Skip to main content

Advertisement

SpringerLink
Log in

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

  • Survey Article
  • Published:
SN Computer Science Aims and scope Submit manuscript

A Publisher Correction to this article was published on 28 September 2023

This article has been updated

Abstract

Given the perpetual surging of cloud services’ requests, energy consumption of cloud data centers with their related CO2 emissions still represents major issues. Efficient use of cloud’s resources becomes then the driven force ensuring both, the satisfaction of service-level agreements and the sobriety of cloud’s energy consumption. This paper purports to survey for the first time, a comprehensive literature of some actual challenges facing various cloud’s resources utilization scheduling approaches dealing with energy conservation. Indeed, cloud resources involve not only computing servers, but also a wide set of intra- and inter-cloud network’s resources to be considered. These resources are mainly provisioned through two well-known technologies: virtualization and/or containerization. As existing studies in the area of cloud resources provisioning are generally categorized into different groups, this research depicts a complete taxonomy of energy-efficient cloud resources scheduling. In this same perspective, we survey some recent efforts made in energy-efficient virtual and containerized resources, by emphasizing first the most used reactive then proactive techniques for managing the whole cloud resources energy efficiency scheduling.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Change history

Notes

  1. Google Cloud: https://cloud.google.com/kubernetes-engine/docs/.

  2. https://searchitoperations.techtarget.com/definition/Google-Container-Engine-GKE.

Abbreviations

ABC:

Artificial bees colony

ACO:

Ant colony optimization

ACP:

Principal component analysis

ACS:

Ant colony system

ANN:

Artificial neural network

API:

Application program interface

AR:

Autoregressive model

ARIMA:

Autoregressive integrated moving average

ARMA:

Autoregressive moving average

BF:

Best fit

BFD:

Best fit decreasing

BN:

Bayesian network

BP:

Bin packing

BW:

Bandwidth

Cbfd:

Combined best fit decreasing

CFS:

Constant fixed selection

CPs:

Cloud providers

DCs:

Data centers

DMA:

Dynamic management algorithm

DVFS:

Dynamic voltage frequency scaling

DVMC:

Dynamic virtual machine consolidation

ECE:

Energy and carbon efficient

FCE:

Frequency core energy

FFD:

First fit decreasing

FLS:

Fuzzy logic system

Gas:

Genetic algorithms

GHS:

Greenhouse gas

GTR:

Global topology resource

HPC:

High-performance computing

IQR:

Interquartile range

ISP:

Internet service provider

KNN:

K-nearest neighbor

LB:

Load balancing

LFPSO:

Levy flight PSO

LR:

Local regression

LSA:

Link state adaptation

M5P:

Regression tree

MAD:

Median absolute deviation

MAE:

Mean absolute error

MAPE:

Mean absolute percentage error

MC:

Maximum correlation

MFD:

Modified first decreasing

MIPS:

Million instructions per second

ML:

Machine learning

MLP:

Multilayer perceptron

MM:

Minimum migration

MMT:

Minimum migration time

MoA:

On arrival migration

MSE:

Mean squared error

NIAs:

Natural inspired algorithms

NPC:

Necessary path condition

NP-hard:

Non-deterministic polynomial time hardness

ODCN:

Optical data center network

OFDM:

Orthogonal frequency division multiplexing

PAM:

Partitioning around medoids

PMs:

Physical machines

POI:

Point of interest

ppA:

Power consumption per application

PSO:

Particle swarm optimization

PSOLBP:

PSO levy bin packing

PUE:

Power usage effectiveness

QoS:

Quality of service

RBF:

Radial basis function

RBM:

Restricted Boltzmann machine

RL:

Reinforcement learning

RLR:

Robust local regression

RMSE:

Root-mean-square error

RS/RC:

Random selection/random choice

RWS:

Roulette wheel selection

SARIMA:

Seasonal autoregressive integrated moving average

SLA:

Service-level agreement

SVM:

Support vector machine

VDCs:

Virtual data centers

VMs:

Virtual machines

VNE:

Virtual network embedding

VNs:

Virtual networks

WDM:

Wave length division multiplexing

WEEC:

Workload and energy-efficient container

WSVM:

Wavelet support vector machine

References

  1. Cisco Public. Cisco global cloud index: forecast and methodology. 2016. 2015–2020 from https://www.iotjournaal.nl/wp-content/uploads/2017/02/white-paper-c11-738085.pdf. Accessed 20 May 2019.

  2. Whitney J, Delforge D. Data center efficiency assessment–scaling up energy efficiency across the data center industry: evaluating key drivers and barriers, Rep. IP NRDC and Anthesis, pp. 14–08, 2014.

  3. Kliazovich D, Bouvry P, Khan SUJ. GreenCloud: a packet-level simulator of energy-aware cloud computing data centers. J Supercomput. 2012;62:1263–83. https://doi.org/10.1007/s11227-010-0504-1.

    Article  Google Scholar 

  4. Raïs I, Orgerie AC, Quinson M, Lefèvre L. Quantifying the impact of shutdown techniques for energy-efficient data centers. Concurr Comput Pract Exp. 2018. https://doi.org/10.1002/cpe.4471.

    Article  Google Scholar 

  5. Carrega A, Singh S, Bruschi R, Bolla R. Traffic merging for energy-efficient datacenter networks. In: Proceedings of the international symposium of performance evaluation of computing and telecommunication systems. IEEE; 2012. pp. 1–5.

  6. Li Y, Orgerie AC, Menaud JM. Balancing the use of batteries and opportunistic scheduling policies for maximizing renewable energy consumption in a Cloud data center. In: 25th Euromicro international conference on parallel, distributed, and network-based processing. St Petersburg, Russia; 2017.

  7. Khosravi A, Garg SK, Buyya R. Energy and carbon-efficient placement of virtual machines in distributed cloud data centers. In: Wolf F, Mohr B, Mey D, editors. Euro-Par 2013 parallel processing. Euro-Par 2013. Lecture notes in computer science. Berlin: Springer; 2013. vol. 8097, pp. 115–134. https://doi.org/10.1016/b978-0-12-801476-9.00006-9.

  8. Baker T, Aldawsari B, Asim M, Tawfik H, Maamar Z, Buyya R. Cloud-SEnergy: a bin-packing based multi-cloud service broker for energy efficient composition and execution of data-intensive applications. Sustain Comput Inform Syst. 2018. https://doi.org/10.1016/j.suscom.2018.05.011.

    Article  Google Scholar 

  9. Lei S, Furlong J, Wang R. Empirical evaluation of vector bin packing algorithms for energy efficient data centers. IEEE Symp Comput Commun. 2013. https://doi.org/10.1109/iscc.2013.6754915.

    Article  Google Scholar 

  10. Ngenzi A, Rangasamy RS, Suchithra R. Improving server consolidation using physical consolidation concept. Int J Adv Appl Sci. 2016.

  11. Halácsy G, Zoltán ÁM. Optimal energy-efficient placement of virtual machines with divisible sizes. Inf Process Lett. 2018;138:51–6. https://doi.org/10.1016/j.ipl.2018.06.003.

    Article  MathSciNet  MATH  Google Scholar 

  12. Somnath M, Marco P. Power efficient server consolidation for Cloud data center. Future Gener Comput Syst. 2017;70:4–16. https://doi.org/10.1016/j.future.2016.12.022.

    Article  Google Scholar 

  13. Wang H, Tianfield H. Energy-aware dynamic virtual machine consolidation for cloud datacenters. IEEE Access. 2018;6:15259–73. https://doi.org/10.1109/access.2018.2813541.

    Article  Google Scholar 

  14. Fares A, Yu-Chu T, Maolin T, Wei-Zhe Z, Chen P, Minrui F. An ant colony system for energy-efficient dynamic virtual machine placement in data centers. Expert Syst Appl. 2019;120:228–38. https://doi.org/10.1016/j.eswa.2018.11.029.

    Article  Google Scholar 

  15. Fatima A, Javaid N, Sultana T, Aalsalem M, Shabbir S, Durre-Adan. An efficient virtual machine placement via bin packing in cloud data centers. In: Barolli L, Takizawa M, Xhafa F, Enokido T, editors. Advanced information and applications AINA 2019. Advances in intelligent systems and computing, vol. 926. Cham: Springer; 2019. https://doi.org/10.1007/978-3-030-15032-7_82.

    Chapter  Google Scholar 

  16. Ayaz AK, Muhammad Z, Rahim K. Energy-aware dynamic resource management in elastic cloud datacenters. Simul Model Pract Theory. 2019;92:82–99. https://doi.org/10.1016/j.simpat.2018.12.001.

    Article  Google Scholar 

  17. Zhu W, Zhuang Y, Zhang L. A three-dimensional virtual resource scheduling method for energy saving in cloud computing. Future Gener Comput Syst. 2017;69:66–74. https://doi.org/10.1016/j.future.2016.10.034.

    Article  Google Scholar 

  18. Portaluri G, Giordano S, Kliazovich D, Dorronsoro B. A power efficient genetic algorithm for resource allocation in cloud computing data centers. In: 2014 IEEE 3rd international conference on cloud networking (CloudNet). Luxembourg; 2014. pp. 58–63. https://doi.org/10.1109/cloudnet.2014.6968969.

  19. Portaluri G, Giordano S. Power efficient resource allocation in cloud computing data centers using multi-objective genetic algorithms and simulated annealing. In: IEEE 4th international conference on cloud networking (CloudNet). Niagara Falls; 2015. pp. 319–321. https://doi.org/10.1109/cloudnet.2015.7335329.

  20. Geronimo GA, Brundo Uriarte R, Westphall C. Order@Cloud: an agnostic meta-heuristic for VM provisioning, adaptation, and organization. J Netw Manag. 2019. https://doi.org/10.1002/nem.2085.

    Article  Google Scholar 

  21. Zhang W, Tan S, Lu G, Liu X. Towards a genetic algorithm based approach for task migrations. In: 2014 international conference on identification, information and knowledge in the internet of things. Beijing; 2014. pp. 182–187. https://doi.org/10.1109/iiki.2014.45.

  22. Sharma NK, Reddy GRM. Novel energy efficient virtual machine allocation at data center using Genetic algorithm. In: 3rd International conference on signal processing, communication and networking (ICSCN). Chennai; 2015. pp. 1–6. https://doi.org/10.1109/icscn.2015.7219897.

  23. Sharma NK, Reddy GRM. A novel energy efficient resource allocation using hybrid approach of genetic DVFS with bin packing. In: Fifth international conference on communication systems and network technologies. Gwalior; 2015. pp. 111–115. https://doi.org/10.1109/csnt.2015.156.

  24. Zha J, Wang C, Chen Q, Lu X. Lai J Server consolidation based on hybrid genetic algorithm. In: Ninth international conference on frontier of computer science and technology. Dalian; 2015. pp. 370–375. https://doi.org/10.1109/fcst.2015.43.

  25. Sharma NK, Guddeti RMR. Multi-objective resources allocation using improved genetic algorithm at cloud data center. In: IEEE international conference on cloud computing in emerging markets (CCEM). Bangalore; 2016. pp. 73–77. https://doi.org/10.1109/ccem.2016.021.

  26. Bakalla M, Al-Jami H, Kurdi H, Alsalamah S. A QoS-aware and energy-efficient genetic resource allocation algorithm for cloud data centers. In: 9th International congress on ultra modern telecommunications and control systems and workshops (ICUMT). Munich; 2017. pp. 244–249. https://doi.org/10.1109/icumt.2017.8255166.

  27. Wen Y, Li L, Jin S, Lin C, Liu Z. Energy-efficient virtual resource dynamic integration method in cloud computing. IEEE Access. 2017;5:12214–23. https://doi.org/10.1109/ACCESS.2017.2721548.

    Article  Google Scholar 

  28. Xiaoqing Z. Efficient and balanced virtualized resource allocation based on genetic algorithm in cloud. In: The 10th international symposium on computational intelligence and design. Hangzhou; 2017. pp. 374–377. https://doi.org/10.1109/iscid.2017.187.

  29. Kaustabha R, Sunanda B, Nandini M. A load balancing approach to resource provisioning in cloud infrastructure with a grouping genetic algorithm. In: International conference on current trends towards converging technologies (ICCTCT). Coimbatore: IEEE; 2018. pp. 1–6. https://doi.org/10.1109/icctct.2018.8550885.

  30. Sharma NK, Reddy GRM. Multi-objective energy efficient virtual machines allocation at the cloud data center. IEEE Trans Serv Comput. 2019;12(1):158–71. https://doi.org/10.1109/TSC.2016.2596289.

    Article  Google Scholar 

  31. Hu W, Li K, Xu J, Bao Q. Cloud-Computing-based resource allocation research on the perspective of improved ant colony algorithm. In: International conference on computer science and mechanical automation (CSMA). Hangzhou, China: IEEE; 2015. pp. 76–80. https://doi.org/10.1109/csma.2015.22.

  32. Ragmani A, Omri AE, Abghour N, Moussaid K, Rida M. A performed load balancing algorithm for public Cloud computing using ant colony optimization. In: 2nd International conference on cloud computing technologies and applications (CloudTech). Morocco: Marrakech; 2016. pp. 221–228. https://doi.org/10.1109/cloudtech.2016.7847703.

  33. Agrawal K, Tripathi P. Power aware artificial bee colony virtual machine allocation for private cloud systems. In: International conference on computational intelligence and communication networks (CICN). Jabalpur, India: IEEE; 2015. pp. 947–950. https://doi.org/10.1109/cicn.2015.186.

  34. Ragmani A, Omri A.E, Abghour N, Moussaid K and Rida M. An intelligent scheduling algorithm for energy efficiency in cloud environment based on artificial bee colony, In: 3rd International conference of cloud computing technologies and applications (CloudTech). Rabat, Morocco, IEEE; 2017. pp. 1–8. https://doi.org/10.1109/cloudtech.2017.8284708.

  35. Benali A, El Asri B and Kriouile H. A pareto-based Artificial Bee Colony and product line for optimizing scheduling of VM on cloud computing. In: International conference on cloud technologies and applications (CloudTech). Marrakech, Morocco; 2015. pp. 1–7. https://doi.org/10.1109/cloudtech.2015.7336980.

  36. Arroba P, Risco-Martín JL, Zapater M, Moya JM, Ayala JL, Olcoz K. Server power modeling for run-time energy optimization of cloud computing facilities. Energy Procedia. 2014;62:401–10. https://doi.org/10.1016/j.egypro.2014.12.402.

    Article  Google Scholar 

  37. Aruna P, Vasantha S. A particle swarm optimization algorithm for power-aware virtual machine allocation. In: 6th International conference on computing, communication and networking technologies (ICCCNT). Denton, TX, USA; 2015. pp. 1–6. https://doi.org/10.1109/icccnt.2015.7395196.

  38. Ramezani F, Naderpour M, Lu J. A multi-objective optimization model for virtual machine mapping in cloud data centres. In: IEEE international conference on fuzzy systems (FUZZ-IEEE), Vancouver, BC, Canada; 2016. pp. 1259–1265. https://doi.org/10.1109/fuzz-ieee.2016.7737833.

  39. Tripathi A, Pathak I, Vidyarthi DP. Energy efficient VM placement for effective resource utilization using modified binary PSO. Comput J. 2017;61(6):832–46. https://doi.org/10.1093/comjnl/bxx096.

    Article  Google Scholar 

  40. Elhady GF, Tawfeek MA. A comparative study into swarm intelligence algorithms for dynamic tasks scheduling in cloud computing. In: IEEE seventh international conference on intelligent computing and information systems (ICICIS), Cairo, Egypt: 2015. pp. 362–369. https://doi.org/10.1109/intelcis.2015.7397246.

  41. Kaur N, Chhabra A. Analytical review of three latest nature inspired algorithms for scheduling in clouds. In: International conference on electrical, electronics, and optimization techniques (ICEEOT). Chennai, India; 2016. pp. 3296–3300. https://doi.org/10.1109/iceeot.2016.7755315.

  42. Abrol P, Gupta S, Kaur K. Analysis of Resource Management and placement policies using a new nature inspired meta heuristic SSCWA avoiding premature convergence in Cloud. In: International conference on computational techniques in information and communication technologies (ICCTICT), New Delhi, India; 2016. pp. 653–658. https://doi.org/10.1109/icctict.2016.7514659.

  43. Gupta P, Tewari P. Monkey search algorithm for task scheduling in cloud IaaS, In: Fourth international conference on image information processing (ICIIP). Shimla, India; 2017. pp. 1–6. https://doi.org/10.1109/iciip.2017.8313789.

  44. Gelenbe E, Lent R, Douratsos M. Choosing a local or remote cloud. In: The second symposium on network cloud computing and applications. London; 2012. pp. 25–30. https://doi.org/10.1109/ncca.2012.16.

  45. Lent R. Simulating the power consumption of computer networks. In: The 15th IEEE international workshop on computer aided modeling, analysis and design of communication links and networks (CAMAD). Miami, FL; 2010. pp. 96–100. https://doi.org/10.1109/camad.2010.5686955.

  46. Su S, Zhang Z, Liu AX, Cheng X, Wang Y, Zhao X. Energy-aware virtual network embedding. IEEE/ACM Trans Netw. 2014;22(5):1607–20. https://doi.org/10.1109/TNET.2013.2286156.

    Article  Google Scholar 

  47. Guan X, Choi BY, Song S. Energy efficient virtual network embedding for green data centers using data center topology and future migration. Comput Commun. 2015;69:50–9. https://doi.org/10.1016/j.comcom.2015.05.003.

    Article  Google Scholar 

  48. Manh Nam T, Huu Thanh N, Trung Hieu H, Tien Manh N, Van Huynh N, Duong Tuan H. Joint network embedding and server consolidation for energy–efficient dynamic data center virtualization. Comput Netw. 2017;125:76–89. https://doi.org/10.1016/j.comnet.2017.06.007.

    Article  Google Scholar 

  49. Nonde L, El-Gorashi TEH, Elmirghani JMH. Energy efficient virtual network embedding for cloud networks. J Lightwave Technol. 2015;33(9):1828–49. https://doi.org/10.1109/JLT.2014.2380777.

    Article  Google Scholar 

  50. Zhang P. Incorporating energy and load balance into virtual network embedding process. Comput Commun. 2018;129:80–8. https://doi.org/10.1016/j.comcom.2018.07.027.

    Article  Google Scholar 

  51. Zong Y, et al. Location-aware energy efficient virtual network embedding in software-defined optical data center networks. IEEE/OSA J Opt Commun Netw. 2018;10(7):58–70. https://doi.org/10.1364/JOCN.10.000B58.

    Article  Google Scholar 

  52. Ghazisaeedi E, Huang C. EnergyMap: energy-efficient embedding of MapReduce-based virtual networks and controlling incast queuing delay, In: 8th IEEE international conference on communication software and networks (ICCSN), Beijing, China; 2016. pp. 698–702. https://doi.org/10.1109/iccsn.2016.7586614.

  53. Triki N, Kara N, El Barachi M, Hadjres S. A green energy-aware hybrid virtual network embedding approach. Comput Netw. 2015;91:712–37. https://doi.org/10.1016/j.comnet.2015.08.016.

    Article  Google Scholar 

  54. Gong X, Ning Z, Guo L, Wei X, Song Q. Location-recommendation-aware virtual network embedding in energy-efficient optical-wireless hybrid networks supporting 5G models. IEEE Access. 2016;4:3065–75. https://doi.org/10.1109/ACCESS.2016.2580615.

    Article  Google Scholar 

  55. Cisco Solution Overview. Containerized data centers: compelling economics and efficiency. https://www.cisco.com/c/dam/en_us/solutions/industries/docs/gov/Containerized_Data_Centers_Solution_Overview.pdf.

  56. Zakarya M. Energy and performance aware resource management in heterogeneous cloud datacenters. A thesis submitted for the degree of Doctor of Philosophy in Computer Science from the University of Surrey; Guildford, Surrey GU2 7XH, United Kingdom; 2017.

  57. Dong-Ki K, Gyu-Beom C, Seong-Hwan K, Il-Sun H, Chan-Hyun Y. Workload-aware resource management for energy efficient heterogeneous Docker containers. In: IEEE region 10 conference (TENCON). Singapore; 2016. pp. 2428–2431. https://doi.org/10.1109/tencon.2016.7848467.

  58. Ching-Chi L, Jian-Jia C, Pangfeng L, Jan-Jan W. Energy-efficient core allocation and deployment for container-based virtualization. In: IEEE 24th international conference on parallel and distributed systems (ICPADS). Singapore; 2018. pp. 93–101. https://doi.org/10.1109/padsw.2018.8644537.

  59. Ayaz A, Zakarya M, Khan R. H2—a hybrid heterogeneity aware resource orchestrator for cloud platforms. IEEE Syst J. 2019. https://doi.org/10.1109/jsyst.2019.2899913.

    Article  Google Scholar 

  60. Piraghaj SF, Dastjerdi AV, Calheiros RN, Buyya R. A framework and algorithm for energy efficient container consolidation in cloud data centers. In: IEEE international conference on data science and data intensive systems. Sydney; 2015. pp. 368–375. https://doi.org/10.1109/dsdis.2015.67.

  61. Tao S, Hui Ma, Gang CH. Energy-aware container consolidation based on PSO in cloud data centers. In: IEEE congress on evolutionary computation (CEC). Rio de Janeiro; 2018. pp. 1–8. https://doi.org/10.1109/cec.2018.8477708.

  62. Piraghaj SF, Dastjerdi AV, Calheiros RN, Buyya R. Efficient virtual machine sizing for hosting containers as a service. In: IEEE world congress on services. New York, NY; 2015. pp. 31–38. https://doi.org/10.1109/SERVICES.2015.14.

  63. Qasim A. Scaling web 2.0 applications using docker containers on vsphere 6.0. [Online]; 2015. http://blogs.vmware.com/performance/2015/04/scaling-web-2-0-applications-using-docker-containers-vsphere-6-0.html.

  64. Li T, Wang J, Li W, Xu T, Qi Q. Load prediction-based automatic scaling cloud computing. In: The international conference on networking and network applications (NaNA). Hakodate, Japan; 2016. pp. 330–335. https://doi.org/10.1109/nana.2016.49.

  65. Aldossary M, Djemame K, Alzamil I, Kostopoulos A, Dimakis A, Agiatzidou E. Energy-aware cost prediction and pricing of virtual machines in cloud computing environments. Future Gener Comput Syst. 2019;93:442–59. https://doi.org/10.1016/j.future.2018.10.027.

    Article  Google Scholar 

  66. Jararweh Y, Bani Issa M, Daraghmeh M, Al-Ayyoub M, Alsmirat MA. Energy efficient dynamic resource management in cloud computing based on logistic regression model and median absolute deviation. Sustain Comput Inform Syst. 2018;19:262–74. https://doi.org/10.1016/j.suscom.2018.07.005.

    Article  Google Scholar 

  67. Daraghmeh M, Bani Melhem S, Agarwal A, Goel N, Zaman M. Linear and logistic regression based monitoring for resource management in cloud networks. In: The IEEE 6th international conference on future internet of things and cloud (FiCloud). Barcelona, Spain; 2018. pp. 259–266. https://doi.org/10.1109/ficloud.2018.00045.

  68. Yoon MS, Kamal AE, Zhu Z. Requests prediction in cloud with a cyclic window learning algorithm. In: The IEEE globecom workshops (GC Wkshps). Washington, DC USA; 2016. pp. 1–6. https://doi.org/10.1109/glocomw.2016.7849022.

  69. Xiaoqing Z. Energy-aware virtual machine management optimization in clouds. In: 2017 3rd IEEE international conference on computer and communications (ICCC). Chengdu, China; 2017; pp. 2434–2438. https://doi.org/10.1109/compcomm.2017.8322972.

  70. Kim IK, Wang W, Qi Y, Humphrey M. CloudInsight: utilizing a council of experts to predict future cloud application workloads. In: IEEE 11th international conference on cloud computing (CLOUD). San Francisco, CA USA; 2018. pp. 41–48. https://doi.org/10.1109/cloud.2018.00013.

  71. Li Y, Xia Y. Auto-scaling web applications in hybrid cloud based on Docker. In: 5th International conference on computer science and network technology (ICCSNT). Changchun, China; 2016. pp. 75–79. https://doi.org/10.1109/iccsnt.2016.8070122.

  72. Renault E, Boumerdassi S, Bouzefrane S, Gbaguid FAR, Milocco R, Ezin EC. Adaptive ARMA based prediction of CPU consumption of servers into datacenters. Cham, Springer International Publishing; 2019. pp. 277–288. https://doi.org/10.1007/978-3-030-03101-5_23.

  73. Shaw R, Howley E, Barrett E. A predictive anti-correlated virtual machine placement algorithm for green cloud computing. In: IEEE/ACM 11th international conference on utility and cloud computing (UCC). Zurich, IEEE; 2018. pp. 267–276. https://doi.org/10.1109/ucc.2018.00035.

  74. Shaw R, Howley E, Barrett E. An energy efficient anti-correlated virtual machine placement algorithm using resource usage predictions. Simul Model Pract Theory. 2019;93:322–42. https://doi.org/10.1016/j.simpat.2018.09.019.

    Article  Google Scholar 

  75. Tran VG, Debusschere V, Bacha S. Hourly server workload forecasting up to 168 hours ahead using Seasonal ARIMA model. In: 2012 IEEE international conference on industrial technology. Athens; 2012. pp. 1127–1131. https://doi.org/10.1109/icit.2012.6210091.

  76. Altomare A, Cesario E. Predictive models for energy-efficient clouds: an analysis on real-life and synthetic data. In: IEEE international conference on computer and information technology; ubiquitous computing and communications; dependable, autonomic and secure computing; pervasive intelligence and computing. Liverpool, UK; 2015. pp. 1538–1543. https://doi.org/10.1109/cit/iucc/dasc/picom.2015.231.

  77. Gagangeet SA, Neeraj K. MEnSuS: an efficient scheme for energy management with sustainability of cloud data centers in edge–cloud environment. Future Gener Comput Syst. 2018;86:1279–300. https://doi.org/10.1016/j.future.2017.09.066.

    Article  Google Scholar 

  78. Zhong W, Zhuang Y, Sun J, and Gu J. The cloud computing load forecasting algorithm based on wavelet support vector machine. In: Proceedings of the Australasian computer science week multiconference (ACSW ‘17). New York, ACM; 2017. p. 5. https://doi.org/10.1145/3014812.3014852.

  79. Farahnakian F, Pahikkala T, Liljeberg P, Plosila J, Tenhunen H. Utilization prediction aware VM consolidation approach for green cloud computing, In: IEEE 8th international conference on cloud computing. New York, USA; 2015. pp. 381–388. https://doi.org/10.1109/cloud.2015.58.

  80. Bashar A. Autonomic scaling of cloud computing resources using BN-based prediction models. In: IEEE 2nd international conference on cloud networking (CloudNet). San Francisco, CA; 2013. pp. 200–204. https://doi.org/10.1109/cloudnet.2013.6710578.

  81. Nathanael Witanto J, Lim H, Atiquzzaman M. Adaptive selection of dynamic VM consolidation algorithm using neural network for cloud resource management. Future Gener Comput Syst. 2018;87:35–42. https://doi.org/10.1016/j.future.2018.04.075.

    Article  Google Scholar 

  82. Ahammad T, Acharjee UK, Hasan MM. Energy-effective service-oriented cloud resource allocation model based on workload prediction. In: 21st International conference of computer and information technology (ICCIT), Dhaka, Bangladesh; 2018. pp. 1–6. https://doi.org/10.1109/iccitechn.2018.8631953.

  83. Lenhardt J, Schiffmann W, Jannevers S. Prediction of future loads using neural networks for energy-efficient computing. In: Fourth international symposium on computing and networking (CANDAR), Hiroshima, Japan; 2016. pp. 579–585. https://doi.org/10.1109/candar.2016.0105.

  84. Ali J, Zafari F, Khan GM, Mahmud SA. Future clients’ requests estimation for dynamic resource allocation in cloud data center using CGPANN. In: 12th International conference on machine learning and applications, Miami, FL, USA; 2013. pp. 331–334. https://doi.org/10.1109/icmla.2013.189.

  85. Qiu F, Zhang B, Guo J. 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; 2016. pp. 319–324. https://doi.org/10.1109/snpd.2016.7515919.

  86. Xia Q, Lan Y, Zhao L, Xiao L. Energy-saving analysis of Cloud workload based on K-means clustering. In: IEEE computers, communications and IT applications conference, Beijing, China; 2014. pp. 305–309. https://doi.org/10.1109/comcomap.2014.7017215.

  87. Ismaeel S, Miri A. Using ELM techniques to predict data centre VM requests. In: 2015 IEEE 2nd international conference on cyber security and cloud computing. New York, NY, USA; 2015. pp. 80–86. https://doi.org/10.1109/cscloud.2015.82.

  88. Raed K, Salam I, Miri A. Energy-efficient resource allocation for cloud data centers using a multi-way data analysis technique, human-computer interaction. Theory Des Dev Pract. 2016;9731:577–85. https://doi.org/10.1007/978-3-319-39510-4_53.

    Article  Google Scholar 

  89. Yu Y, Jindal V, Yen I, Bastani F Integrating clustering and learning for improved workload prediction in the cloud. In: IEEE 9th international conference on cloud computing (CLOUD), San Francisco, CA, USA; 2016. pp. 876–879. https://doi.org/10.1109/cloud.2016.0127.

  90. Farahnakian F, Liljeberg P, Plosila J. Energy-efficient virtual machines consolidation in cloud data centers using reinforcement learning. In: 22nd Euromicro international conference on parallel, distributed, and network-based processing. Torino, Italy; 2014. pp. 500–507. https://doi.org/10.1109/pdp.2014.109.

  91. Khelghatdoust M, Gramoli V, Sun D. GLAP: distributed dynamic workload consolidation through gossip-based learning. In: IEEE international conference on cluster computing (CLUSTER). Taipei, Taiwan; 2016. pp. 80–89. https://doi.org/10.1109/cluster.2016.24.

  92. Thein T, Myat My M, Parvin S, Gawanmeh A. Reinforcement learning based methodology for energy-efficient resource allocation in cloud data centers. J King Saud Univ Comput Inf Sci. 2018. https://doi.org/10.1016/j.jksuci.2018.11.005.

    Article  Google Scholar 

  93. Loff J, Garcia J. Vadara: predictive elasticity for cloud applications. In: IEEE 6th international conference on cloud computing technology and science. Singapore; 2014. pp. 541–546. https://doi.org/10.1109/cloudcom.2014.161.

  94. Al-Rawahi M, Edirisinghe EA, Jeyarajan T. Machine learning-based framework for resource management and modelling for video analytic in cloud-based hadoop environment. In: International IEEE conferences on ubiquitous intelligence & computing, advanced and trusted computing, scalable computing and communications, cloud and big data computing, internet of people, and smart world congress. Toulouse, France; 2016. pp. 801–807. https://doi.org/10.1109/uic-atc-scalcom-cbdcom-iop-smartworld.2016.0128.

  95. Hirwa JS, Rugwiro U, Stammers M, Gu C. Cloud-based clusters: multivariate optimization techniques for resource performance prediction. In: 15th International symposium on parallel and distributed computing (ISPDC). Fuzhou, China; 2016. pp. 165–171. https://doi.org/10.1109/ispdc.2016.29.

  96. Nguyen HM, Woo S, Im J, Jun J, Kim D. A workload prediction approach using models stacking based on recurrent neural network and autoencoder. In: 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). Sydney, Australia; 2016. pp. 929–936. https://doi.org/10.1109/hpcc-smartcity-dss.2016.0133.

  97. Liao S, Zhang H, Shu G, Li J. Adaptive resource prediction in the cloud using linear stacking model. In: 2017 Fifth international conference on advanced cloud and big data (CBD). Shanghai, China; 2017. pp. 33–38. https://doi.org/10.1109/cbd.2017.14.

  98. Borkowski M, Schulte S, Hochreiner C. Predicting cloud resource utilization. In: 2018 15th international conference on smart cities: improving quality of life using ICT & IoT (HONET-ICT). Islamabad; 2016. pp. 38–42. https://doi.org/10.1145/2996890.2996907.

  99. Shariffdeen RS, Munasinghe DTSP, Bhathiya HS, Bandara UKJ, Bandara HMND. Adaptive workload prediction for proactive auto scaling in PaaS systems. In: 2nd International conference on cloud computing technologies and applications (CloudTech). Marrakech, Morocco; 2016. pp. 22–29. https://doi.org/10.1109/cloudtech.2016.7847713.

  100. Rahmanian AA, Ghobaei-Arani M, Tofighy S. A learning automata-based ensemble resource usage prediction algorithm for cloud computing environment. Future Gener Comput Syst. 2018;79:54–71. https://doi.org/10.1016/j.future.2017.09.049.

    Article  Google Scholar 

  101. Kaur G, Bala A, Chana I. An intelligent regressive ensemble approach for predicting resource usage in cloud computing. J Parallel Distrib Comput. 2019;123:1–12. https://doi.org/10.1016/j.jpdc.2018.08.008.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Research Foundation for Development and Innovation in Science and Engineering, Casablanca, Morocco

    Ikhlasse Hamzaoui & Hicham Medromi

  2. Engineering Research Laboratory (LRI), System Architecture Team (EAS), National and High School of Electricity and Mechanic (ENSEM), Hassan II University, 8118, Casablanca, Morocco

    Ikhlasse Hamzaoui & Hicham Medromi

  3. EIGSI, La Rochelle, France

    Ikhlasse Hamzaoui & Benjamin Duthil

  4. University of La Rochelle, La Rochelle, France

    Benjamin Duthil & Vincent Courboulay

Authors
  1. Ikhlasse Hamzaoui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Benjamin Duthil
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vincent Courboulay
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hicham Medromi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ikhlasse Hamzaoui.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hamzaoui, I., Duthil, B., Courboulay, V. et al. A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management. SN COMPUT. SCI. 1, 73 (2020). https://doi.org/10.1007/s42979-020-0078-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-020-0078-9

Keywords

Search

Navigation