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The Journal of Supercomputing

Erschienen in:

09.03.2021

Efficient resource utilization using multi-step-ahead workload prediction technique in cloud

verfasst von: Sounak Banerjee, Sarbani Roy, Sunirmal Khatua

Erschienen in: The Journal of Supercomputing | Ausgabe 9/2021

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Abstract

The demand of cloud-based services is growing rapidly due to the high scalability and cost-effective nature of cloud infrastructure. As a result, the size of the data center is increasing drastically, so is the cost of maintenance in terms of resource management and energy consumption. Hence, it is important to develop a proper resource management plan to maximize the profit by reducing the overhead of operational cost. In this paper, we propose a multi-step-ahead workload prediction approach using Machine learning techniques and allocate the resources based on this prediction in a way that allows the resources to be utilized more efficiently and thereby, reducing the data center’s overall energy consumption. We evaluate the effectiveness of our framework based on real workload trace of Bitbrains. Experimental results show that our framework outperforms other state-of-the-art approaches for predicting workload over a long-run and significantly improves resource utilization while enabling substantial energy savings.

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Ahmed NK, Atiya AF, Gayar NE, El-Shishiny H (2010) An empirical comparison of machine learning models for time series forecasting. Econom Rev 29(5–6):594–621MathSciNetCrossRef

Ajiro Y, Tanaka A (2007) Improving packing algorithms for server consolidation. In: International CMG Conference, vol 253, pp 399–406

Barford P, Crovella M (1998) Generating representative web workloads for network and server performance evaluation. In: Proceedings of the 1998 ACM SIGMETRICS Joint International Conference on Measurement and Modeling of Computer Systems, pp 151–160

Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener Comput Syst 28(5):755–768CrossRef

Beloglazov A, Buyya R (2010) Energy efficient resource management in virtualized cloud data centers. In: 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing. IEEE, pp 826–831

Beloglazov A, Buyya R (2012) 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):1366–1379CrossRef

Benson T, Anand A, Akella A, Zhang M (2011) Microte: fine grained traffic engineering for data centers. In: Proceedings of the Seventh Conference on Emerging Networking Experiments and Technologies, pp 1–12

Bey KB, Benhammadi F, Mokhtari A, Guessoum Z (2009) CPU load prediction model for distributed computing. In: 2009 Eighth International Symposium on Parallel and Distributed Computing. IEEE, pp 39–45

Borodin A, Karp R, Tardos G (1990) On the power of randomization in online algorithms. In: Proceeding of the Twenty-Second Annual ACM Symposium on Theory of Computing

10.

Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I (2009) Cloud computing and emerging it platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener Comput Syst 25(6):599–616CrossRef

11.

Chase JS, Anderson DC, Thakar PN, Vahdat AM, Doyle RP (2001) Managing energy and server resources in hosting centers. ACM SIGOPS Oper Syst Rev 35(5):103–116CrossRef

12.

Chen Y-L, Chang M-F, Chao-Wei Yu, Chen X-Z, Liang W-Y (2018) Learning-directed dynamic voltage and frequency scaling scheme with adjustable performance for single-core and multi-core embedded and mobile systems. Sensors 18(9):3068CrossRef

13.

Chen Z, Zhu Y, Di Y, Feng S (2015) Self-adaptive prediction of cloud resource demands using ensemble model and subtractive-fuzzy clustering based fuzzy neural network. Comput Intell Neurosci 2015:919805

14.

Chou J-S, Nguyen T-K (2018) Forward forecast of stock price using sliding-window metaheuristic-optimized machine-learning regression. IEEE Trans Ind Inf 14(7):3132–3142CrossRef

15.

Cook G, Lee J, Tsai T, Kong A, Deans J, Johnson B, Jardim E (2017) Clicking clean: who is winning the race to build a green internet? Greenpeace Inc., Washington, DC

16.

Dabbagh M, Hamdaoui B, Guizani M, Rayes A (2015) Energy-efficient resource allocation and provisioning framework for cloud data centers. IEEE Trans Netw Serv Manag 12(3):377–391CrossRef

17.

Dinda PA (2006) Design, implementation, and performance of an extensible toolkit for resource prediction in distributed systems. IEEE Trans Parallel Distrib Syst 17(2):160–173CrossRef

18.

Duan H, Chen C, Min G, Yu W (2017) Energy-aware scheduling of virtual machines in heterogeneous cloud computing systems. Future Gener Comput Syst 74:142–150CrossRef

19.

Feitelson DG (2002) Workload modeling for performance evaluation. In: IFIP International Symposium on Computer Performance Modeling, Measurement and Evaluation. Springer, pp 114–141

20.

Garg SK, Yeo CS, Anandasivam A, Buyya R (2011) Environment-conscious scheduling of HPC applications on distributed cloud-oriented data centers. J Parallel Distrib Comput 71(6):732–749CrossRef

21.

Hota HS, Handa R, Shrivas AK (2017) Time series data prediction using sliding window based RBF neural network. Int J Comput Intell Res 13(5):1145–1156

22.

Iranfar A, Zapater M, Atienza D (2018) Machine learning-based quality-aware power and thermal management of multistream HEVC encoding on multicore servers. IEEE Trans Parallel Distrib Syst 29(10):2268–2281CrossRef

23.

Islam S, Keung J, Lee K, Liu A (2012) Empirical prediction models for adaptive resource provisioning in the cloud. Future Gener Comput Syst 28(1):155–162CrossRef

24.

Ismaeel S, Miri A (2015) Using ELM techniques to predict data centre VM requests. In: 2015 IEEE 2nd International Conference on Cyber Security and Cloud Computing. IEEE, pp 80–86

25.

Li H (2009) Workload dynamics on clusters and grids. J Supercomput 47(1):1–20CrossRef

26.

Li M, Ganesan D, Shenoy P (2009) Presto: feedback-driven data management in sensor networks. IEEE/ACM Trans Netw 17(4):1256–1269CrossRef

27.

Łuczak M (2016) Hierarchical clustering of time series data with parametric derivative dynamic time warping. Expert Syst Appl 62:116–130CrossRef

28.

man Jr EGC, Garey MR, Johnson DS (1996) Approximation algorithms for bin packing: a survey. In: Approximation algorithms for NP-hard problems, pp 46–93

29.

Mastroianni C, Meo M, Papuzzo G (2013) Probabilistic consolidation of virtual machines in self-organizing cloud data centers. IEEE Trans Cloud Comput 1(2):215–228CrossRef

30.

Nathuji R, Schwan K (2007) Virtualpower: coordinated power management in virtualized enterprise systems. ACM SIGOPS Oper Syst Rev 41(6):265–278CrossRef

31.

Peng C, Li Y, Yu Y, Zhou Y, Du S (2018) Multi-step-ahead host load prediction with gru based encoder-decoder in cloud computing. In: 2018 10th International Conference on Knowledge and Smart Technology (KST). IEEE, pp 186–191

32.

Rodrigues PP, Gama J, Pedroso J (2008) Hierarchical clustering of time-series data streams. IEEE Trans Knowl Data Eng 20(5):615–627CrossRef

33.

Rong H, Zhang H, Xiao S, Li C, Chunhua H (2016) Optimizing energy consumption for data centers. Renew Sustain Energy Rev 58:674–691CrossRef

34.

Saini LM, Soni MK (2002) Artificial neural network-based peak load forecasting using conjugate gradient methods. IEEE Trans Power Syst 17(3):907–912CrossRef

35.

Shen S, van Beek V, Iosup A (2015) Statistical characterization of business-critical workloads hosted in cloud datacenters. In 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing. IEEE, pp 465–474

36.

Shojafar M, Cordeschi N, Amendola D, Baccarelli E (2015) Energy-saving adaptive computing and traffic engineering for real-time-service data centers. In: 2015 IEEE International Conference on Communication Workshop (ICCW). IEEE, pp 1800–1806

37.

Son J, Dastjerdi AV, Calheiros RN, Buyya R (2017) SLA-aware and energy-efficient dynamic overbooking in SDN-based cloud data centers. IEEE Trans Sustain Comput 2(2):76–89CrossRef

38.

Song B, Yao Yu, Zhou Yu, Wang Z, Sidan D (2018) Host load prediction with long short-term memory in cloud computing. J Supercomput 74(12):6554–6568CrossRef

39.

Subirats J, Guitart J (2015) Assessing and forecasting energy efficiency on cloud computing platforms. Future Gener Comput Syst 45:70–94CrossRef

40.

The SPECpower Benchmark. https://www.spec.org/power_ssj2008/results/res2020q1/power_ssj2008-20200310-01018.html/

41.

Trace description. http://gwa.ewi.tudelft.nl/datasets/gwa-t-12-bitbrains/

42.

Tran N, Reed DA (2004) Automatic ARIMA time series modeling for adaptive I/O prefetching. IEEE Trans Parallel Distrib Syst 15(4):362–377CrossRef

43.

Voorsluys W, Broberg J, Buyya R et al (2011) Introduction to cloud computing. In: Cloud computing: principles and paradigms, pp 1–44

Titel: Efficient resource utilization using multi-step-ahead workload prediction technique in cloud
verfasst von: Sounak Banerjee
Sarbani Roy
Sunirmal Khatua
Publikationsdatum: 09.03.2021
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 9/2021
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-021-03701-y

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