research-article

A survey and classification of the workload forecasting methods in cloud computing

Authors:

Mohammad Masdari,

Afsane KhoshnevisAuthors Info & Claims

Cluster Computing, Volume 23, Issue 4

Pages 2399 - 2424

https://doi.org/10.1007/s10586-019-03010-3

Published: 01 December 2020 Publication History

Abstract

Workload prediction is one of the important parts of proactive resource management and auto-scaling in cloud computing. Accurate prediction of workload in cloud computing is of high importance for improving cloud performance, mitigate energy consumptions, meeting the required quality of service (QoS) level, predicting the energy consumption of data centers (DCs), and improving the cloud service providers’ scalability. However, in cloud computing context workload prediction is a challenging issue and various schemes using machine learning, data mining, and mathematical methods to deal with this issue. This scheme presents an extensive literature review of the workload prediction schemes proposed in the literature to improve resource management in the cloud DCs. It first provides the required knowledge regarding the workload prediction context and presents a taxonomy of the workload prediction schemes according to their applied prediction algorithm. Moreover, the main contributions of these schemes are illustrated and their major advantages and limitation are specified. At last, the open research opportunities in the workload prediction field are focused and the concluding remarks are presented.

References

[1]

Masdari M, ValiKardan S, Shahi Z, and Azar SI Towards workflow scheduling in cloud computing: a comprehensive analysis J. Netw. Comput. Appl. 2016 66 64-82

[2]

Masdari M, Nabavi SS, and Ahmadi V An overview of virtual machine placement schemes in cloud computing J. Netw. Comput. Appl. 2016 66 106-127

[3]

González-Martínez JA, Bote-Lorenzo ML, Gómez-Sánchez E, and Cano-Parra R Cloud computing and education: a state-of-the-art survey Comput. Educ. 2015 80 132-151

[4]

Masdari M, Salehi F, Jalali M, and Bidaki M A survey of PSO-based scheduling algorithms in cloud computing J. Netw. Syst. Manage. 2017 25 1 122-158

[5]

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

[6]

Coutinho EF, de Carvalho Sousa FR, Rego PAL, Gomes DG, and de Souza JN Elasticity in cloud computing: a survey Ann. Telecommun. 2015 70 7–8 289-309

[7]

Lorido-Botran T, Miguel-Alonso J, and Lozano JA A review of auto-scaling techniques for elastic applications in cloud environments J. Grid Comput. 2014 12 4 559-592

[8]

Amiri M and Mohammad-Khanli L Survey on prediction models of applications for resources provisioning in cloud J. Netw. Comput. Appl. 2017 82 93-113

[9]

Singh S and Chana I QoS-aware autonomic resource management in cloud computing: a systematic review ACM Comput. Surv. (CSUR) 2016 48 3 42

[10]

Kaur T and Chana I Energy efficiency techniques in cloud computing: a survey and taxonomy ACM Comput. Surv. (CSUR) 2015 48 2 22

[11]

Dougherty B, White J, and Schmidt DC Model-driven auto-scaling of green cloud computing infrastructure Future Gener. Comput. Syst. 2012 28 2 371-378

[12]

Qu C, Calheiros RN, and Buyya R Auto-scaling web applications in clouds: a taxonomy and survey ACM Comput. Surv. (CSUR) 2018 51 4 73

[13]

Netto MA, Cardonha C, Cunha RL, Assuncao MD (2014) Evaluating auto-scaling strategies for cloud computing environments. In 2014 IEEE 22nd International Symposium on Modelling, Analysis & Simulation of Computer and Telecommunication Systems, IEEE, p 187–196

[14]

de Assunção MD, Cardonha CH, Netto MA, and Cunha RL Impact of user patience on auto-scaling resource capacity for cloud services Future Gener. Comput. Syst. 2016 55 41-50

[15]

Qu C, Calheiros RN, and Buyya R Auto-scaling web applications in clouds: a taxonomy and survey ACM Comput. Surv. 2016 51 4 73

[16]

Turowski M, Lenk A (2015) Vertical scaling capability of OpenStack. Service-Oriented Computing-ICSOC 2014 Workshops. Springer, Cham, p 351–362

[17]

Cai Z, Li Q, and Li X Elasticsim: a toolkit for simulating workflows with cloud resource runtime auto-scaling and stochastic task execution times J. Grid Comput. 2017 15 2 257-272

[18]

Armant V, De Cauwer M, Brown KN, and O’Sullivan B Semi-online task assignment policies for workload consolidation in cloud computing systems Future Gener. Comput. Syst. 2018 82 89-103

[19]

Kardani‐Moghaddam S, Buyya R, and Ramamohanarao K Performance anomaly detection using isolation‐trees in heterogeneous workloads of web applications in computing clouds Concurr. Comput. 2019

[20]

Bajaj S (2018) Current drift in energy efficiency cloud computing: new provocations, workload prediction, consolidation, and resource over commitment. In critical research on Scalability and security issues in virtual cloud environments: IGI Global, Pennsylvania, p 283–303

[21]

Li L, Feng M, Jin L, Chen S, Ma L, and Gao J Domain knowledge embedding regularization neural networks for workload prediction and analysis in cloud computing J. Inf. Technol. Res. (JITR) 2018 11 4 137-154

[22]

Guo M, Guan Q, and Ke W Optimal scheduling of VMs in queueing cloud computing systems with a heterogeneous workload IEEE Access 2018 6 15178-15191

[23]

Pagán J, Zapater M, and Ayala JL Power transmission and workload balancing policies in eHealth mobile cloud computing scenarios Future Gener. Comput. Syst. 2018 78 587-601

[24]

Deng R, Lu R, Lai C, Luan TH, and Liang H Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption IEEE Internet Things J. 2016 3 6 1171-1181

[25]

Zhong C and Yuan X Intelligent elastic scheduling algorithms for PaaS cloud platform based on load prediction 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC) 2019 New Jersey IEEE 1500-1503

[26]

Youssef F, El Habib BL, Hamza R, El Houssine L, Ahmed E, and Hanoune M A new conception of load balancing in cloud computing using tasks classification levels Int. J. Cloud Appl. Comput. (IJCAC) 2018 8 4 118-133

[27]

Stergiou C, Psannis KE, Kim B-G, and Gupta B Secure integration of IoT and cloud computing Future Gener. Comput. Syst. 2018 78 964-975

[28]

Stergiou C, Psannis KE, Gupta BB, and Ishibashi Y Security, privacy & efficiency of sustainable cloud computing for big data & IoT Sustain. Comput. 2018 19 174-184

[29]

Sonkar S and Kharat M Load prediction analysis based on virtual machine execution time using optimal sequencing algorithm in cloud federated environment Int. J. Inf. Technol. 2019 11 2 265-275

[30]

Singh P, Gupta P, and Jyoti K Tasm: technocrat arima and svr model for workload prediction of web applications in cloud Clus. Comput. 2019 22 2 619-633

[31]

Sharma P, Sengupta J, and Suri P Survey of intrusion detection techniques and architectures in cloud computing IJHPCN 2019 13 2 184-198

[32]

Rahhali H and Hanoune M A new conception of load balancing in cloud computing using Hybrid heuristic algorithm Int. J. Comput. Sci. Issues (IJCSI) 2018 15 6 1-8

[33]

Qaddoum KS, El Emam NN, and Abualhaj MA Elastic neural network method for load prediction in cloud computing grid Int. J. Electr. Comput. Eng. 2019 9 2 1201

[34]

Prassanna J, Venkataraman N Adaptive regressive holt–winters workload prediction and firefly optimized lottery scheduling for load balancing in cloud. Wireless Networks

[35]

Patel D, Gupta RK, and Pateriya R Energy-aware prediction-based load balancing approach with VM migration for the cloud environment. Data engineering and applications 2019 Singapore Springer 59-74

[36]

Nguyen HM, Kalra G, and Kim D Host load prediction in cloud computing using long short-term memory encoder–decoder J. Supercomput. 2019

[37]

Nguyen HM, Kalra G, Jun TJ, Woo S, and Kim D ESNemble: an echo state network-based ensemble for workload prediction and resource allocation of Web applications in the cloud J. Supercomput. 2019 75 10 6303-6323

[38]

Li L, Wang Y, Jin L, Zhang X, and Qin H Two-stage adaptive classification cloud workload prediction based on neural networks Int. J. Grid High Perform. Comput. (IJGHPC) 2019 11 2 1-23

[39]

Kirchoff DF, Xavier M, Mastella J, De Rose CA (2019) A preliminary study of machine learning workload prediction techniques for cloud applications. In 2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). IEEE, New Jersey, p 222–227

[40]

Gupta B, Agrawal DP, and Yamaguchi S Handbook of research on modern cryptographic solutions for computer and cyber security 2016 Pennsylvania IGI global

[41]

Bhagavathiperumal S and Goyal M Dynamic provisioning of cloud resources based on workload prediction. Computing and Network Sustainability. 2019 Singapore Springer 41-49

[42]

Amiri M, Mohammad-Khanli L, and Mirandola R A new efficient approach for extracting the closed episodes for workload prediction in cloud Computing 2019

[43]

Zhang H, Jiang G, Yoshihira K, Chen H, and Saxena A Intelligent workload factoring for a hybrid cloud computing model 2009 Congress on Services-I 2009 New Jersey IEEE 701-708

[44]

Di S, Wang CL (2013) Minimization of cloud task execution length with workload prediction errors. In 20th Annual International Conference on High Performance Computing. IEEE, New Jersey, p 69–78

[45]

Khoshkbarforoushha A, Ranjan R, Gaire R, Abbasnejad E, Wang L, and Zomaya AY Distribution based workload modelling of continuous queries in clouds IEEE Trans. Emerg. Topics Comput. 2017 5 1 120-133

[46]

Wang P, Fang W, Guo B, Bao H (2017) Apply petri nets to human performance and workload prediction under multitask. In International Conference on Applied Human Factors and Ergonomics. Springer, Cham. p 395–405

[47]

Reeba PJ, Shaji R, Jayan J (2016) A secure virtual machine migration using processor workload prediction method for cloud environment. In 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), IEEE, p 1–6

[48]

Baldan FJ, Ramirez-Gallego S, Bergmeir C, Benitez-Sanchez JM, and Herrera F A forecasting methodology for workload forecasting in cloud systems IEEE Trans. Cloud Comput. 2016 6 4 929-941

[49]

Singh N, Rao S (2012) Online ensemble learning approach for server workload prediction in large datacenters. In 2012 11th International Conference on Machine Learning and Applications. IEEE, p 68–71

[50]

Hagan MT and Behr SM The time series approach to short term load forecasting IEEE Trans. Power Syst. 1987 2 3 785-791

[51]

Babu KR and Samuel P Interference aware prediction mechanism for auto scaling in cloud Comput. Electr. Eng. 2017 69 351-363

[52]

Antonescu A-F and Braun T Simulation of SLA-based VM-scaling algorithms for cloud-distributed applications Future Gener. Comput. Syst. 2016 54 260-273

[53]

Yang J, Liu C, Shang Y, Mao Z, Chen J (2013) Workload predicting-based automatic scaling in service clouds. In Cloud Computing (CLOUD), 2013 IEEE Sixth International Conference on IEEE. p 810–815

[54]

Bao J, Lu Z, Wu J, Zhang S, Zhong Y (2014) Implementing a novel load-aware auto scale scheme for private cloud resource management platform. In Network Operations and Management Symposium (NOMS), 2014 IEEE. IEEE, p 1–4

[55]

Khorsand R, Ghobaei-Arani M, and Ramezanpour M WITHDRAWN: a fuzzy auto-scaling approach using workload prediction for MMOG application in a cloud environment 2018 Amsterdam Elsevier

[56]

Li S, Wang Y, Qiu X, Wang D, Wang L (2013) A workload prediction-based multi-vm provisioning mechanism in cloud computing. In 2013 15th Asia-Pacific Network Operations and Management Symposium (APNOMS). IEEE, p 1–6

[57]

Kumar AS, Mazumdar S (2016) Forecasting HPC workload using ARMA models and SSA. In 2016 International Conference on Information Technology (ICIT). IEEE, p 294–297

[58]

Calheiros RN, Masoumi E, Ranjan R, and Buyya R Workload prediction using ARIMA model and its impact on cloud applications’ QoS IEEE Trans. Cloud Comput. 2015 3 4 449-458

[59]

Messias VR, Estrella JC, Ehlers R, Santana MJ, Santana RC, and Reiff-Marganiec S Combining time series prediction models using genetic algorithm to autoscaling Web applications hosted in the cloud infrastructure Neural Comput. Appl. 2016 27 8 2383-2406

[60]

Barati M and Sharifian S A hybrid heuristic-based tuned support vector regression model for cloud load prediction J. Supercomput. 2015 71 11 4235-4259

[61]

Raghunath BR and Annappa B Virtual machine migration triggering using application workload prediction Procedia Comput. Sci. 2015 54 167-176

[62]

Tong JJ, Hai-hong E, Song MN, and Song JD Host load prediction in cloud based on classification methods J. China Univ. Posts Telecommun. 2014 21 4 40-46

[63]

Zhong W, Zhuang Y, Sun J, and Gu J A load prediction model for cloud computing using PSO-based weighted wavelet support vector machine Appl. Intell. 2018 48 11 4072-4083

[64]

Nikravesh AY, Ajila SA, Lung CH (2015) Towards an autonomic auto-scaling prediction system for cloud resource provisioning. In Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems. IEEE Press, p 35–45

[65]

Cetinski K and Juric MB AME-WPC: advanced model for efficient workload prediction in the cloud J. Netw. Compu. Appl. 2015 55 191-201

[66]

Nehru EI, Venkatalakshmi B, Balalcrishnant R, Nithya R (2013) Neural load prediction technique for power optimization in cloud management system. In 2013 IEEE Conference on Information & Communication Technologies. IEEE, p 541–544

[67]

Nguyen HM, Woo S, Im J, Jun T, Kim D (2016) A workload prediction approach using models stacking based on recurrent neural network and autoencoder. In 2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS). IEEE, p 929–936

[68]

Wamba GM, Li Y, Orgerie AC, Beldiceanu N, Menaud JM (2017) Cloud workload prediction and generation models. In 2017 29th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD). IEEE, p 89–96

[69]

Yu Y, Jindal V, Yen IL, Bastani F (2016) Integrating clustering and learning for improved workload prediction in the cloud. In 2016 IEEE 9th International Conference on Cloud Computing (CLOUD). IEEE, p 876–879

[70]

Song B, Yu Y, Zhou Y, Wang Z, and Du S Host load prediction with long short-term memory in cloud computing J. Supercomput. 2018 74 12 6554-6568

[71]

Imam MT, Miskhat SF, Rahman RM, Amin MA (2011) Neural network and regression based processor load prediction for efficient scaling of Grid and Cloud resources. In 14th International Conference on Computer and Information Technology (ICCIT 2011). IEEE, p 333–338

[72]

Yang Q, Zhou Y, Yu Y, Yuan J, Xing X, and Du S Multi-step-ahead host load prediction using autoencoder and echo state networks in cloud computing J. Supercomput. 2015 71 8 3037-3053

[73]

Kumar J and Singh AK Workload prediction in cloud using artificial neural network and adaptive differential evolution Future Gener. Comput. Syst. 2018 81 41-52

[74]

Lu Y, Panneerselvam J, and Liu L Wu Y (2016) Rvlbpnn: a workload forecasting model for smart cloud computing Sci. Prog. 2016 2016 9

[75]

Zhou X et al. Load balancing prediction method of cloud storage based on analytic hierarchy process and hybrid hierarchical genetic algorithm SpringerPlus 2016 5 1 1989

[76]

Kousiouris G, Cucinotta T, and Varvarigou T The effects of scheduling, workload type and consolidation scenarios on virtual machine performance and their prediction through optimized artificial neural networks J. Syst. Softw. 2011 84 8 1270-1291

[77]

Ramezani F, Naderpour M (2017) A fuzzy virtual machine workload prediction method for cloud environments. In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, p 1–6

[78]

Yang J et al.A cost-aware auto-scaling approach using the workload prediction in service cloudsInf. Syst. Front.20141617-181391365

[79]

Liu B, Lin Y, Chen Y (2016) Quantitative workload analysis and prediction using Google cluster traces. In Computer Communications Workshops (INFOCOM WKSHPS), 2016 IEEE Conference on, 2016. p 935–940

[80]

Di S, Kondo D, Cirne W (2012) Host load prediction in a Google compute cloud with a Bayesian model. In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis. IEEE Computer Society Press, p. 21

[81]

Dietrich B, Nunna S, Goswami D, Chakraborty S, Gries M (2010) LMS-based low-complexity game workload prediction for DVFS. In 2010 IEEE International Conference on Computer Design. IEEE, p 417–424

[82]

Tian C et al. Minimizing content reorganization and tolerating imperfect workload prediction for cloud-based video-on-demand services IEEE Trans. Serv. Comput. 2016 9 6 926-939

[83]

Patel YS and Misra R Performance comparison of deep VM workload prediction approaches for cloud. In Progress in Computing, Analytics and Networking 2018 Singapore Springer 149-160

[84]

Gupta S, Dinesh DA (2017) Resource usage prediction of cloud workloads using deep bidirectional long short term memory networks. In 2017 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS). IEEE, p 1–6

[85]

Zhang Q, Yang LT, Yan Z, Chen Z, and Li P An efficient deep learning model to predict cloud workload for industry informatics IEEE Trans. Industr. Inf. 2018 14 7 3170-3178

[86]

Gong Z, Gu X, Wilkes J (2010) Press: predictive elastic resource scaling for cloud systems. In Network and Service Management (CNSM), 2010 International Conference on, 2010. p 9–16

[87]

Jv BB and Dharma D HAS: hybrid auto-scaler for resource scaling in cloud environment J. Parallel Distrib. Comput. 2018 120 1-15

[88]

Panneerselvam J, Liu L, Antonopoulos N, Bo Y (2014) Workload analysis for the scope of user demand prediction model evaluations in cloud environments. In Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing. IEEE Computer Society, p 883–889

[89]

Pacheco-Sanchez S, Casale G, Scotney B, McClean S, Parr G, Dawson S (2011) Markovian workload characterization for qos prediction in the cloud. In 2011 IEEE 4th International Conference on Cloud Computing. p 147–154

[90]

Shen Z, Subbiah S, Gu X, Wilkes J (2011) Cloudscale: elastic resource scaling for multi-tenant cloud systems. In Proceedings of the 2nd ACM Symposium on Cloud Computing. p. 5

[91]

Chen X Decentralized computation offloading game for mobile cloud computing IEEE Trans. Parallel Distrib. Syst. 2014 26 4 974-983

[92]

Khan A, Yan X, Tao S, Anerousis N (2012) Workload characterization and prediction in the cloud: A multiple time series approach. In 2012 IEEE Network Operations and Management Symposium. p 1287–1294

[93]

Guo Y, Stolyar A, Walid A (2018) Online vm auto-scaling algorithms for application hosting in a cloud. IEEE Transactions on Cloud Computing

[94]

Gandhi A, Dube P, Karve A, Kochut A, and Zhang L Model-driven optimal resource scaling in cloud Softw. Syst. Model. 2017 17 2 509-526

[95]

Vondra T and Šedivý J Cloud autoscaling simulation based on queueing network model Simul. Model. Pract. Theory 2017 70 83-100

[96]

Sahni J and Vidyarthi DPHeterogeneity-aware adaptive auto-scaling heuristic for improved QoS and resource usage in cloud environmentsComputing2017994351-3813627440

[97]

Jiang J, Lu J, Zhang G, Long G (2013) Optimal cloud resource auto-scaling for web applications. In Cluster, Cloud and Grid Computing (CCGrid), 2013 13th IEEE/ACM International Symposium on, 2013. p 58–65

[98]

Jheng JJ, Tseng FH, Chao HC, Chou LD (2014) A novel VM workload prediction using Grey Forecasting model in cloud data center. In The International Conference on Information Networking 2014 (ICOIN2014). p 40–45

[99]

Kluge F, Uhrig S, Mische J, Satzger B, Ungerer T (2010) Dynamic workload prediction for soft real-time applications. In 2010 10th IEEE International Conference on Computer and Information Technology. p 1841–1848

[100]

Ardagna D, Casolari S, Panicucci B (2011) Flexible distributed capacity allocation and load redirect algorithms for cloud systems. In 2011 IEEE 4th International Conference on Cloud Computing. p 163–170

[101]

Qazi K, Li Y, Sohn A (2013) PoWER: prediction of workload for energy efficient relocation of virtual machines. In Proceedings of the 4th annual Symposium on Cloud Computing, 2013: ACM, p. 31

[102]

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

[103]

Ghorbani M, Wang Y, Xue Y, Pedram M, Bogdan P (2014) Prediction and control of bursty cloud workloads: a fractal framework. In Proceedings of the 2014 International Conference on Hardware/Software Codesign and System Synthesis. ACM, p. 12

[104]

Cortez E, Bonde A, Muzio A, Russinovich M, Fontoura M, Bianchini R (2017) Resource central: Understanding and predicting workloads for improved resource management in large cloud platforms. In Proceedings of the 26th Symposium on Operating Systems Principles. p 153–167

[105]

Ganapathi A, Chen Y, Fox A, Katz R, Patterson D (2010) Statistics-driven workload modeling for the cloud. In 2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010). p 87–92

[106]

Nguyen HM, Kim SH, Le DT, Heo S, Im J, Kim D (2015) Epcloud flow: load prediction and migration optimizations for epc network on cloud. In 2015 IEEE 8th International Conference on Cloud Computing. p 981–984

[107]

Prevost JJ, Nagothu K, Jamshidi M, Kelley B (2014) Optimal calculation overhead for energy efficient cloud workload prediction. In 2014 World Automation Congress (WAC), 2014: IEEE, p 741–747

[108]

Liu Y, Gong B, Xing C, and Jian Y A virtual machine migration strategy based on time series workload prediction using cloud model Math. Probl. Eng. 2014 2014 11

[109]

Lyu H, Li P, Yan R, Masood A, Sheng B, Luo Y (2016) Load forecast of resource scheduler in cloud architecture. In 2016 International Conference on Progress in Informatics and Computing (PIC). p 508–512

[110]

Qazi K, Li Y, Sohn A (2014) Workload prediction of virtual machines for harnessing data center resources. In 2014 IEEE 7th International Conference on Cloud Computing, 2014: IEEE, p 522–529

[111]

Duggan J, Chi Y, Hacigümüş H, Zhu S, Cetintemel U (2013) Packing light: portable workload performance prediction for the cloud. In 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW). p 258–265

[112]

Zhang L, Zhang Y, Jamshidi P, Xu L, Pahl C (2014) Workload patterns for quality-driven dynamic cloud service configuration and auto-scaling. In Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, 2014: IEEE Computer Society. p 156–165

[113]

Cao J, Fu J, Li M, and Chen J CPU load prediction for cloud environment based on a dynamic ensemble model Software 2014 44 7 793-804

[114]

Shariffdeen R, Munasinghe D, Bhathiya H, Bandara U, Bandara HD (2016) Adaptive workload prediction for proactive auto scaling in PaaS systems. In 2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech), 2016: IEEE, p 22–29

[115]

Singh N and Rao S Ensemble learning for large-scale workload prediction IEEE Trans. Emerg. Topics Comput. 2014 2 2 149-165

[116]

Sommer M, Klink M, Tomforde S, Hähner J (2016) Predictive load balancing in cloud computing environments based on ensemble forecasting. In 2016 IEEE International Conference on Autonomic Computing (ICAC), 2016: IEEE, p 300–307

[117]

Hu R, Jiang J, Liu G, Wang L (2013) KSwSVR: a new load forecasting method for efficient resources provisioning in cloud. In 2013 IEEE International Conference on Services Computing, 2013: IEEE, p 120–127

[118]

Tarsa SJ, Kumar AP, Kung H (2014) Workload prediction for adaptive power scaling using deep learning. In 2014 IEEE International Conference on IC Design & Technology, 2014: IEEE, p 1–5

[119]

Janardhanan D, Barrett E (2017) CPU workload forecasting of machines in data centers using LSTM recurrent neural networks and ARIMA models. In 2017 12th International Conference for Internet Technology and Secured Transactions (ICITST), 2017: IEEE, p 55–60

[120]

Bi J, Zhang L, Yuan H, Zhou M (2018) Hybrid task prediction based on wavelet decomposition and ARIMA model in cloud data center. In 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC), 2018: IEEE, p 1–6

[121]

Liu C, Liu C, Shang Y, Chen S, Cheng B, and Chen J An adaptive prediction approach based on workload pattern discrimination in the cloud J. Netw. Comput. Appl. 2017 80 35-44

[122]

Tang X, Liao X, Zheng J, and Yang X Energy efficient job scheduling with workload prediction on cloud data center Clus. Comput. 2018 21 3 1581-1593

[123]

Gandhi A, Chen Y, Gmach D, Arlitt M, Marwah M (2011) Minimizing data center SLA violations and power consumption via hybrid resource provisioning. In 2011 International Green Computing Conference and Workshops, 2011: IEEE, p 1–8

[124]

Guo J, Wu J, Na J, Zhang B (2017) A type-aware workload prediction strategy for non-stationary cloud service. In 2017 IEEE 10th Conference on Service-Oriented Computing and Applications (SOCA), 2017: IEEE, p 98–103

[125]

Ahn YW, Cheng AM, Baek J, Jo M, and Chen H-H An auto-scaling mechanism for virtual resources to support mobile, pervasive, real-time healthcare applications in cloud computing IEEE Netw. 2013 27 5 62-68

[126]

Shahin AA (2017) Automatic cloud resource scaling algorithm based on long short-term memory recurrent neural network. arXiv preprint arXiv:1701.03295

[127]

Ali-Eldin A, Tordsson J, Elmroth E, Kihl M (2013) Workload classification for efficient auto-scaling of cloud resources. Tech. Rep.

Cited By

Zuo ZHuang YLi ZJiang YLiu C(2025)Mixed contrastive transfer learning for few-shot workload prediction in the cloudComputing10.1007/s00607-024-01366-y107:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s00607-024-01366-y
Zhang KLiu ZChoi NJi BWooldridge MDy JNatarajan S(2024)Learning-augmented online algorithm for two-level ski-rental problemProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i18.30010(20293-20300)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i18.30010
Gao WYu ZXiong HGuo BWang LYao Y(2024)Parallel Task Scheduling in Autonomous Robotic Systems: An Event-Driven Multimodal Prediction ApproachProceedings of the 53rd International Conference on Parallel Processing10.1145/3673038.3673147(742-751)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3673038.3673147
Show More Cited By

Index Terms

A survey and classification of the workload forecasting methods in cloud computing

Index terms have been assigned to the content through auto-classification.

Recommendations

An Intelligent Swarm Based Prediction Approach For Predicting Cloud Computing User Resource Needs
Abstract
Cloud computing aimed at offering elastic resource allocation on demand to cloud consumers. Building a cloud resource demand prediction model is a challenging task because cloud consumer’s demand changes over time. Most of the current ...
A Survey on Cloud Computing Elasticity
UCC '12: Proceedings of the 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing

Elasticity is a key feature in the cloud computing context, and perhaps what distinguishes this computing paradigm of the other ones, such as cluster and grid computing. Considering the importance of elasticity in cloud computing context, the objective ...
Amazon Cloud Computing With Java

Comments

Information & Contributors

Information

Published In

cover image Cluster Computing

Cluster Computing Volume 23, Issue 4

Dec 2020

1028 pages

ISSN:1386-7857

Issue’s Table of Contents

© Springer Science+Business Media, LLC, part of Springer Nature 2019.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 December 2020

Accepted: 23 October 2019

Revision received: 01 September 2019

Received: 08 July 2019

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

30
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 26 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zuo ZHuang YLi ZJiang YLiu C(2025)Mixed contrastive transfer learning for few-shot workload prediction in the cloudComputing10.1007/s00607-024-01366-y107:1Online publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1007/s00607-024-01366-y
Zhang KLiu ZChoi NJi BWooldridge MDy JNatarajan S(2024)Learning-augmented online algorithm for two-level ski-rental problemProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i18.30010(20293-20300)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i18.30010
Gao WYu ZXiong HGuo BWang LYao Y(2024)Parallel Task Scheduling in Autonomous Robotic Systems: An Event-Driven Multimodal Prediction ApproachProceedings of the 53rd International Conference on Parallel Processing10.1145/3673038.3673147(742-751)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3673038.3673147
Zhao ZYe WDuan P(2024)Tuning Database Parameters Using Query Perception and Evolutionary Reinforcement LearningProceedings of the 2024 8th International Conference on Control Engineering and Artificial Intelligence10.1145/3640824.3640869(52-59)Online publication date: 26-Jan-2024
https://dl.acm.org/doi/10.1145/3640824.3640869
Wang SChu ZSun YLiu YGuo YChen YJian HMa LLu XZhou JSerra ESpezzano F(2024)Multiscale Representation Enhanced Temporal Flow Fusion Model for Long-Term Workload ForecastingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680072(4948-4956)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680072
Cong RZhao ZZhang LMin G(2024)Cost-Effective Server Deployment for Multi-Access Edge Networks: A Cooperative SchemeIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2024.342652335:9(1583-1597)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1109/TPDS.2024.3426523
Wu YLiu JWang CXie XShi G(2024)Graph Transformer and LSTM Attention for VNF Multi-Step Workload Prediction in SFCIEEE Transactions on Network and Service Management10.1109/TNSM.2024.340371421:4(4480-4493)Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1109/TNSM.2024.3403714
Mendes ARosa MMarotta MAraujo AMelo ARalha C(2024)MAS-Cloud+Future Generation Computer Systems10.1016/j.future.2023.12.022154:C(16-34)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.future.2023.12.022
Parthasarathy S(2024)Secure Aware Optimized Support Vector Regression Models Based Host Overload Detection in CloudWireless Personal Communications: An International Journal10.1007/s11277-024-11079-2135:4(2061-2075)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s11277-024-11079-2
Quoc KTong VDao CLe TTran D(2024)Boosted regression for predicting CPU utilization in the cloud with periodicityThe Journal of Supercomputing10.1007/s11227-024-06451-980:18(26036-26060)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s11227-024-06451-9
Show More Cited By

View Options

View options

Figures

Tables

Media

View Issue’s Table of Contents