Top

Energy Systems

Published in:

19-11-2019 | Original Paper

Adaptive cloud resource management through workload prediction

Authors: Lata J. Gadhavi, Madhuri D. Bhavsar

Published in: Energy Systems | Issue 3/2022

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Resource management strategy in adaptive cloud provisions the needed resources dynamically to the end-users. To improve the runtime performance of adaptive cloud for service-based applications, two aspects of technical issues are required to be addressed. The first one is the balancing of a large amount of data on existing resources and the second is resource provisioning which can adjust the number of resources optimally to adapt the time-varying workload. As the growth of data is increasing tremendously, efficient resource management is the need in cloud computing. We build a cloud framework to process data in automation with adaptive resource and workload management strategy. Numbers of approaches are reviewed and applied for workload prediction. We developed the model Auto-Regressive Integrated Moving Average-workload Prediction for Efficient Resource Provisioning (ARIMA-PERP) and evaluated the results that can satisfy the on-demand need of end-users for efficient resource utilization. To serve the maximum number of user requests, performance metrics of the proposed approach are evaluated. It is observed that our evaluated results achieved an accurate prediction by 91.11%, which meets the efficient resource utilization for the demanded workload. As compared with the exiting approach, we achieved better performance by 0.11% for accurate prediction. The proposed architecture is intended to provide the resources dynamically and efficiently satisfying the demands of the user. To achieve this objective of efficient resource provisioning, algorithms are developed for workload prediction which helps in deciding optimum resource provisioning. Our system uses proactive approach resource management and deployment of the adaptive cloud system. In traditional systems, resources are managed based on demand, availability and the strategy of scheduling which results in delayed response time at large. We configured the ARIMA model to predict the future workload for provisioning the resources dynamically and remove the problem of over-provisioning and under-provisioning in a cloud environment.

previous article Scalable intelligent data-driven decision making for cognitive cities

next article RETRACTED ARTICLE: Indian smart city ranking model using taxicab distance-based approach

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Ji, C., Li, Y., Qiu, W., Awada, U., Li, K.: Big data processing in cloud computing environments. In: 12th International Symposium on Pervasive Systems, Algorithms and Networks, pp. 17–23, San Marcos (2012)

Li, C., Zhuang, H., Lu, K., Sun, M., Zhou, J., Dai, D., Zhou, X.: An adaptive auto-configuration tool for Hadoop. In: 19th International Conference on Engineering of Complex Computer Systems, Washington, pp. 69–72 (2014)

Ficco, M.: Security event correlation approach for cloud computing. Int. J. High Perform. Comput. Netw. 7, 173–185 (2013)CrossRef

Banu, M., Aranganathan, A.: Study of load optimization and performance issues in cloud. Ind. J. Electr. Eng. Comput. Sci. 11(3), 1035–1041 (2018)

Amiri, M., Mohammad-Khanli, L.: Survey on prediction models of applications for resources provisioning in cloud. J. Netw. Comput. Appl. 82, 93–113 (2017)CrossRef

Di, S., Kondo, D., Cirne, W.: Host load prediction in a Google compute cloud with a Bayesian model. In: SC ‘12: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, pp. 1–11, Salt Lake City (2012)

Cuomo, A., Rak, M.: Villano, U, Performance prediction of cloud applications through benchmarking and simulation. Int. J. Comput. Sci. Eng. 11(1), 46–55 (2015)

Song, W., Xiao, Z., Chen, Q., Luo, H.: Adaptive resource provisioning for the cloud using online bin packing. IEEE Trans. Comput. 63(11), 2647–2660 (2014)MathSciNetCrossRef

Singh, S., Chana, I.: Resource provisioning and scheduling in clouds: QoS perspective. J. Supercomput. 72(3), 926–960 (2016)CrossRef

10.

Liang, Q., Zhang, J., Zhang, Y.H., Liang, J.M.: The placement method of resources and applications based on request prediction in cloud data center. Inf. Sci. 279, 735–745 (2014)CrossRef

11.

Yang, J., Liu, C., Shang, Y., Chen, B., Mao, Z., Liu, C., Niu, L., Chen, J.: A cost- aware auto-scaling approach using the workload prediction in service clouds. Inf. Syst. Front. 16(1), 7–18 (2014)CrossRef

12.

Chang, Y.C., Chang, R.S., Chuang, F.W.: A predictive method for workload forecasting in the cloud environment. In: Lecture Notes in Electrical Engineering, vol. 260. Springer, New York, pp. 577–585 (2014)

13.

Jiang, Y., Perng, C.-S., Li, T., Chang, R.N.: Cloud analytics for capacity planning and instant VM provisioning. IEEE Trans. Netw. Serv. Manag. 10(3), 312–325 (2013)CrossRef

14.

Alasaad, A., Shafiee, K., Behairy, H.M., Leung, V.C.M.: Innovative schemes for resource allocation in the cloud for media streaming applications. IEEE Trans. Parallel Distrib. Syst. 26(4), 1021–1033 (2015)CrossRef

15.

Amiri, M., Derakhshi, F., Reza, M., Khanli, L.: IDS fitted Q improvement using fuzzy approach for resource provisioning in cloud. J. Intell. Fuzzy Syst. 32, 1–12 (2016)

16.

Garg, S.K., Toosi, A.N., Gopalaiyengar, S.K., Buyya, R.: SLA-based virtual machine management for heterogeneous workloads in a cloud data center. J. Netw. Comput. Appl. 45, 108–120 (2014)CrossRef

17.

Jheng, J.-J., Tseng, F.-H., Chao, H.-C., Chou, L.-D.: A novel VM workload prediction using Grey forecasting model in cloud data center. In: International Conference on Information Networking, pp. 40–45, Phuket (2014)

18.

Yin, J., Lu, X., Chen, H., Zhao, X., Xiong, N.N.: System resource utilization analysis and prediction for cloud based applications under bursty workloads. Inf. Sci. 279, 338–357 (2014)CrossRef

19.

Lu, C.T., Chang, C.W., Chang, J.S.: VM scaling based on Hurst exponent and Markov transition with empirical cloud data. J. Syst. Softw. 99, 199–207 (2015)CrossRef

20.

Sheng, D., Cho Li, W., Cappello, F.: Adaptive algorithm for minimizing cloud task length with prediction errors. IEEE Trans. Cloud Comput. 2(2), 194–207 (2014)CrossRef

21.

Hu, Y., Deng, B., Peng, F., Wang, D.: Workload prediction for cloud computing elasticity mechanism. In: 2016 IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), Chengdu, pp. 244–249 (2016)

22.

Akindele, A.B., Samuel, A.A.: Predicting cloud resource provisioning using machine learning techniques. In: 2013 Proceedings of the 26th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), pp. 1–4, Vancouver (2013)

23.

Kousiouris, G., Menychtas, A., Kyriazis, D., Gogouvitis, S., Varvarigou, T.: Dynamic, behavioral-based estimation of resource provisioning based on high-level application terms in cloud platforms. Future Gener. Comput. Syst. 32, 27–40 (2014)CrossRef

24.

Manvi, S.S., Krishna Shyam, G.: Resource management for Infrastructure as a Service (IaaS) in cloud computing: a survey. J. Netw. Comput. Appl. 41, 424–440 (2014)CrossRef

25.

Lee, S., Meredith, J.S., Vetter, J.S., COMPASS: a framework for automated performance modeling and prediction. In: Proceedings of the 29th ACM on International Conference on Supercomputing, ICS’15, Newport Beach/Irvine, pp. 405–414 (2015)

26.

Larsson, T., Svensson, M.: Resource Prediction for Cloud Computing. U.S. Patent 20160380908, 29 Dec 2016 (2016)

27.

Jacobson, D.I., Altos, L., Joshi, N., Jose, S., Oberai, P., Carlos, S., Yuan, Y., Fremont, Tuffs, P.S.: Pacific grove, predictive auto scaling engine. U.S. Patent 14057898, 23 Apr 2015 (2015)

28.

Kumar, A., Sangwan, S. R., Nayyar, A.: Multimedia social big data: mining. In: Multimedia Big Data Computing for IoT Applications. Springer, Singapore, pp. 289–321 (2020)

29.

Kaur, A., Gupta, P., Singh, M., Nayyar, A.: Data placement in era of cloud computing: a survey, taxonomy and open research issues. Scalable Comput. Pract. Exp. 20(2), 377–398 (2019)CrossRef

30.

Singh, P., Gupta, P., Jyoti, K., Nayyar, A.: Research on auto-scaling of web applications in cloud: survey, trends and future directions. Scalable Comput. Pract. Exp. 20(2), 399–432 (2019)CrossRef

31.

Calheiros, R.N., Masoumi, E., Ranjan, R., Buyya, R.: Workload prediction using ARIMA model and its impact on cloud applications’ QoS. IEEE Trans. Cloud Comput. 3(4), 449–458 (2015)CrossRef

32.

Kumar, R., Kalra, M., Tanwar, S., Tyagi, S., Kumar, N.: Min-parent: an effective approach to enhance resource utilization in cloud environment. In: 2016 International Conference on Advances in Computing, Communication, & Automation (ICACCA). Springer, Dehradun, pp. 1–6 (2016)

33.

Tyagi, S., Obaidat, M.S., Tanwar, S., Kumar, N., Lal, M.: Sensor cloud based measurement to management system for precise irrigation. In: GLOBECOM 2017—2017 IEEE Global Communications Conference, Singapore, pp. 1–6 (2017)

34.

Zhao, Shenghui., Chen, Haibao., Zhao, Ruibin., Zhao, Yuyan., Chen, Guilin.: A big data processing-oriented prediction method of cloud computing service request. J. Appl. Sci. Eng. 19(4), 497–504 (2016)

35.

Prasad, V., Nair, A., Tanwar, S.: Resource Allocation in Cloud Computing, Instant Guide to Cloud Computing. BPB Publications, New Delhi, pp. 343–376 (2019)

36.

Singh, S., Chana, I.: A survey on resource scheduling in cloud computing: issues and challenges. J Grid Comput. 14(2), 217–264 (2016)CrossRef

37.

Serrano, D., Bouchenak, S., Kouki, Y., Alvares de Oliveira Jr., F., Ledoux, T., Lejeune, J., Sopena, J., Arantes, L., Sens, P.: SLA guarantees for cloud services. Future Gener. Comput. Syst. 54, 233–246 (2016)

38.

Aras, C.M., Miller, J.D., Scott, R.K.: System for allocation of network resources using an autoregressive integrated moving average method. U.S. Patent 5884037, 16 Mar 1999 (1999)

39.

Wood, D.A., Zafer, M., Zerfos, P.: Fast and automated ARIMA model initialization. U.S. Patent 14163418, 24 Jan 2014 (2014)

40.

Singh, S.P., Nayyar, A., Kumar, R., Sharma, A.: Fog computing: from architecture to edge computing and big data processing. J. Supercomput. 75(4), 2070–2105 (2019)CrossRef

41.

Nayyar, A.: Private virtual infrastructure (PVI) model for cloud computing. Int. J. Softw. Eng. Res. Pract. 1(1), 10–14 (2011)

42.

Wang, W., Yanmin, L.: Analysis of the mean absolute error (MAE) and the root mean square error (RMSE) in assessing rounding model. IOP Conf. Ser. Mater. Sci. Eng. 324, 1–11 (2018)CrossRef

43.

In Jae Myung: Tutorial on maximum likelihood estimation. J. Math. Psychol. 47(1), 90–100 (2003)MathSciNetCrossRef

44.

Kalyvianaki, E., Charalambous, T., Hand, S.: Self-adaptive and self-configured CPU resource provisioning for virtualized servers using Kalman filters. In: Proceedings of the 6th International Conference on Autonomic Computing (ICAC ‘09). ACM, New York, pp. 117–126 (2009)

45.

Chen, J., Wang, Y.: A hybrid method for short-term host utilization prediction in cloud computing. J. Electr. Comput. Eng. 1–14 (2019)

46.

https://dumps.wikimedia.org/other/pagecounts-raw. Accessed 22 Aug 2016

47.

Kumari, A., Tanwar, S., Tyagi, S., Kumar, N., Parizi, R.M., Choo, K.R.: Fog data analytics: a taxonomy and process model. J. Netw. Comput. Appl. 128, 90–104 (2019)CrossRef

48.

Fei, X., Shah, N., Verba, N., Chao, K.-M., Sanchez-Anguix, V., Lewandowski, J., James, A., Usman, Z.: CPS data streams analytics based on machine learning for cloud and fog computing: a survey. Future Gener. Comput. Syst. 90, 435–450 (2019)CrossRef

49.

Nayyar, A.: Interoperability of cloud computing with web services. Int. J. Electrocomput. World Knowl. Interface 1(1) (2011)

50.

Almohri, H.M.J., Watson, L.T., Evans, D.: Predictability of IP address allocations for cloud computing platforms. In: IEEE Transactions on Information Forensics and Security, vol. 15, pp. 500–511 (2020)

51.

Bacis, E., De Capitani di Vimercati, S., Foresti, S., Paraboschi, S., Rosa, M., Samarati, P.: Securing resources in decentralized cloud storage. In: IEEE Transactions on Information Forensics and Security, vol. 15, pp. 286–298 (2020)

52.

Kaur, A., Singh, V.P., Gill, S.S.: The future of cloud computing: opportunities, challenges and research trends: In: 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), pp. 213–219 (2019)

Title: Adaptive cloud resource management through workload prediction
Authors: Lata J. Gadhavi
Madhuri D. Bhavsar
Publication date: 19-11-2019
Publisher: Springer Berlin Heidelberg
Published in: Energy Systems / Issue 3/2022
Print ISSN: 1868-3967
Electronic ISSN: 1868-3975
DOI: https://doi.org/10.1007/s12667-019-00368-6

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Other articles of this Issue 3/2022

Experimental evaluation of adaptive maximum power point tracking for a standalone photovoltaic system

Prospect of green power generation as a solution to energy crisis in Bangladesh

Intent and permission modeling for privacy leakage detection in android

A task-based model for minimizing energy consumption in WSNs

Indian smart city ranking model using taxicab distance-based approach

Preface—special issue “Energy Efficiency in Building using Intelligent computing for Smart Cities”