Abstract
The gaining popularity of the Internet of Things (IoT), big data analytics, and blockchain to make the digital world connected, smart, and secure in the context of smart cities have led to increasing use of the cloud computing technology. Consequently, cloud data centers become hungry for energy consumption. This has an adverse effect on the environment in addition to the high operational and maintenance costs of large-scale data centers. Several works in the literature have proposed energy-efficient task scheduling in a cloud computing environment. However, most of these works use a scheduler that predicts the power consumption of an incoming task based on a static model. In most scenarios, the scheduler considers the CPU utilization of a server for power prediction and task allocations. This might give misleading results as the power consumption of a server, handling a variety of requests in smart cities, depends on other metrics such as memory, disk, and network in addition to CPU. Our proposed Intelligent Autonomous Agent Energy-Aware Task Scheduler in Virtual Machines (IAA-EATSVM) uses the multi-metric machine learning approach for scheduling of incoming tasks. IAA-EATSVM outperforms the mostly used Energy Conscious Task Consolidation (ECTC) based on a static approach. The detailed performance analysis is elaborated in the paper.
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References
Mell, P., Grance, T.: The NIST definition of cloud computing recommendations of the national institute of standards and technology. NIST Spec. Publ. 145, 7 (2011). https://doi.org/10.1136/emj.2010.096966
Richter, A., Khoshgoftaar, T.: Efficient learning from big data for cancer risk modeling: a case study with melanoma. Comput. Biol. Med. 110, 29–39 (2019)
Xia, F., Yang, L.T., Wang, L., Vinel, A.: Internet of things. Int. J. Commun. Syst. 25 (2012)
Al Omar, A., Alam Bhuiyan, Z.M., Basu, A., Kiyomoto, S.: Privacy-friendly platform for healthcare data in cloud based on blockchain environment. Futur. Gener. Comput. Syst. 95, 511–521 (2019)
DELFORGE P (2015) America’s Data Centers Consuming and Wasting Growing Amounts of Energy| NRDC. https://www.nrdc.org/resources/americas-data-centers-consuming-and-wasting-growing-amounts-energy. Accessed 10 Sep 2019
Vidal J Global carbon emission. https://www.climatechangenews.com/2017/12/11/tsunami-data-consume-one-fifth-global-electricity-2025/
Greenberg, S., Mills, E., Tschudi, B., Berkeley, L.: Best practices for data centers : lessons learned from benchmarking 22 data centers T. Aceee SUMMER, 76–87 (2006). https://doi.org/10.1016/j.energy.2012.04.037
Ham, S.W., Kim, M.H., Choi, B.N., Jeong, J.W.: Simplified server model to simulate data center cooling energy consumption. Energy Build. 86, 328–339 (2015). https://doi.org/10.1016/j.enbuild.2014.10.058
Dai, X., Wang, J.M., Bensaou, B.: Energy-efficient virtual machines scheduling in multi-tenant data centers. IEEE Trans. Cloud Comput. 4, 210–221 (2016). https://doi.org/10.1109/TCC.2015.2481401
Lee, Y.C., Zomaya, A.Y.: Energy efficient utilization of resources in cloud computing systems. J. Supercomput. 60, 268–280 (2012). https://doi.org/10.1007/s11227-010-0421-3
Ismail, L., Materwala, H.: EATSVM: energy-aware task scheduling on cloud virtual machines. Procedia Comput. Sci. 135, 248–258 (2018). https://doi.org/10.1016/j.procs.2018.08.172
Huai, W., Qian, Z., Li, X., et al.: Energy aware task scheduling in data centers. J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl. 4, 18–38 (2013)
Ying, C.T., Yu, J.: Energy-aware genetic algorithms for task scheduling in cloud computing. In: Proc—7th ChinaGrid Annu Conf ChinaGrid 2012, pp. 43–48 (2012). https://doi.org/10.1109/ChinaGrid.2012.15
Wu, C.M., Chang, R.S., Chan, H.Y.: A green energy-efficient scheduling algorithm using the DVFS technique for cloud datacenters. Futur. Gener. Comput. Syst. 37, 141–147 (2014). https://doi.org/10.1016/j.future.2013.06.009
Qureshi, B.: Profile-based power-aware workflow scheduling framework for energy-efficient data centers. Futur. Gener. Comput. Syst. 94, 453–467 (2019). https://doi.org/10.1016/j.future.2018.11.010
Ilager, S., Ramamohanarao, K., Buyya, R.: ETAS: energy and thermal-aware dynamic virtual machine consolidation in cloud data center with proactive hotspot mitigation. Concurr. Comput., 1–15 (2019). https://doi.org/10.1002/cpe.5221
Fan, X., Weber, W.-D., Barroso, L.A.: Power provisioning for a warehouse-sized computer. ACM SIGARCH Comput. Archit. News 35, 13 (2007). https://doi.org/10.1145/1273440.1250665
Bircher, W.L., John, L.K.: Complete system power estimation using processor performance events. IEEE Trans. Comput. 61, 563–577 (2011)
Kim, N., Cho, J., Seo, E.: Energy-credit scheduler: an energy-aware virtual machine scheduler for cloud systems. Futur. Gener. Comput. Syst. 32, 128–137 (2014). https://doi.org/10.1016/j.future.2012.05.019
Calheiros, R.N., Ranjan, R., Beloglazov, A., et al.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw—Pract Exp 41 (2011)
Carlucci G CPULoadGenerator. https://github.com/GaetanoCarlucci/CPULoadGenerator. Accessed 1 Sep 2019
Stress project page. https://people.seas.harvard.edu/~apw/stress/. Accessed 1 Sep 2019
Vandenbergh, H.: Vdbench Users Guide, 1–114 (2012)
Mortimer, M.: iperf3 Documentation (2018)
Linux perf Examples. http://www.brendangregg.com/perf.html. Accessed 2 Sep 2019
Collectd: https://collectd.org/wiki/index.php/Main_Page. Accessed 1 Sep 2019
Tektronix: User Manual TDS1000- and TDS2000-Series Digital Storage Oscilloscope. 206 (2009)
Wedlock, B.D., Roberge, J.K., James, K.: Electronic Components and Measurements. Prentice-Hall (1969)
R: What is R? https://www.r-project.org/about.html
Kopytov A Sysbench. https://github.com/akopytov/sysbench#sysbench. Accessed 14 Aug 2018
MPlayer—The Movie Player. http://www.mplayerhq.hu/design7/dload.html. Accessed 14 Aug 2018
The PARSEC Benchmark Suite. http://parsec.cs.princeton.edu/parsec3-doc.htm. Accessed 14 Aug 2018
Weka 3—Data Mining with Open Source Machine Learning Software in Java. https://www.cs.waikato.ac.nz/ml/weka/downloading.html. Accessed 25 Aug 2018
H264 Video Format. http://www.h264info.com/h264.htmlis. Accessed 14 Aug 2018
UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/index.php. Accessed 25 Aug 2018
The energy rate report. https://www.chooseenergy.com/electricity-rates-by-state/. Accessed 25 Feb 2020
Carbon dioxide emission. https://carbonfund.org/calculation-methods/. Accessed 25 Feb 2020
Ismail, L., Materwala, H.: Energy-Aware VM placement and task scheduling in Cloud-IoT computing: classification and performance evaluation. IEEE Internet. Things J. 5, 5166–5176 (2018). https://doi.org/10.1109/JIOT.2018.2865612
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This work was funded by the Emirates Center for Energy and Environment Research, United Arab Emirates University, under Grant 31R101.
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Ismail, L., Materwala, H. (2020). Artificial Intelligent Agent for Energy Savings in Cloud Computing Environment: Implementation and Performance Evaluation. In: Jezic, G., Chen-Burger, J., Kusek, M., Sperka, R., Howlett, R., Jain, L. (eds) Agents and Multi-Agent Systems: Technologies and Applications 2020. Smart Innovation, Systems and Technologies, vol 186. Springer, Singapore. https://doi.org/10.1007/978-981-15-5764-4_12
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