-service clouds.” IEEE Trans. Autom. Sci. Eng. 12 (1): 162–170. https://doi.org/10.1109/TASE.2013.2276477.
Xia, Y. N., X. Luo, J. Li, and Q. S. Zhu. 2013. “A petri-net-based approach to reliability determination of ontology-based service compositions.” IEEE Trans. Syst. Man Cybern. Part B 43 (5): 1240–1247. https://doi.org/10.1109/TSMCA.2012.2227957.
Xiong, K., and H. Perros. 2009. “Service performance and analysis in cloud computing.” In Proc., Congress on Services—I, 693–700. Washington, DC: IEEE.
Yuriyama, M., and T. Kushida. 2010. “Sensor-cloud infrastructure: Physical sensor management with virtualized sensors on cloud computing.” In Proc., Int. Conf. on Network-Based Information Systems, 1–8. Washington, DC: IEEE.
Zhang, H., G. Jiang, K. Yoshihira, H. Chen, and A. Saxena. 2009. “Intelligent workload factoring for a hybrid cloud computing model.” In Proc., 2009 World Conf. on Services—I, 701–708. Washington, DC: IEEE.
Information & Authors
Information
Published In
Journal of Energy Engineering
Volume 145 • Issue 5 • October 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: Aug 6, 2018
Accepted: Jan 11, 2019
Published online: Jul 15, 2019
Published in print: Oct 1, 2019
Discussion open until: Dec 15, 2019
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.