Abstract
Cloud computing is an extensively implemented technique to handle enormous amount of data as it provides flexibility and scalability features. In an established cloud environment, users process their request to share the data that are stored in it. Under the dynamic cloud environment, multiple requests are processed in a short time, which leads to the problem of resource allocation. Virtual Machines or servers aid the cloud in maintaining the workflow active through proper distribution of resources. However, the accurate workload prediction model is necessary for effective resource management. In the present paper, a novel BeeM-NN framework is proposed through the integration of Workload Neural Network Algorithm (WNNA) and Novel Bee Mutation Optimization Algorithm (NBMOA) for optimized workload prediction in a cloud environment. The proposed model encloses the Fitness Feature Extraction Algorithm initially to extract the feature dataset from Azure public dataset and is provided to train the WNNA. The predicted workloads are optimized with the NBMOA in the cloud. The generated model is tested using the workload data traces from the federated cloud service provider and is evaluated and compared with the existing models. The outcome showed the prediction model achieved an accuracy of 99.98% better than the current models with optimum performance in the consumption of resources and cost. The future work is to use the predicted workloads for scheduling in the cloud.
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References
Amiri M, Mohammad-Khanli L (2017) Survey on prediction models of applications for resources provisioning in cloud. J Netw Comput Appl 82:93–113
Amiri M, Feizi-Derakhshi M-R, Mohammad-Khanli L (2017) IDS fitted Q improvement using fuzzy approach for resource provisioning in cloud. J Intell Fuzzy Syst 32(1):229–240
Amjad MK, Butt SI, Kousar R, Ahmad R, Agha MH, Faping Z, Anjum N, Asgher U (2018) Recent research trends in genetic algorithm based flexible job shop scheduling problems. Math Prob Eng 2018:1–32
Arulkumar V, Bhalaji N (2020) Performance analysis of nature inspired load balancing algorithm in cloud environment. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-019-01655-x
Beloglazov A, Buyya R (2010) Energy efficient resource management in virtualized cloud data centers. In: Tenth IEEE/ACM International Conference on cluster, cloud and grid computing, pp 826–831
Github (2019), Azure/AzurePublicDataset, https://github.com/AzurePublicDataset
Gupta S, Dinesh DA (2017) Resource usage prediction of cloud workloads using deep bidirectional long short term memory networks. In: IEEE International Conference on advanced networks and telecommunications systems (ANTS), pp 1–6
Gupta S, Muthiyan N, Kumar S, Nigam A, Dinesh DA (2017) A supervised deep learning framework for proactive anomaly detection in cloud workloads. In: 14th IEEE India Council International Conference (INDICON), pp 1–6
Huang Z, Peng J, Lian H, Guo J, Qiu W (2017) Deep recurrent model for server load and performance prediction in data center. J Complex. https://doi.org/10.1155/2017/8584252
Hu R, Jiang J, Liu G, Wang L (2013) Cpu load prediction using support vector regression and kalman smoother for cloud. In: IEEE 33rd International Conference on Distributed Computing Systems Workshops, pp 88–92
Levin A, Lorenz D, Merlino G, Panarello A, Puliafito A, Tricomi G (2018) Hierarchical load balancing as a service for federated cloud networks. Comput Commun 129:125–137
Lu Y, Panneerselvam J, Liu L, Wu Y (2016) RVLBPNN: a workload forecasting model for smart cloud computing. Sci Programm. https://doi.org/10.1155/2016/5635673
Michael A, Armando F, Rean G, Joseph Anthony D, Randy K, Andy K, Gunho L, David P, Ariel R, Ion S (2010) A view of cloud computing. Commun ACM 53(4):50–58
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: IEEE Tenth International Conference on Knowledge and Smart Technology (KST), pp 186–191
Pizzuti C (2011) A multiobjective genetic algorithm to find communities in complex networks. IEEE Trans Evol Comput 16(3):418–430
Qiu F, Zhang B, Guo J (2016) A deep learning approach for vm workload prediction in the cloud. In: 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), pp 319–324
Saha O, Dasgupta P (2018) A comprehensive survey of recent trends in cloud robotics architectures and applications. Robotics 7(3):47
Sahi SK, Dhaka VS (2016) A survey paper on workload prediction requirements of cloud computing. In: Third International Conference on Computing for Sustainable Global Development (INDIACom), pp 254–258
Salehinejad H, Sankar S, Barfett J, Colak E, Valaee S (2017) Recent advances in recurrent neural networks. arXiv preprint arXiv:1801.01078
Sathappan OL, Chitra P, Venkatesh P, Prabhu M (2011) Modified genetic algorithm for multiobjective task scheduling on heterogeneous computing system. Int J Inf Technol Commun Converg 1(2):146–158
Shyam GK, Manvi SS (2016) Virtual resource prediction in cloud environment: a bayesian approach. J Netw Comput Appl 65:144–154
Song B, Yu Y, Zhou Y, Wang Z, Du S (2018) Host load prediction with long shortterm memory in cloud computing. J Supercomput 74(12):6554–6568
Tavana M, Shahdi-Pashaki S, Teymourian E, Santos-Arteaga FJ, Komaki M (2018) A discrete cuckoo optimization algorithm for consolidation in cloud computing. Comput Ind Eng 115:495–511
Xiao Z, Song W, Chen Q (2012) Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Trans Parallel Distrib Syst 24:1107–1117
Xu Y, Fan P, Yuan L (2013) A simple and efficient artificial bee colony algorithm. Math Prob Eng, pp 1–9
Yang J, Liu C, Shang Y, Cheng B, Mao Z, Liu C, Niu L, Chen J (2014) A costaware auto-scaling approach using the workload prediction in service clouds. Inf Syst Front 16(1):7–18
Zeng N, Wang Z, Zineddin B, Li Y, Du M, Xiao L, Liu X, Young T (2014) Image-based quantitative analysis of gold immunochromatographic strip via cellular neural network approach. IEEE Trans Med Imaging 33(5):1129–1136
Zeng N, Wang Z, Zineddin B, Li Y, Du M, Xiao L, Liu X, Young T (2016) A novel switching delayed PSO algorithm for estimating unknown parameters of lateral flow immunoassay. Cogn Comput 8(2):143–152
Zeng N, Wang Z, Zhang H, Kim KE, Li Y, Liu X (2019) An improved particle filter with a novel hybrid proposal distribution for quantitative analysis of gold immunochromatographic strips. IEEE Trans Nanotechnol 18:819–829
Zhou Y, Xiang Y, Chen Z, He J, Wang J (2018) A scalar projection and angle-based evolutionary algorithm for many-objective optimization problems. IEEE Trans Cybernet 49(6):2073–2084
Zhu Y, Zhang W, Chen Y, Gao H (2019) A novel approach to workload prediction using attention-based LSTM encoder-decoder network in cloud environment. EURASIP J Wirel Commun Netw 2019(1):274
Zou W, Zhu Y, Chen H, Zhang B (2011) Solving multiobjective optimization problems using artificial bee colony algorithm. Discrete Dyn Nat Soc 2011
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Shishira, S.R., Kandasamy, A. BeeM-NN: An efficient workload optimization using Bee Mutation Neural Network in federated cloud environment. J Ambient Intell Human Comput 12, 3151–3167 (2021). https://doi.org/10.1007/s12652-020-02474-1
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DOI: https://doi.org/10.1007/s12652-020-02474-1