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Open Access

Application-Oriented Cloud Workload Prediction: A Survey and New Perspectives

Key Laboratory of Embedded System and Service Computing, Ministry of Education, and also with Department of Computer Science and Technology, Tongji University, Shanghai 201804, China
Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Department of Computer Science and Technology, Tongji University, Shanghai 201804, China, and also with Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
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Abstract

Workload prediction is critical in enabling proactive resource management of cloud applications. Accurate workload prediction is valuable for cloud users and providers as it can effectively guide many practices, such as performance assurance, cost reduction, and energy consumption optimization. However, cloud workload prediction is highly challenging due to the complexity and dynamics of workloads, and various solutions have been proposed to enhance the prediction behavior. This paper aims to provide an in-depth understanding and categorization of existing solutions through extensive literature reviews. Unlike existing surveys, for the first time, we comprehensively sort out and analyze the development landscape of workload prediction from a new perspective, i.e., application-oriented rather than prediction methodologies per se. Specifically, we first introduce the basic features of workload prediction, and then analyze and categorize existing efforts based on two significant characteristics of cloud applications: variability and heterogeneity. Furthermore, we also investigate how workload prediction is applied to resource management. Finally, open research opportunities in workload prediction are highlighted to foster further advancements.

Keywords

cloud computingworkload predictionresource managementartificial intelligence for IT operations (AIOps)

References

[1]
C. Cui, B. He, C. Yu, J. Xiao, C. Li, D. Fan, S. Li, L. Mi, Z. Cao, S. Yang, et al., Astrocloud: A distributed cloud computing and application platform for astronomy, arXiv preprint arXiv:1701.05641, 2017.
Crossref
[2]

M. G. Avram, Advantages and challenges of adopting cloud computing from an enterprise perspective, Procedia Technol., vol. 12, pp. 529–534, 2014.
Crossref Google Scholar
[3]
J. Peng, X. Zhang, Z. Lei, B. Zhang, W. Zhang, and Q. Li, Comparison of several cloud computing platforms, in Proc. Second Int. Symp. on Information Science and Engineering, Shanghai, China, 2009, pp. 23–27.
Crossref
[4]

M. N. O. Sadiku, S. M. Musa, and O. D. Momoh, Cloud computing: Opportunities and challenges, IEEE Potentials, vol. 33, no. 1, pp. 34–36, 2014.
Crossref Google Scholar
[5]

J. Viega, Cloud computing and the common man, Computer, vol. 42, no. 8, pp. 106–108, 2009.
Google Scholar
[6]
B. Feng, Z. Ding, and C. Jiang, FAST: A forecasting model with adaptive sliding window and time locality integration for dynamic cloud workloads, IEEE Trans. Serv. Comput., vol. 16, no. 2, pp. 1184–1197, 2023.
Crossref
[7]

M. Masdari and A. Khoshnevis, A survey and classification of the workload forecasting methods in cloud computing, Clust. Comput., vol. 23, no. 4, pp. 2399–2424, 2020.
Crossref Google Scholar
[8]

D. Saxena, J. Kumar, A. K. Singh, and S. Schmid, Performance analysis of machine learning centered workload prediction models for cloud, IEEE Trans. Parallel Distrib. Syst., vol. 34, no. 4, pp. 1313–1330, 2023.
Crossref Google Scholar
[9]

S. Kashyap and A. Singh, Prediction-based scheduling techniques for cloud data center’s workload: a systematic review, Clust. Comput., vol. 26, no. 5, pp. 3209–3235, 2023.
Crossref Google Scholar
[10]
Google cluster trace, https://github.com/google/cluster-data, 2024.
[11]
Alibaba/clusterdata, https://github. com/alibaba/clusterdata, 2024.
[12]
AzurePublicDataset, https://github. com/Azure/AzurePublicDataset, 2024.
[13]
WS-DREAM: Towards Open Datasets and Source Code for Web Service Research, http://wsdream.github.io/, 2024.
[14]
Analytics/archive/data/pagecounts-raw, https://wikitech.wikimedia.org/w/index.php?title=Analytics/Archive/Data/Pagecounts-raw&oldid= 1912925, 2024.
[15]
A. Anwar, M. Mohamed, V. Tarasov, M. Littley, L. Rupprecht, Y. Cheng, N. Zhao, D. Skourtis, A. S. Warke, H. Ludwig et al., Improving docker registry design based on production workload analysis, in Proc. 16th USENIX Conf. File and Storage Technologies, Oakland, CA, USA, 2018, pp. 265–278.
[16]
The Grid Workloads Archive, http://gwa.ewi.tudelft.nl/, 2024.
[17]

B. Javadi, D. Kondo, A. Iosup, and D. Epema, The failure trace archive: Enabling the comparison of failure measurements and models of distributed systems, J. Parallel Distrib. Comput., vol. 73, no. 8, pp. 1208–1223, 2013.
Crossref Google Scholar
[18]

K. Park and V. S. Pai, CoMon, SIGOPS Oper. Syst. Rev., vol. 40, no. 1, pp. 65–74, 2006.
Crossref Google Scholar
[19]

D. G. Feitelson, D. Tsafrir, and D. Krakov, Experience with using the Parallel Workloads Archive, J. Parallel Distrib. Comput., vol. 74, no. 10, pp. 2967–2982, 2014.
Crossref Google Scholar
[20]

U. Lublin and D. G. Feitelson, The workload on parallel supercomputers: Modeling the characteristics of rigid jobs, J. Parallel Distrib. Comput., vol. 63, no. 11, pp. 1105–1122, 2003.
Crossref Google Scholar
[21]
C. Goble, S. Soiland-Reyes, F. Bacall, S. Owen, A.Williams, I. Eguinoa, B. Droesbeke, S. Leo, L. Pireddu, L. Rodr´ıguez-Navas, et al., Implementing fair digital objects in the eosc-life workflow collaborator, Zenodo, 2021.
[22]
X. Tang, Q. Liu, Y. Dong, J. Han, and Z. Zhang, Fisher: An efficient container load prediction model with deep neural network in clouds, in Proc. IEEE Intl. Conf. on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom), Melbourne, Australia, 2018, pp. 199–206.
Crossref
[23]

R. N. Calheiros, E. Masoumi, R. Ranjan, and R. Buyya, Workload prediction using ARIMA model and its impact on cloud applications’ QoS, IEEE Trans. Cloud Comput., vol. 3, no. 4, pp. 449–458, 2015.
Crossref Google Scholar
[24]

E. Dhib, K. Boussetta, N. Zangar, and N. Tabbane, Cost, energy, and response delay awareness-solution for cloud resources management: Proposition of a predictive dynamic algorithm for VMs allocation over a distributed cloud infrastructure, J. Ambient Intell. Humaniz. Comput., vol. 13, no. 4, pp. 2119–2129, 2022.
Crossref Google Scholar
[25]
A. Gupta and A. Kumar, Mid Term Daily Load Forecasting using ARIMA, Wavelet-ARIMA and Machine Learning, in Proc. IEEE Int. Conf. Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), Madrid, Spain, 2020, pp. 1–5.
Crossref
[26]
H. El-Kassabi, M. A. Serhani, R. Dssouli, N. Al-Qirim, and I. Taleb, Cloud workflow resource shortage prediction and fulfillment using multiple adaptation strategies, in Proc. IEEE 11th Int. Conf. Cloud Computing (CLOUD), San Francisco, CA, USA, 2018, pp. 974–977.
Crossref
[27]
C. Liu Shallow, Deep, ensemble models for network device workload forecasting, in Proc. 2020 Federated Conf. Computer Science and Information Systems, Annals of Computer Science and Information Systems, Virtual Event, 2020, pp. 101–104.
Crossref
[28]

A. Bala and I. Chana, Prediction-based proactive load balancing approach through VM migration, Eng. Comput., vol. 32, no. 4, pp. 581–592, 2016.
Crossref Google Scholar
[29]

S. U. R. Baig, W. Iqbal, J. L. Berral, A. Erradi, and D. Carrera, Adaptive prediction models for data center resources utilization estimation, IEEE Trans. Netw. Serv. Manage., vol. 16, no. 4, pp. 1681–1693, 2019.
Crossref Google Scholar
[30]
Y. Lu, J. Panneerselvam, L. Liu, and Y. Wu, RVLBPNN: A workload forecasting model for smart cloud computing, Sci. Program., vol. 2016, p. 5635673, 2016.
Crossref
[31]
S. Li, J. Bi, H. Yuan, M. Zhou, and J. Zhang, Improved LSTM-based prediction method for highly variable workload and resources in clouds, in Proc. IEEE Int. Conf. Systems, Man, and Cybernetics (SMC), Toronto, Canada, 2020, pp. 1206–1211.
Crossref
[32]
M. P. Yadav, N. Pal, and D. K. Yadav, Workload prediction over cloud server using time series data, in Proc. 11th Int. Conf. Cloud Computing, Data Science & Engineering (Confluence), Noida, India, 2021, pp. 267–272.
Crossref
[33]

Z. Ahamed, M. Khemakhem, F. Eassa, F. Alsolami, A. Basuhail, and K. Jambi, Deep reinforcement learning for workload prediction in federated cloud environments, Sensors, vol. 23, no. 15, pp. 6911, 2023.
Crossref Google Scholar
[34]

Z. Tian, S. Li, Y. Wang, and Y. Sha, A prediction method based on wavelet transform and multiple models fusion for chaotic time series, Chaos Solitons Fractals, vol. 98, pp. 158–172, 2017.
Crossref Google Scholar
[35]

S. Jeddi and S. Sharifian, A hybrid wavelet decomposer and GMDH-ELM ensemble model for Network function virtualization workload forecasting in cloud computing, Appl. Soft Comput., vol. 88, p. 105940, 2020.
Crossref Google Scholar
[36]

J. Kumar and A. K. Singh, Decomposition based cloud resource demand prediction using extreme learning machines, J. Netw. Syst. Manag., vol. 28, no. 4, pp. 1775–1793, 2020.
Crossref Google Scholar
[37]
P. Yazdanian and S. Sharifian, E2LG: A multiscale ensemble of LSTM/GAN deep learning architecture for multistep-ahead cloud workload prediction, J. Supercomput., vol. 77, no. 10, pp. 11052–11082, 2021.
Crossref
[38]

I. K. Kim, W. Wang, Y. Qi, and M. Humphrey, Forecasting cloud application workloads With CloudInsightfor predictive resource management, IEEE Trans. Cloud Comput., vol. 10, no. 3, pp. 1848–1863, 2022.
Crossref Google Scholar
[39]
J. Cao, J. Fu, M. Li, and J. Chen, CPU load prediction for cloud environment based on a dynamic ensemble model, Softw. Pract. Exp., vol. 44, no. 7, pp. 793–804, 2014.
Crossref
[40]

G. Kaur, A. Bala, and I. Chana, An intelligent regressive ensemble approach for predicting resource usage in cloud computing, J. Parallel Distrib. Comput., vol. 123, pp. 1–12, 2019.
Crossref Google Scholar
[41]

L. Von Krannichfeldt, Y. Wang, and G. Hug, Online ensemble learning for load forecasting, IEEE Trans. Power Syst., vol. 36, no. 1, pp. 545–548, 2021.
Crossref Google Scholar
[42]

K. Lalitha Devi and S. Valli, Time series-based workload prediction using the statistical hybrid model for the cloud environment, Computing, vol. 105, no. 2, pp. 353–374, 2023.
Crossref Google Scholar
[43]

L. Bao, J. Yang, Z. Zhang, W. Liu, J. Chen, and C. Wu, On accurate prediction of cloud workloads with adaptive pattern mining, J. Supercomput., vol. 79, no. 1, pp. 160–187, 2023.
Crossref Google Scholar
[44]

L. Li, M. Feng, L. Jin, S. Chen, L. Ma, and J. Gao, Domain knowledge embedding regularization neural networks for workload prediction and analysis in cloud computing, J. Inf. Technol. Res., vol. 11, no. 4, pp. 137–154, 2018.
Crossref Google Scholar
[45]
A. Gandhi, Y. Chen, D. Gmach, M. Arlitt, and M. Marwah, Minimizing data center SLA violations and power consumption via hybrid resource provisioning, in Proc. Int. Green Computing Conf. and Workshops, Orlando, FL, USA, 2011, pp. 1–8.
Crossref
[46]

K. K. D´esir´e, K. A. Francis, K. H. Kouassi, E. Dhib, N. Tabbane, and O. Asseu, “Fractional rider deep long short term memory network for workload predictionbased distributed resource allocation using spark in cloud gaming, Engineering, vol. 13, no. 03, pp. 135–157, 2021.
Crossref Google Scholar
[47]

F. Ullah, M. Bilal, and S.-K. Yoon, Intelligent time-series forecasting framework for non-linear dynamic workload and resource prediction in cloud, Comput. Netw., vol. 225, p. 109653, 2023.
Crossref Google Scholar
[48]
A. Kaim, S. Singh, and Y. S. Patel, Ensemble CNN attention-based BiLSTM deep learning architecture for multivariate cloud workload prediction, in Proc. 24th Int. Conf. Distributed Computing and Networking, Kharagpur, India, 2023, pp. 342–348.
Crossref
[49]
C. An and J. T. Zhou, Resource demand forecasting approach based on generic cloud workload model, in Proc. IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Guangzhou, China, 2018, pp. 554–563.
Crossref
[50]

X. Wang, J. Cao, D. Yang, Z. Qin, and R. Buyya, Online cloud resource prediction via scalable window waveform sampling on classified workloads, Future Gener. Comput. Syst., vol. 117, pp. 338–358, 2021.
Crossref Google Scholar
[51]

W. Matoussi and T. Hamrouni, A new temporal locality-based workload prediction approach for SaaS services in a cloud environment, J. King Saud Univ. Comput. Inf. Sci., vol. 34, no. 7, pp. 3973–3987, 2022.
Crossref Google Scholar
[52]
R. Khorsand, M. Ghobaei-Arani, and M. Ramezanpour, FAHP approach for autonomic resource provisioning of multitier applications in cloud computing environments, Softw. Pract. Exp., vol. 48, no. 12, pp. 2147–2173, 2018.
Crossref
[53]
Z. Ding, B. Feng, and C. Jiang, COIN: A container workload prediction model focusing on common and individual changes in workloads, IEEE Trans. Parallel Distrib. Syst., vol. 33, no. 12, pp. 4738–4751, 2022.
Crossref
[54]
B. Feng and Z. Ding, GROUP: An end-to-end multi-step-ahead workload prediction approach focusing on workload group behavior, in Proc. ACM Web Conf. 2023, Austin, TX, USA, 2023, pp. 3098–3108.
Crossref
[55]
Y. Li, H. Yuan, Z. Fu, X. Ma, M. Xu, and S. Wang, ELASTIC: Edge workload forecasting based on collaborative cloud-edge deep learning, in Proc. ACM Web Conf. 2023, Austin, TX, USA, 2023, pp. 3056–3066.
Crossref
[56]
C. Lee, M. Song, K. Min, E. Ha, J. Lee, and W. Kim, Optimization of cloud computing workload prediction model with domain-based feature selection method, in Proc. Int. Conf. Artificial Intelligence in Information and Communication (ICAIIC), Bali, Indonesia, 2023, pp. 868–871.
Crossref
[57]

X. Tang, Large-scale computing systems workload prediction using parallel improved LSTM neural network, IEEE Access, vol. 7, pp. 40525–40533, 2019.
Crossref Google Scholar
[58]

J. Chen, K. Li, H. Rong, K. Bilal, K. Li, and P. S. Yu, A periodicity-based parallel time series prediction algorithm in cloud computing environments, Inf. Sci. Int. J., vol. 496, no. C, pp. 506–537, 2019.
Crossref Google Scholar
[59]
J. Huang, C. Xiao, W. Wu, Y. Yin, and H. Chang, MADC: Multi-scale attention-based deep clustering for workload prediction, in Proc. IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom), New York City, NY, USA, 2021, pp. 316–323.
Crossref
[60]
D. Ruta, L. Cen, and Q. H. Vu, Deep Bi-directional LSTM networks for device workload forecasting, in Proc. 2020 Federated Conf. Computer Science and Information Systems, Annals of Computer Science and Information Systems, 2020, pp. 115–118.
Crossref
[61]

J. Kumar and A. K. Singh, Workload prediction in cloud using artificial neural network and adaptive differential evolution, Future Gener. Comput. Syst., vol. 81, no. C, pp. 41–52, 2018.
Crossref Google Scholar
[62]

J. Kumar, D. Saxena, A. K. Singh, and A. Mohan, BiPhase adaptive learning-based neural network model for cloud datacenter workload forecasting, Soft Comput. A Fusion Found. Methodol. Appl., vol. 24, no. 19, pp. 14593–14610, 2020.
Crossref Google Scholar
[63]

D. Saxena and A. K. Singh, Auto-adaptive learning-based workload forecasting in dynamic cloud environment, Int. J. Comput. Appl., vol. 44, no. 6, pp. 541–551, 2022.
Crossref Google Scholar
[64]

A. K. Singh, D. Saxena, J. Kumar, and V. Gupta, A quantum approach towards the adaptive prediction of cloud workloads, IEEE Trans. Parallel Distrib. Syst., vol. 32, no. 12, pp. 2893–2905, 2021.
Crossref Google Scholar
[65]
Y. S. Patel and J. Bedi, MAG-D: A multivariate attention network based approach for cloud workload forecasting, Future Gener. Comput. Syst., vol. 142, no. C, pp. 376–392, 2023.
Crossref
[66]
R. Karthikeyan, V. Balamurugan, R. Cyriac, and B. Sundaravadivazhagan, COSCO2: AI-augmented evolutionary algorithm based workload prediction framework for sustainable cloud data centers, Trans. Emerg. Telecommun. Technol., vol. 34, no. 1, p. e4652.2023.
Crossref
[67]
L. Nashold and R. Krishnan, Using lstm and sarima models to forecast cluster cpu usage, arXiv preprint arXiv:2007.08092, 2020.
[68]

J. Kumar, A. K. Singh, and R. Buyya, Self directed learning based workload forecasting model for cloud resource management, Inf. Sci., vol. 543, pp. 345–366, 2021.
Crossref Google Scholar
[69]

S. Ouhame, Y. Hadi, and A. Ullah, An efficient forecasting approach for resource utilization in cloud data center using CNN-LSTM model, Neural Comput. Appl., vol. 33, no. 16, pp. 10043–10055, 2021.
Crossref Google Scholar
[70]

J. Dogani, F. Khunjush, M. R. Mahmoudi, and M. Seydali, Multivariate workload and resource prediction in cloud computing using CNN and GRU by attention mechanism, J. Supercomput., vol. 79, no. 3, pp. 3437–3470, 2023.
Crossref Google Scholar
[71]

L. Zhang, Y. Xie, M. Jin, P. Zhou, G. Xu, Y. Wu, D. Feng, and D. Long, A novel hybrid model for docker container workload prediction, IEEE Trans. Netw. Serv. Manag., vol. 20, no. 3, pp. 2726–2743, 2023.
Crossref Google Scholar
[72]

J. Chen and Y. Wang, An adaptive short-term prediction algorithm for resource demands in cloud computing, IEEE Access, vol. 8, pp. 53915–53930, 2020.
Crossref Google Scholar
[73]

Y. Xie, M. Jin, Z. Zou, G. Xu, D. Feng, W. Liu, and D. Long, Real-time prediction of docker container resource load based on a hybrid model of ARIMA and triple exponential smoothing, IEEE Trans. Cloud Comput., vol. 10, no. 2, pp. 1386–1401, 2022.
Crossref Google Scholar
[74]
R. B. Roy, T. Patel, and D. Tiwari, IceBreaker: Warming serverless functions better with heterogeneity, in Proc. 27th ACM Int. Conf. Architectural Support for Programming Languages and Operating Systems, Lausanne, Switzerland, 2022, pp. 753–767.
Crossref
[75]
A. Bhattacharjee, A. D. Chhokra, Z. Kang, H. Sun, A. Gokhale, and G. Karsai, BARISTA: Efficient and scalable serverless serving system for deep learning prediction services, in Proc. IEEE Int. Conf. Cloud Engineering (IC2E), Prague, Czech Republic, 2019, pp. 23–33.
Crossref
[76]
L. Zhao, Y. Yang, Y. Li, X. Zhou, and K. Li, Understanding, predicting and scheduling serverless workloads under partial interference, in Proc. Int. Conf. for High Performance Computing, Networking, Storage and Analysis, St. Louis, MS, USA, 2021.
Crossref
[77]
J. Wei and M. Gao, Workload prediction of serverless computing, in Proc. 2021 5th Int. Conf. Deep Learning Technologies (ICDLT), Qingdao, China, 2021, pp. 93–99.
Crossref
[78]
R. B. Roy, T. Patel, and D. Tiwari, DayDream: Executing dynamic scientific workflows on serverless platforms with hot starts, in Proc. SC22 : Int. Conf. for High Performance Computing, Networking, Storage and Analysis, Dallas, TX, USA, 2022, pp. 1–18.
Crossref
[79]

O. Poppe, Q. Guo, W. Lang, P. Arora, M. Oslake, S. Xu, and A. Kalhan, Moneyball, Proc. VLDB Endow., vol. 15, no. 6, pp. 1279–1287, 2022.
Crossref Google Scholar
[80]

R. F. da Silva, G. Juve, M. Rynge, E. Deelman, and M. Livny, Online task resource consumption prediction for scientific workflows, Parallel Process. Lett., vol. 25, no. 3, p. 1541003, 2015.
Crossref Google Scholar
[81]
M. Tanash, B. Dunn, D. Andresen, W. Hsu, H. Yang, and A. Okanlawon, Improving HPC system performance by predicting job resources via supervised machine learning, in Proc. Practice and Experience in Advanced Research Computing on Rise of the Machines (Learning ), Chicago, IL, USA, 2019, pp. 1–8.
Crossref
[82]
M. N. Newaz and M. A. Mollah, Memory usage prediction of HPC workloads using feature engineering and machine learning, in Proc. Int. Conf. High Performance Computing in Asia-Pacific Region, Singapore, 2023, pp. 64–74.
Crossref
[83]
D. Burrell, X. Chatziliadis, E. T. Zacharatou, S. Zeuch, and V. Markl, Workload prediction for iot data management systems, BTW 2023, 2023.
[84]

L. Ruan, Y. Bai, S. Li, S. He, and L. Xiao, Workload time series prediction in storage systems: a deep learning based approach, Clust. Comput., vol. 26, no. 1, pp. 25–35, 2023.
Crossref Google Scholar
[85]
J. Li, H. Xu, Y. Zhu, Z. Liu, C. Guo, and C. Wang, Lyra: Elastic scheduling for deep learning clusters, in Proc. Eighteenth European Conf. Computer Systems, Rome Italy, 2023, pp. 835–850.
Crossref
[86]
R. Gu, Y. Chen, S. Liu, H. Dai, G. Chen, K. Zhang, Y. Che, and Y. Huang, Liquid: Intelligent resource estimation and network-efficient scheduling for deep learning jobs on distributed GPU clusters, IEEE Trans. Parallel Distrib. Syst., p. 1, 2021.
Crossref
[87]

R. Liu, W. Sun, and W. Hu, Workload based geo-distributed data center planning in fast developing economies, IEEE Access, vol. 8, pp. 224269–224282, 2020.
Crossref Google Scholar
[88]

G. Andreadis, F. Mastenbroek, V. van Beek, and A. Iosup, Capelin: Data-driven compute capacity procurement for cloud datacenters using portfolios of scenarios, IEEE Trans. Parallel Distrib. Syst., vol. 33, no. 1, pp. 26–39, 2022.
Crossref Google Scholar
[89]
A. Newell, D. Skarlatos, J. Fan, P. Kumar, M. Khutornenko, M. Pundir, Y. Zhang, M. Zhang, Y. Liu, L. Le, et al., RAS: Continuously optimized region-wide datacenter resource allocation, in Proc. ACM SIGOPS 28th Symp. on Operating Systems Principles, Virtual Event, Germany, 2021, pp. 505–520.
[90]
T. N. Le, Z. Liu, Y. Chen, and C. Bash, Joint capacity planning and operational management for sustainable data centers and demand response, in Proc. Seventh Int. Conf. on Future Energy Systems, New York, NY, USA, 2016, pp. 1–12.
Crossref
[91]
Z. Zhong, J. He, M. A. Rodriguez, S. Erfani, R. Kotagiri, and R. Buyya, Heterogeneous task co-location in containerized cloud computing environments, in Proc. IEEE 23rd Int. Symp. on Real-Time Distributed Computing (ISORC), Nashville, TN, USA, 2020, pp. 79–88.
Crossref
[92]
K. Ray, A. Banerjee, and N. C. Narendra, Proactive microservice placement and migration for mobile edge computing, in Proc. IEEE/ACM Symp. on Edge Computing (SEC), San Jose, CA, USA, 2020, pp. 28–41
Crossref
[93]

L. Li, D. Shi, R. Hou, R. Chen, B. Lin, and M. Pan, Energy-efficient proactive caching for adaptive video streaming via data-driven optimization, IEEE Internet Things J., vol. 7, no. 6, pp. 5549–5561, 2020.
Crossref Google Scholar
[94]

H. Bae and J. Park, Proactive service caching in a MEC system by using spatio-temporal correlation among MEC servers, Appl. Sci., vol. 13, no. 22, p. 12509, 2023.
Crossref Google Scholar
[95]

M. Kumar, A. Kishor, J. K. Samariya, and A. Y. Zomaya, An autonomic workload prediction and resource allocation framework for fog-enabled industrial IoT, IEEE Internet Things J., vol. 10, no. 11, pp. 9513–9522, 2023.
Crossref Google Scholar
[96]

X. Tang, Y. Liu, T. Deng, Z. Zeng, H. Huang, Q. Wei, X. Li, and L. Yang, A job scheduling algorithm based on parallel workload prediction on computational grid, J. Parallel Distrib. Comput., vol. 171, no. C, pp. 88–97, 2023.
Crossref Google Scholar
[97]
M. Niknafs, P. Eles, and Z. Peng, Runtime resource management with multiple-step-ahead workload prediction, ACM Trans. Embed. Comput. Syst., vol. 22, no. 4, p. 71,
Crossref
[98]
J. Das, S. Ghosh, S. K. Ghosh, and R. Buyya, LYRIC: Deadline and budget aware spatio-temporal query processing in cloud, IEEE Trans. Serv. Comput., vol. 15, no. 5, pp. 2869–2882, 2022.
Crossref
[99]

B. Fei, X. Zhu, D. Liu, J. Chen, W. Bao, and L. Liu, Elastic resource provisioning using data clustering in cloud service platform, IEEE Trans. Serv. Comput., vol. 15, no. 3, pp. 1578–1591, 2022.
Crossref Google Scholar
[100]

T. A. L. Genez, L. F. Bittencourt, N. L. S. da Fonseca, and E. R. M. Madeira, Estimation of the available bandwidth in inter-cloud links for task scheduling in hybrid clouds, IEEE Trans. Cloud Comput., vol. 7, no. 1, pp. 62–74, 2019.
Crossref Google Scholar
[101]

S. Wang, Z. Ding, and C. Jiang, Elastic scheduling for microservice applications in clouds, IEEE Trans. Parallel Distrib. Syst., vol. 32, no. 1, pp. 98–115, 2021.
Crossref Google Scholar
[102]
Z. Wen, Y. Wang, and F. Liu, StepConf: SLO-aware dynamic resource configuration for serverless function workflows, in Proc. IEEE INFOCOM 2022 - IEEE Conf. Computer Communications, London, UK, 2022, pp. 1868–1877.
Crossref
[103]
G. Safaryan, A. Jindal, M. Chadha, and M. Gerndt, SLAM: SLO-aware memory optimization for serverless applications, in Proc. IEEE 15th Int. Conf. Cloud Computing (CLOUD), Barcelona, Spain, 2022, pp. 30–39.
Crossref
[104]
B. Feng, Z. Ding, X. Zhou, and C. Jiang, Heterogeneity-aware proactive elastic resource allocation for serverless applications, IEEE Trans. Serv. Comput., pp. 1–14, 2024.
Crossref
[105]

E. F. Coutinho, F. R. de Carvalho Sousa, P. A. L. Rego, D. G. Gomes, and J. N. de Souza, Elasticity in cloud computing: A survey, Ann. Telecommun. Ann. Des Télécommunications, vol. 70, no. 7, pp. 289–309, 2015.
Crossref Google Scholar
[106]
S. T. Singh, M. Tiwari, and A. S. Dhar, Machine learning based workload prediction for auto-scaling cloud applications, in Proc. OPJU Int. Technology Conf. Emerging Technologies for Sustainable Development (OTCON), Raigarh, India, 2023, pp. 1–6.
Crossref
[107]

M. Abdullah, W. Iqbal, J. L. Berral, J. Polo, and D. Carrera, Burst-aware predictive autoscaling for containerized microservices, IEEE Trans. Serv. Comput., vol. 15, no. 3, pp. 1448–1460, 2022.
Crossref Google Scholar
[108]

M. A. Razzaq, J. A. Mahar, M. Ahmad, N. Saher, A. Mehmood, and G. S. Choi, Hybrid auto-scaled service-cloud-based predictive workload modeling and analysis for smart campus system, IEEE Access, vol. 9, pp. 42081–42089, 2021.
Crossref Google Scholar
[109]

A. Zhao, Q. Huang, Y. Huang, L. Zou, Z. Chen, and J. Song, Research on resource prediction model based on Kubernetes container auto-scaling technology, IOP Conf. Ser.: Mater. Sci. Eng., vol. 569, no. 5, p. 052092, 2019.
Crossref Google Scholar
[110]
M. Yan, X. Liang, Z. Lu, J. Wu, and W. Zhang, HANSEL: Adaptive horizontal scaling of microservices using Bi-LSTM, Appl. Soft Comput., vol. 105, p. 107216, 2021.
Crossref
[111]

W. Iqbal, A. Erradi, and A. Mahmood, Dynamic workload patterns prediction for proactive auto-scaling of web applications, J. Netw. Comput. Appl., vol. 124, pp. 94–107, 2018.
Crossref Google Scholar
[112]

A. Ali Khan, M. Zakarya, I. U. Rahman, R. Khan, and R. Buyya, HeporCloud: An energy and performance efficient resource orchestrator for hybrid heterogeneous cloud computing environments, J. Netw. Comput. Appl., vol. 173, p. 102869, 2021.
Crossref Google Scholar
[113]
J. Liu, S. Wang, A. Zhou, J. Xu, and F. Yang, SLA-driven container consolidation with usage prediction for green cloud computing, Front. Comput. Sci. Sel. Publ. Chin. Univ., vol. 14, no. 1, pp. 42–52, 2020.
Crossref
[114]
P. Tamilarasi and D. Akila, Prediction based load balancing and VM migration in big data cloud environment, in Proc. 2nd Int. Conf. Computation, Automation and Knowledge Management (ICCAKM), Dubai, United Arab Emirates, 2021, pp. 123–127.
Crossref
[115]

N. K. Biswas, S. Banerjee, U. Biswas, and U. Ghosh, An approach towards development of new linear regression prediction model for reduced energy consumption and SLA violation in the domain of green cloud computing, Sustain. Energy Technol. Assess., vol. 45, p. 101087, 2021.
Crossref Google Scholar
[116]
J. Zeng, D. Ding, X. K. Kang, H. Xie, and Q. Yin, Adaptive DRL-based virtual machine consolidation in energy-efficient cloud data center, IEEE Trans. Parallel Distrib. Syst., p. 1, 2022.
Crossref
[117]

R. Pushpalatha and B. Ramesh, Workload prediction based virtual machine migration and optimal switching strategy for cloud power management, Wirel. Pers. Commun., vol. 123, no. 1, pp. 761–784, 2022.
Crossref Google Scholar
Tsinghua Science and Technology
Volume 30 Issue 1,
February 2025
Pages 34-54
DOI: 10.26599/TST.2024.9010024
Cite this article:
Feng B, Ding Z. Application-Oriented Cloud Workload Prediction: A Survey and New Perspectives. Tsinghua Science and Technology, 2025, 30(1): 34-54. https://doi.org/10.26599/TST.2024.9010024

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Received: 25 October 2023
Revised: 19 January 2024
Accepted: 24 January 2024
Published: 11 September 2024
© The Author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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