Abstract
The growth of data and energy-hungry services is due to the increase of Internet of Things (IoT) devices connecting the world. For accessing these devices, the information has to be stored in one place and allowed to retrieve by the users whenever required. For the storage of data, IoT devices are relying on cloud infrastructure by transforming them into distributed one based on the facilities provided by the Internet Service Providers. Due to the large-scale applications, dependency of the services stored on the cloud making the IoT work more efficiently and flexibly. But the Data centers where the IoT data is stored is consuming more energy. The shortage of energy around the world made the researchers to think about energy-efficient data centers. There are plenty of Wireless sensor networks techniques are available, cannot be applied directly to the IoT data centers. The development of energy-efficient schemes becomes a challenging issue in the area of IoT Data Centers. In this chapter, we introduce Big data and how it is related to IoT and the migration of the data to the servers, load balancing among the servers, virtualization, green Computing describing the importance of energy conservation along with the recent analysis of the energy consumption. The various challenges and development of new algorithms by overcoming the limitations of the algorithms will be discussed as part of this chapter. As a conclusion, the future challenges for the energy conservation in IoT will be discussed.
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References
Ashton, K.: That internet of things thing. RFID J 22(7), 97–114 (2009)
Bilal, M.: A review of internet of things architecture, technologies and analysis smartphone-based attacks against 3D printers. arXiv preprint arXiv:1708.04560(2017)
Atzori, L., Iera, A., Morabito, G.: The internet of things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)
Shehzad, M.Q.: Implementation of Intelligent Algorithms on Data Centers for Smart Energy Utilization, University of Oslo, Masters Thesis (2017)
Ghribi, C.: Energy Efficient Resource Allocation in Cloud Computing Environments, Ph.D Thesis, Telecom Sudparis and University of Paris (2014)
Awad, A.I., El-Hefnawy, N.A., Abdelkader, H.M.: Enhanced particle swarm optimization for task scheduling in cloud computing environments. Procedia Comput. Sci. 65, 920–929 (2015)
Eaton, C., Deroos, D., Deutsch, T., Lapis, G., Zikopoulos, P.C.: Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data, Mc Graw-Hill Companies, 978-0-07-179053-6 (2012)
Schneider, R.D.: Hadoop for Dummies Special Edition, John Wiley and Sons Canada, 978-1-118-25051-8 (2012)
Trifu, M.R.A.: Mihaela laura IVAN. Big data: present and future . Database Syst. J. 5(1) (2014)
Isley, P.: Cisco visual networking index: Forecast and methodology, 2014–2019 White Paper, Document ID1458683795628678, Cisco Systems, May (2015)
Rountree, D., Castrillo, I.: Understanding the fundamentals of cloud computing in theory and practice. Newnes, The basics of cloud computing (2013)
Nadeem, M.A.: Cloud computing: security issues and challenges. J. Wirel. Commun. 1(1), 10–15 (2016)
Buyya, R., Vecchiola, C., ThamaraiSelvi, S.: Mastering cloud computing: foundations and applications programming. Newnes (2013)
Kumar, D.K. et al.: Review on virtualization for cloud computing. J. Adv. Res. Comput. Co
Kazim, M., et al.: Security aspects of virtualization in cloud computing. Comput. Information Systems and Industrial Management. Springer, Berlin, Heidelberg, pp. 229–240 (2013)
Jain, R., Paul, S.: Network virtualization and software defined networking for cloud computing: a survey. IEEE Commun. Mag. 51(11), 24–31 (2013)
Kumar, D.K., et al.: Review on virtualization for cloud computing. J. Adv. Res. Comput. Commun. Eng. 3 (2014)
Bittman, T.J.: Server virtualization: one path that leads to cloud computing. Gartner RAS Core Research Note G171730 (2009): 2009
Keller, E., et al.: NoHype: virtualized cloud infrastructure without the virtualization. ACM SIGARCH Comput. Arch. News. 38(3) (2010). ACM
Kashyap, D., Viradiya, J.: A survey of various load balancing algorithms in cloud computing. Int. J. Sci. Technol. Res 3(11), 115–119 (2014)
Jadhav, A.U.: Load balancing using switch mechanism. Int. J. Sci. Res. Educ. (2017)
Singh, A.K., et al.: Scheduling algorithm with load balancing in cloud computing. Int. J. Sci. Eng. Res. 2(1), 38–43 (2014)
Seth, G., Harisha, A.: Energy efficient timing-sync protocol for sensor network. In: 2015 International Conference on Computing and Network Communications (CoCoNet). IEEE (2015)
Brendel, J., et al.: World-wide-web server with delayed resource-binding for resource-based load balancing on a distributed resource multi-node network. U.S. Patent No. 5,774,660. 30 Jun. 1998
Rajan, R.G., Jeyakrishnan, V.: A survey on load balancing in cloud computing environments. Int. J. Adv. Res. Comput. Commun. Eng. 2(12), 4726–4728 (2013)
Gandhi, R., et al.: Duet: cloud scale load balancing with hardware and software. ACM SIGCOMM Comput. Commun. Rev. 44(4), 27–38 (2015)
Sweekriti, Shetty, S.: Distributed and dynamic load balancing in cloud data center. Int. J. Comput. Sci. Mobile Comput. IJCSMC4.5 (2015)
Hewlett Packark Enterprise. Harness the power of IOT data,Real-time decision-making and control at the edge, Business White Paper
Liu, Q., et al.: Green data center with IoT sensing and cloud-assisted smart temperature control system. Comput. Netw. 101, 104–112 (2016)
Thibodeau, P.: Data Centers are the new polluters. http://www.computerworld.com/article/2598562/da ta-center/data-centers-are-the-new-polluters.html, Accessed 5 April 2015
America’s Data Centers Consuming and Wasting Growing amounts of energy. http://www.nrdc.org/energy/data-centerefficiency-assessment.asp. Accessed 6 April 2015
Natural Resources Defense Council. http://www.nrdc.org/about/. Accessed 4 April 2015
Verge, J.: NRDC: Multi-Tenant Data Centers need to play a bigger energy efficiency role. http://www.datacenterknowledge.com/archives/2014/08/26/d ata-center-energy-efficiency-role/. Accessed 5 April 2015
Rahman, A., Liu, X., Kong, F.: A survey on geographic load balancing based data center power management in the smart grid environment. IEEE Commun. Surv. Tutor. 16(1), 214–233 (2014)
Rasmussen, N.: Electrical Efficiency Modelling for Data Centers. White paper#113, APC Legendary Reliability (2007)
Dayarathna, M., Wen, Y., Fan, R.: Data center energy consumption modeling: a survey. IEEE Commun. Surv. Tutor. 18(1) (2016)
Arghandeh, R., Pipattanasomporn, M., Rahman, S.: Flywheel energy storage systems for ride-through applications in a facility Microgrid. IEEE Trans. Smart Grid 3(4), 1955–1962 (2012)
Bird, S., et al.: Distributed (green) data centers: a new concept for energy, computing, and telecommunications. Energy Sustain. Dev. 19, 83–91 (2014)
Thorwat, P.D., Shetty, S.: Implementation of multilevel authentication scheme for multicloud environment. In: International Conference on Information and Communication Technologies. Karnataka, India (2014)
Kaewpuang, R., et al.: Adaptive power management for data center in smart grid environment. In: 2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications (ISPA). IEEE (2012)
Chen, C., He, B., Tang, X.: Green-aware workload scheduling in geographically distributed data centers. In: 2012 IEEE 4th International Conference on Cloud Computing Technology and Science (CloudCom). IEEE (2012)
Guyon, D., et al.: How much energy can green HPC cloud users save? In: 2017 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP). IEEE (2017)
Andersen, D.G., et al.: FAWN: a fast array of wimpy nodes. In: Proceedings of the ACM SIGOPS 22nd Symposium on Operating Systems Principles. ACM (2009)
Caulfield, A.M., Grupp, L.M., Swanson, S.: Gordon: using flash memory to build fast, power-efficient clusters for data-intensive applications. ACM Sigplan. Notices 44(3), 217–228 (2009)
Tiwari, V., Ashar, P., Malik, S.: Technology mapping for low power. In: Processing of 30th ACM International Design Automation Conference, DAC 1993, 74–79, New York, NY (1993)
Su, C.L., Tsui, C.Y., Despain, A.M.: Saving power in the control path of embedded processors. IEEE Design Test Comput. 11(4), 24–31 (1994)
Sharma, N.K., Ram Mohana Reddy, G.: A novel energy efficient resource allocation using hybrid approach of genetic dvfs with bin packing. In: 2015 Fifth International Conference on Communication Systems and Network Technologies (CSNT). IEEE (2015)
Ghribi, C., Hadji, M., Zeghlache, D.: Energy efficient vm scheduling for cloud data centers: Exact allocation and migration algorithms. In: 2013 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). IEEE (2013)
Letchford, A.N., Ni, Q., Zhong, Z.: An exact algorithm for a resource allocation problem in mobile wireless communications. Comput. Optim. Appl. 68(2), 193–208 (2017)
Brusco, M.J.: An exact algorithm for a workforce allocation problem with application to an analysis of cross-training policies. Iie Trans. 40(5), 495–508 (2008)
Yu, Y., Gao, Y.: Constraint programming-based virtual machines placement algorithm in datacenter. In: International Conference on Intelligent Information Processing. Springer, Berlin, Heidelberg (2012)
Ge, R., Feng, X., Cameron, K.W.: Performance-constrained distributed dvs scheduling for scientific applications on power-aware clusters. In: Proceedings of the ACM/IEEE SC 2005 Conference Supercomputing, 2005. IEEE (2005)
Hsu, C.-H., Feng, W.-C.: A power-aware run-time system for high-performance computing. In: Proceedings of the 2005 ACM/IEEE Conference on Supercomputing. IEEE Computer Society (2005)
Snowdon, D.C., Ruocco, S., Heiser, G.: Power management and dynamic voltage scaling: myths and facts. In: Proceedings of the 2005 Workshop on Power Aware Real-Time Computing, Vol. 12 (2005)
Alahmadi, A,, et al.: Enhanced first-fit decreasing algorithm for energy-aware job scheduling in cloud. In: 2014 International Conference on Computational Science and Computational Intelligence (CSCI), Vol. 2. IEEE (2014)
Verma, A., Ahuja, P., Neogi, A.: pMapper: power and migration cost aware application placement in virtualized systems. In: Proceedings of the 9th ACM/IFIP/USENIX International Conference on Middleware, pp. 243–264 (2008)
Beloglazov, A., Buyya, R.: Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Trans. Parallel Distrib. Syst. 24(7), 1366–1379 (2013)
Quang-Hung, N., et al.: A genetic algorithm for power-aware virtual machine allocation in private cloud. In: Information and Communication Technology-EurAsia Conference. Springer, Berlin, Heidelberg (2013)
Kaur, J., Bahl, K.: Job Scheduling in Cloud Computing Using Genetic Algorithm
Singh, S., Kalra, M.: Scheduling of independent tasks in cloud computing using modified genetic algorithm. In: 2014 International Conference on Computational Intelligence and Communication Networks (CICN). IEEE (2014)
Dam, S., et al.: Genetic algorithm and gravitational emulation based hybrid load balancing strategy in cloud computing. In: 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT). IEEE (2015)
Acharya, J., Mehta, M., Saini, B.: Particle swarm optimization based load balancing in cloud computing. In: International Conference on Communication and Electronics Systems (ICCES). IEEE (2016)
Chimakurthi, L.: Power efficient resource allocation for clouds using ant colony framework. arXiv preprint arXiv:1102.2608 (2011)
Jing, C.: Ant-colony optimization based algorithm for energy-efficient scheduling on dynamically reconfigurable systems. In: 2015 Ninth International Conference on Frontier of Computer Science and Technology (FCST). IEEE (2015)
Liu, X.-F., et al.: An energy efficient ant colony system for virtual machine placement in cloud computing. IEEE Trans. Evolut. Comput. 22, 113–128 (2018)
Rahim, S. et al.,: Ant colony optimization based energy management controller for smart grid. In: 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA). IEEE (2016)
Gabi, D., et al.: Hybrid cat swarm optimization and simulated annealing for dynamic task scheduling on cloud computing environment. J. ICT 17(3), 435–467 (2018)
Addya, S.K., et al.: Simulated annealing based VM placement strategy to maximize the profit for Cloud service providers. Eng. Sci. Technol. Int. J. 20(4), 1249–1259 (2017)
Liu, X., Liu, J.: A task scheduling based on simulated annealing algorithm in cloud computing. Int. J. Hybrid Inf. Technol. 9(6), 403–412 (2016)
Sharma, N.K., Reddy Guddeti, R.M.: On demand virtual machine allocation and migration at cloud data center using Hybrid of Cat Swarm optimization and genetic algorithm. In: 2016 Fifth International Conference on Eco-friendly Computing and Communication Systems (ICECCS). IEEE (2016)
Chu, S.-C., Tsai, P.-W., Pan, J.-S.: Cat swarm optimization. In: Pacific Rim International Conference on Artificial Intelligence. Springer, Berlin, Heidelberg (2006)
Bahrami, M., Bozorg-Haddad, O., Chu, X.: Cat Swarm Optimization (CSO) Algorithm. Advanced Optimization by Nature-Inspired Algorithms. Springer, Singapore, pp. 9–18 (2018)
Pradhan, P.M., Panda, G.: Solving multiobjective problems using cat swarm optimization. Expert Systems with Applications, pp. 2956–2964. Elsevier Ltd (2011)
Sharma, N.K., Reddy Guddeti, R.M.: On demand virtual machine allocation and migration at cloud data center using hybrid of cat swarm optimization and genetic algorithm. In: 2016 Fifth International Conference on Eco-friendly Computing and Communication Systems (ICECCS). IEEE (2016)
Sharma, N.K., Ram Mohana Reddy, G.: A novel approach for multi-dimensional variable sized virtual machine allocation and migration at cloud data center. In: 2017 9th International Conference on Communication Systems and Networks (COMSNETS). IEEE (2017)
Sharma, N.K.: Energy Efficient Resources Management and Task Scheduling at Cloud Data Center, Thesis NITK surathkal (2018)
Sharma, N.K., Ram Mohana Reddy, G.: Novel energy efficient virtual machine allocation at data center using genetic algorithm. In: 2015 3rd International Conference on Signal Processing, Communication and Networking (ICSCN). IEEE (2015)
Alsubaihi, S., Gaudiot, J.-L.: PETS: performance, energy and thermal aware scheduler for job mapping with resource allocation in heterogeneous systems. In: 2016 IEEE 35th International Performance Computing and Communications Conference (IPCCC). IEEE (2016)
Ngatman, M.F., Sharif, J.M., AsriNgadi, Md.: A study on modified PSO algorithm in cloud computing. In: 2017 6th ICT International Student Project Conference (ICT-ISPC). IEEE (2017)
Sharma, N.K., Mohana Guddeti, R.: Multi-objective energy efficient virtual machines allocation at the cloud data center. IEEE Transactions on Services Computing (2016)
Sharma, N.K., Mohana Reddy Guddeti, R.: Multi-objective resources allocation using improved genetic algorithm at cloud data center. In: 2016 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM). IEEE (2016)
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Rudra, B., Prashanth, D.S. (2019). Models and Algorithms for Energy Conservation in Internet of Things. In: Mittal, M., Tanwar, S., Agarwal, B., Goyal, L. (eds) Energy Conservation for IoT Devices . Studies in Systems, Decision and Control, vol 206. Springer, Singapore. https://doi.org/10.1007/978-981-13-7399-2_4
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