Future computation of cloud datacenter resource usage is a provoking task due to dynamic and Business Critic workloads. Accurate prediction of cloud resource utilization through historical observation facilitates, effectively aligning the task with resources, estimating the capacity of a cloud server, applying intensive auto-scaling and controlling resource usage. As imprecise prediction of resources leads to either low or high provisioning of resources in the cloud. This paper focuses on solving this problem in a more proactive way. Most of the existing prediction models are based on a mono pattern of workload which is not suitable for handling peculiar workloads. The researchers address this problem by making use of a contemporary model to dynamically analyze the CPU utilization, so as to precisely estimate data center CPU utilization. The proposed design makes use of an Ensemble Random Forest-Long Short Term Memory based deep architectural models for resource estimation. This design preprocesses and trains data based on historical observation. The approach is analyzed by using a real cloud data set. The empirical interpretation depicts that the proposed design outperforms the previous approaches as it bears 30%–60% enhanced accuracy in resource utilization.
BenmakreloufSSt-OngeCKaraN, et al. (2020) Abnormal behavior detection using resource level to service level metrics mapping in virtualized systems. Future Generation Computer Systems102: 680–700.
3.
BentéjacCCsörgőAandMartinez-MunozG (2021) A comparative analysis of gradient boosting algorithms. Artificial Intelligence Review54(3): 1937–1967.
4.
BreimanL (2001) Random forests. Machine Learning45(1): 5–32.
5.
BuyyaRSriramaSNCasaleG, et al. (2018) A manifesto for future generation cloud computing: Research directions for the next decade. ACM Computing Surveys (CSUR)51(5): 1–38.
6.
CalzarossaMCDella VedovaMLMassariL, et al. (2016) Workloads in the clouds. Switzerland: Springer International Publishing.
7.
ChenZZhuYDiY, et al. (2015) Self-adaptive prediction of cloud resource demands using ensemble model and subtractive-fuzzy clustering based fuzzy neural network. Computational Intelligence and Neuroscience2015: 14.
8.
DugganMMasonKDugganJ, et al. (2017) Predicting Host CPU Utilization In Cloud Computing Using Recurrent Neural Networks. In: 2017 12th international conference for internet technology and secured transactions (ICITST), Cambridge, 11–14 December 2017, pp.67–72. New York: IEEE.
9.
JiangYPerngC-SLiT, et al. (2013) Cloud analytics for capacity planning and instant VM provisioning. IEEE Transactions on Network and Service Management10(3): 312–325.
10.
KalidossTSannasiGLakshmananS, et al. (2018) Data anonymisation of vertically partitioned data using Map Reduce techniques on cloud. International Journal of Communication Networks and Distributed Systems20(4): 519–531.
11.
KimKWangWQiY, et al. (2018) Cloud in sight: Utilizing a council of experts to predict future cloud application workloads. In: 2018 IEEE 11th international conference on cloud computing (CLOUD), San Francisco, CA, 2–7 July 2018, pp.41–48. New York: IEEE.
12.
KumarJSinghAK (2018) Workload prediction in cloud using artificial neural network and adaptive differential evolution. Future Generation Computer Systems81: 41–52.
13.
LiaoSZhangHShuG, et al. (2017) Adaptive resource prediction in the cloud using linear stacking model. In: 2017 fifth international conference on advanced cloud and big data (CBD), Shanghai, China, 13–16 August 2017, pp.33–38. New York: IEEE.
14.
MasonKDugganMBarrettE, et al. (2018) Predicting host CPU utilization in the cloud using evolutionary neural networks. Future Generation Computer Systems86: 162–173.
15.
QiuFZhangBGuoJ (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), Shanghai, China, 30 May–1 June 2016, pp.319–324. New York: IEEE.
RavichandranPDafni RoseJVijayakumarK (2020) Stock trend prediction using deep neural networks in time series and social sentiment analysis. In: Proceedings of the first international conference on advanced scientific innovation in science, engineering and technology, ICASISET, Chennai, India16–17 May 2020. EAI.
18.
SelvakumarKSaiRameshLSabenaS, et al. (2019) CLOUD COMPUTING-TMACS: a robust and verifiable threshold multi-authority access control system in public cloud storage. In: SatapathyS.BhatejaV.DasS. (eds) Smart Intelligent Computing and Applications. Smart Innovation, Systems and Technologies (Vol. 105). Singapore: Springer, pp.365–373. https://doi.org/10.1007/978-981-13-1927-3_39
19.
SinghPGuptaPJyotiK (2019) TASM: technocrat ARIMA and SVR model for workload prediction of web applications in cloud. Cluster Computing22: 619–633.
20.
SongBYuYZhouY, et al. (2018) Host load prediction with long short-term memory in cloud computing. The Journal of Supercomputing15: 6554–6568.
Van HoudtGMosqueraCNápolesG (2020) A review on the long short-term memory model. ArtifIntell53: 5929–5955.
23.
VijayakumarKArunC (2019) Continuous security assessment of cloud based applications using distributed hashing algorithm in SDLC. Cluster Computing22(5): 10789–10800.
24.
XiahouJLinFHuangQ, et al. (2018) Multi-datacenter cloud storage service selection strategy based on AHP and backward cloud generator model. Neural Computing and Applications29(1): 71–85.
25.
ZhangQYangLTYanZ, et al. (2018) An efficient deep learning model to predict cloud workload for industry informatics. IEEE Transactions On Industrial Informatics14(7): 3170–3178.
26.
ZhuYZhangWChenY, et al. (2019) A novel approach to workload prediction using attention-based LSTM encoder-decoder network in cloud environment. Wireless Com Network2019: 274.
27.
Zia UllahQHassanSKhanGM (2017) Adaptive resource utilization prediction system for infrastructure as a service cloud. Computational Intelligence Neuroscience2017: 4873459.
