Taxonomy of green cloud computing techniques with environment quality improvement considering: a survey
- Department of Computer and Electronic Engineering, Aletaha Institute of Higher Education, Tehran, IR
Published in Issue 2022-04-23
How to Cite
Jahangard, L. R., & Shirmarz, A. (2022). Taxonomy of green cloud computing techniques with environment quality improvement considering: a survey. International Journal of Energy and Environmental Engineering, 13(4 (December 2022). https://doi.org/10.1007/s40095-022-00497-2
Abstract
Abstract Nowadays, cloud computing is one of the most up-to-date topics conducted by many researchers. The specialists and researchers try to create a new generation of data centers using virtual machines to supply the network service virtually and dynamically. These services will lead everyone to access their required application worldwide via the Internet. Furthermore, the number of datacenters (DC) is growing exponentially. Therefore, a novel concept called green computing has been raised to decrease the negative effect of data centers to protect the environment. Green cloud computing solutions strive to reduce carbon dioxide emissions, energy, power, and water consumption that are harmful to the environment. In this paper, the approaches moving toward green computing are investigated and categorized to help the researchers and specialists in cloud computing expand green cloud computing and improve the environment quality. The "green cloud computing" has been searched in this survey. We have searched ACM, IEEE, Elsevier, and Springer and surveyed the papers between 2010 and 2022. This paper is a holistic survey useful for researchers who work on green cloud computing and its environmental influence. This paper can lead researchers to move toward green computing to protect the environment against these days’ environmental issues. These days, environmental issues like climate change make this subject more important than before.Keywords
- Cloud computing,
- Green computing,
- Data center,
- Virtualization,
- Energy consumption
References
- Radu (2017) Green cloud computing: a literature survey 9(12) https://doi.org/10.3390/sym9120295
- Shirmarz and Ghaffari (2020) Performance issues and solutions in SDN-based data center: a survey 76(10) (pp. 7545-7593) https://doi.org/10.1007/s11227-020-03180-7
- Mukherjee and Sahoo (2010) Green cloud: an algorithmic approach 9(9) (pp. 1-6)
- Wang et al. (2012) A survey of green mobile networks: opportunities and challenges 17(1) (pp. 4-20) https://doi.org/10.1007/s11036-011-0316-4
- de Carvalho Junior et al. (2016) Green cloud meta-scheduling 14(1) (pp. 109-126) https://doi.org/10.1007/s10723-015-9333-z
- Zhao et al. (2013) A survey on green computing based on cloud environment 9(3) (pp. 27-33) https://doi.org/10.3991/ijoe.v9i3.2559
- Liu, L., et al.: GreenCloud: a new architecture for green data center. In: Proceedings of the 6th international conference industry session on Autonomic computing and communications industry session, pp. 29–38 (2009)
- Tawade, S.S.: Green cloud: emerging trends and their impacts (2015)
- Jing et al. (2013) State-of-the-art research study for green cloud computing 65(1) (pp. 445-468) https://doi.org/10.1007/s11227-011-0722-1
- Yang et al. (2013) Implementation of cloud IAAS for virtualization with live migration (pp. 199-207) Springer https://doi.org/10.1007/978-3-642-38027-3_21
- Pinto et al. (2018) Green computing and energy consumption issues in the modern age 4(3) (pp. 661-665)
- Sasikala (2013) Energy efficiency in cloud computing: way towards green computing 2(4) (pp. 305-324) https://doi.org/10.1504/IJCC.2013.058095
- Fazelpour et al. (2022) An assessment of reducing energy consumption for optimizing building design in various climatic conditions (pp. 319-329) https://doi.org/10.1007/s40095-021-00461-6
- Sedighkia and Abdoli (2022) Balancing environmental impacts and economic benefits of agriculture under the climate change through an integrated optimization system https://doi.org/10.1007/s40095-022-00482-9
- Masoud et al. (2017) Green cloud computing: a review 167(9) (pp. 5-7)
- Patil, A., Patil, D.: An analysis report on green cloud computing current trends and future research challenges. In: Proceedings of International Conference on Sustainable Computing in Science, Technology and Management (SUSCOM), Amity University Rajasthan, Jaipur-India (2019)
- Masdari et al. (2020) Bio-inspired virtual machine placement schemes in cloud computing environment: taxonomy, review, and future research directions (pp. 2533-2563) https://doi.org/10.1007/s10586-019-03026-9
- A-Shehri, H.A., Hamdi, K.: Multi-objective VM placement algorithms for green cloud data centers: an overview. In: 2018 21st Saudi Computer Society National Computer Conference (NCC), pp. 1–8. IEEE (2018)
- Aamir, M., Alam, M.: A survey of green cloud computing (2019).
- https://doi.org/10.13140/RG.2.2.29969.99689
- Saha (2018) Green computing: current research trends 6(3) (pp. 467-469)
- Maryam, K., Sardaraz, M., Tahir, M.: Evolutionary algorithms in cloud computing from the perspective of energy consumption: a review. In: 2018 14th International Conference on Emerging Technologies (ICET), pp. 1–6. IEEE (2018)
- Sheth, M.A., Bhosale, M.S., Pawar, M.P.: "GREEN CLOUD COMPUTING," contemporary research in india no. special issue, 2021
- Jayalath, J., Chathumali, E., Kothalawala, K., Kuruwitaarachchi, N.: Green cloud computing: a review on adoption of green-computing attributes and vendor specific implementations. In: 2019 International Research Conference on Smart Computing and Systems Engineering (SCSE), pp. 158–164. IEEE (2019)
- Khattar et al. (2019) Toward energy-efficient cloud computing: a survey of dynamic power management and heuristics-based optimization techniques 75(8) (pp. 4750-4810) https://doi.org/10.1007/s11227-019-02764-2
- Jyoti et al. (2020) Cloud computing using load balancing and service broker policy for IT service: a taxonomy and survey 11(11) (pp. 4785-4814) https://doi.org/10.1007/s12652-020-01747-z
- Katal et al. (2022) Energy efficiency in cloud computing data center: a survey on hardware technologies 25(1) (pp. 675-705) https://doi.org/10.1007/s10586-021-03431-z
- Stergiou, C.L., Psannis, K.E., Ishibashi, Y.: Green cloud communication system for big data management. In: 2020 3rd World Symposium on Communication Engineering (WSCE), pp. 69–73. IEEE (2020)
- Jumde and Dongre (2021) Analysis on energy efficient green cloud computing 1913(1) https://doi.org/10.1088/1742-6596/1913/1/012100
- Bird (2014) Distributed (green) data centers: a new concept for energy, computing, and telecommunications (pp. 83-91) https://doi.org/10.1016/j.esd.2013.12.006
- Atrey et al. (2013) A study on green cloud computing 6(6) (pp. 93-102) https://doi.org/10.14257/ijgdc.2013.6.6.08
- Borah et al. (2015) Power saving strategies in green cloud computing systems 8(1) (pp. 299-306) https://doi.org/10.14257/ijgdc.2015.8.1.28
- Naidu, P.A., Chadha, P., Nalina, V.: Efficient strategies for green cloud computing. J. Netw. Commun. Emerg. Technol.
- 10
- (6) (2020)
- Jalali et al. (2016) Fog computing may help to save energy in cloud computing 34(5) (pp. 1728-1739) https://doi.org/10.1109/JSAC.2016.2545559
- Jalali, F.: Energy consumption of cloud computing and fog computing applications. PhD Dissertation, University of Melbourne (2015)
- Kaur, A., Kinger, S.: Temperature aware resource scheduling in green clouds. In: 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1919–1923 (2013).
- https://doi.org/10.1109/ICACCI.2013.6637475
- Balasooriya et al. (2016) Green cloud computing and economics of the cloud: moving towards sustainable future 5(1)
- Itani et al. (2015) G-Route: an energy-aware service routing protocol for green cloud computing 18(2) (pp. 889-908) https://doi.org/10.1007/s10586-015-0443-y
- Kinger et al. (2014) Prediction based proactive thermal virtual machine scheduling in green clouds (pp. 1-12) https://doi.org/10.1155/2014/208983
- Bruneo, D., Lhoas, A., Longo, F., Puliafito, A.: Analytical evaluation of resource allocation policies in green IaaS clouds. In: 2013 International Conference on Cloud and Green Computing, pp. 84–91. IEEE (2013)
- Vishwanath et al. (2015) Energy consumption comparison of interactive cloud-based and local applications 33(4) (pp. 616-626) https://doi.org/10.1109/JSAC.2015.2393431
- Ghiasi and Arani (2015) Smart virtual machine placement using learning automata to reduce power consumption in cloud data centers 5(6) (pp. 553-562) https://doi.org/10.6029/smartcr.2015.06.005
- Moghaddam et al. (2012) Multi-level grouping genetic algorithm for low carbon virtual private clouds (pp. 315-324)
- Le and Wright (2015) Scheduling workloads in a network of datacentres to reduce electricity cost and carbon footprint (pp. 31-40)
- Nikoui et al. (2016) Providing a cloud broker-based approach to improve the energy consumption and achieve a green cloud computing 138(1) (pp. 42-49)
- Reguri, V.R., Kogatam, S., Moh, M.: Energy efficient traffic-aware virtual machine migration in green cloud data centers. In: 2016 IEEE 2nd International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS), pp. 268–273. IEEE (2016)
- Sahoo and Goswami (2017) Cost and energy optimisation of cloud data centres through dual VM modes-activation and passivation 18(3–4) (pp. 371-389)
- Dougherty et al. (2012) Model-driven auto-scaling of green cloud computing infrastructure 28(2) (pp. 371-378) https://doi.org/10.1016/j.future.2011.05.009
- Hulkury, M.N., Doomun, M.R.: Integrated green cloud computing architecture. In: 2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT), pp. 269–274. IEEE (2012)
- Mann and Chana (2012) Heterogeneous workload consolidation for efficient management of data centers in cloud computing (pp. 13-17)
- Moghaddam et al. (2015) Carbon-aware distributed cloud: multi-level grouping genetic algorithm 18(1) (pp. 477-491) https://doi.org/10.1007/s10586-014-0359-y
- Ibrahim et al. (2018) An integer linear programming model and adaptive genetic algorithm approach to minimize energy consumption of cloud computing data centers (pp. 551-565) https://doi.org/10.1016/j.compeleceng.2018.02.028
- Bergen, A.C.: Energy adaptive digital ecosystems. MSc Dissertation, University of Victoria (2017)
- Bruneo et al. (2014) Modeling and evaluation of energy policies in green clouds 26(11) (pp. 3052-3065) https://doi.org/10.1109/TPDS.2014.2364194
- Huang, J., Wu, K., Moh, M.: Dynamic virtual machine migration algorithms using enhanced energy consumption model for green cloud data centers. In: 2014 International Conference on High Performance Computing and Simulation (HPCS), pp. 902–910. IEEE (2014)
- Xu et al. (2013) Greening data center networks with throughput-guaranteed power-aware routing 57(15) (pp. 2880-2899) https://doi.org/10.1016/j.comnet.2012.12.012
- Kaur, A., Kinger, S.: Temperature aware resource scheduling in green clouds. In: 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1919–1923. IEEE (2013)
- Kushwaha, A.S., Alam, B., Kaur, G.: Observation of energy efficiency in green cloud simulator. In: 2016 6th International Conference-Cloud System and Big Data Engineering (Confluence), pp. 135–140. IEEE (2016)
- Makris, T.: Measuring and analyzing energy consumption of the data center (2017)
- Khan, N.: Investigating energy efficiency of physical and virtual machines in cloud computing (2017)
- Wad Nasir (2019) Sudan University of Science and Technology
- Ali and Mohammed (2014) Optimization of power consumption in cloud data centers using green networking techniques 22(2) (pp. 13-27) https://doi.org/10.33899/rengj.2014.87317
- Khan, N., Haugerud, H., Shrestha, R., Yazidi, A.: Optimizing power and energy efficiency in cloud computing. In: Proceedings of the 11th International Conference on Management of Digital EcoSystems, pp. 256–261 (2019)
- Yanovskaya, O., Yanovsky, M., Kharchenko, V.: The concept of green cloud infrastructure based on distributed computing and hardware accelerator within fpga as a service. In: Proceedings of IEEE East-West Design and Test Symposium (EWDTS 2014), pp. 1–4. IEEE (2014)
- Murwantara, I.M., Bordbar, B.: A simplified method of measurement of energy consumption in cloud and virtualized environment. In: 2014 IEEE Fourth International Conference on Big Data and Cloud Computing, pp. 654–661. IEEE (2014)
- Jain, A., Mishra, M., Peddoju, S.K., Jain, N.: Energy efficient computing-green cloud computing. In: 2013 International Conference on Energy Efficient Technologies for Sustainability, pp. 978–982. IEEE (2013)
- Khan et al. (2018) IEEE access special section editorial: green cloud and fog computing: energy efficiency and sustainability aware infrastructures, protocols, and applications (pp. 12280-12283) https://doi.org/10.1109/ACCESS.2018.2805543
- Wang, T., Xia, Y., Muppala, J., Hamdi, M., Foufou, S.: A general framework for performance guaranteed green data center networking. In: 2014 IEEE Global Communications Conference, pp. 2510–2515. IEEE (2014)
- Gholipour et al. (2020) A novel energy-aware resource management technique using joint VM and container consolidation approach for green computing in cloud data centers https://doi.org/10.1016/j.simpat.2020.102127
- Mukherjee and De (2016) Femtolet: a novel fifth generation network device for green mobile cloud computing (pp. 68-87) https://doi.org/10.1016/j.simpat.2016.01.014
- Lin (2012) A novel green cloud computing framework for improving system efficiency (pp. 2326-2333) https://doi.org/10.1016/j.phpro.2012.02.345
- Ragmani, A., El Omri, A., Abghour, N., Moussaid, K., Rida, M.: A novel green service level agreement for cloud computing using fuzzy logic. In: CLOSE, pp. 658–665 (2018)
- Abualigah and Diabat (2021) A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments 24(1) (pp. 205-223) https://doi.org/10.1007/s10586-020-03075-5
- Jeevitha and Athisha (2021) A novel scheduling approach to improve the energy efficiency in cloud computing data centers 12(6) (pp. 6639-6649) https://doi.org/10.1007/s12652-020-02283-6
- Debnath et al. (2016) E-waste management—a potential route to green computing (pp. 669-675) https://doi.org/10.1016/j.proenv.2016.07.063
- Shaw, R., Howley, E., Barrett, E.: A predictive anti-correlated virtual machine placement algorithm for green cloud computing. In: 2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC), pp. 267–276. IEEE (2018)
- Karunakaran (2019) A stochastic development of cloud computing based task scheduling algorithm 1(01) (pp. 41-48)
- Xu et al. (2019) VMSAGE: a virtual machine scheduling algorithm based on the gravitational effect for green cloud computing https://doi.org/10.1016/j.simpat.2018.10.006
- Li (2012) CyberGuarder: a virtualization security assurance architecture for green cloud computing 28(2) (pp. 379-390) https://doi.org/10.1016/j.future.2011.04.012
- Mishra et al. (2018) An adaptive task allocation technique for green cloud computing 74(1) (pp. 370-385) https://doi.org/10.1007/s11227-017-2133-4
- Lu and Sun (2019) An effective task scheduling algorithm based on dynamic energy management and efficient resource utilization in green cloud computing environment 22(1) (pp. 513-520) https://doi.org/10.1007/s10586-017-1272-y
- Mishra et al. (2020) Load balancing in cloud computing: a big picture 32(2) (pp. 149-158)
- Stavrinides and Karatza (2019) An energy-efficient, QoS-aware and cost-effective scheduling approach for real-time workflow applications in cloud computing systems utilizing DVFS and approximate computations (pp. 216-226) https://doi.org/10.1016/j.future.2019.02.019
- Karuppasamy and Balakannan (2019) An improving data delivery method using EEDD algorithm for energy conservation in green cloud network 23(18) (pp. 8643-8649) https://doi.org/10.1007/s00500-019-04027-x
- Karuppasamy and Balakannan (2019) Energy-efficient data delivery in green cloud networks (pp. 313-321) Springer https://doi.org/10.1007/978-981-13-0776-8_29
- Gamsiz and Özer (2021) An energy-aware combinatorial virtual machine allocation and placement model for green cloud computing (pp. 18625-18648) https://doi.org/10.1109/ACCESS.2021.3054559
- Di Salvo et al. (2017) Can cloud computing be labeled as “green”? Insights under an environmental accounting perspective (pp. 514-526) https://doi.org/10.1016/j.rser.2016.11.153
- Shukur et al. (2020) Cloud computing virtualization of resources allocation for distributed systems 1(3) (pp. 98-105) https://doi.org/10.38094/jastt1331
- Fathi, M.H., Khanli, L.M.: Consolidating VMs in green cloud computing using harmony search algorithm. In: Proceedings of the 2018 International Conference on Internet and e-Business, pp. 146–151 (2018)
- Gai et al. (2016) Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing (pp. 46-54) https://doi.org/10.1016/j.jnca.2015.05.016
- Juarez et al. (2018) Dynamic energy-aware scheduling for parallel task-based application in cloud computing (pp. 257-271) https://doi.org/10.1016/j.future.2016.06.029
- Toor (2019) Energy and performance aware fog computing: a case of DVFS and green renewable energy (pp. 1112-1121) https://doi.org/10.1016/j.future.2019.07.010
- Naresh et al. (2020) Energy consumption reduction in cloud environment by balancing cloud user load 7(7) (pp. 1003-1010)
- Zhou, Q., et al.: Energy efficient algorithms based on VM consolidation for cloud computing: comparisons and evaluations. In: 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), pp. 489–498. IEEE (2020)
- Gu et al. (2019) Energy efficient task allocation and energy scheduling in green energy powered edge computing (pp. 89-99) https://doi.org/10.1016/j.future.2018.12.062
- Bhattacherjee et al. (2020) Energy-efficient migration techniques for cloud environment: a step toward green computing 76(7) (pp. 5192-5220) https://doi.org/10.1007/s11227-019-02801-0
- López-Pires, F., Barán, B., Pereira, C., Velázquez, M., González, O.: Evaluation of two-phase virtual machine placement algorithms for green cloud datacenters. In: 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS* W), pp. 62–67. IEEE (2019)
- Bindhu (2019) Green cloud computing solution for operational cost efficiency and environmental impact reduction 1(02) (pp. 120-128)
- Moghaddam and Hossein (2020) Green cloud multimedia networking: NFV/SDN based energy-efficient resource allocation (pp. 873-889`) https://doi.org/10.1109/TGCN.2020.2982821
- Diouani and Medromi (2018) Green cloud computing: efficient energy-aware and dynamic resources management in data centers 9(7) (pp. 124-127)
- Rehani and Garg (2018) Meta-heuristic based reliable and green workflow scheduling in cloud computing 9(4) (pp. 811-820) https://doi.org/10.1007/s13198-017-0659-8
- Nedyalkov, I., Stefanov, A., Georgiev, G.: Modelling and studying of cloud infrastructures. In: 2018 International Conference on High Technology for Sustainable Development (HiTech), pp. 1–4. IEEE (2018)
- Kaushal, S., Gogia, D., Kumar, B.: Recent trends in green cloud computing. In: Proceedings of 2nd International Conference on Communication, Computing and Networking, pp. 947–956. Springer (2019)
- Stergiou et al. (2018) Security, privacy and efficiency of sustainable cloud computing for big data and IoT (pp. 174-184)
- Liu et al. (2020) SLA-driven container consolidation with usage prediction for green cloud computing 14(1) (pp. 42-52) https://doi.org/10.1007/s11704-018-7172-3
- Yuan et al. (2018) Spatial task scheduling for cost minimization in distributed green cloud data centers 16(2) (pp. 729-740) https://doi.org/10.1109/TASE.2018.2857206
- Benotmane et al. (2018) Towards a cloud computing in the service of green logistics 29(1) (pp. 37-61)
- Qiu et al. (2018) Towards green cloud computing: demand allocation and pricing policies for cloud service brokerage 5(2) (pp. 238-251) https://doi.org/10.1109/TBDATA.2018.2823330
- Aslam and Kalra (2019) Using artificial neural network for VM consolidation approach to enhance energy efficiency in green cloud (pp. 139-154) Springer https://doi.org/10.1007/978-981-13-0277-0_12
- Mohiuddin and Almogren (2019) Workload aware VM consolidation method in edge/cloud computing for IoT applications (pp. 204-214) https://doi.org/10.1016/j.jpdc.2018.09.011
- Shu et al. (2021) Research on strong agile response task scheduling optimization enhancement with optimal resource usage in green cloud computing (pp. 12-20) https://doi.org/10.1016/j.future.2021.05.012
- Haseeb et al. (2021) Intelligent and secure edge-enabled computing model for sustainable cities using green internet of things https://doi.org/10.1016/j.scs.2021.102779
- Jaiswal et al. (2021) Green computing in IoT: time slotted simultaneous wireless information and power transfer (pp. 155-169) https://doi.org/10.1016/j.comcom.2020.12.024
- Biswas et al. (2021) An approach towards development of new linear regression prediction model for reduced energy consumption and SLA violation in the domain of green cloud computing
- Ajmal et al. (2021) Cost-based energy efficient scheduling technique for dynamic voltage and frequency scaling system in cloud computing
- Haddad (2021) Combined IT and power supply infrastructure sizing for standalone green data centers
- Rehman et al. (2021) M-SMDM: a model of security measures using green internet of things with cloud integrated data management for smart cities https://doi.org/10.1016/j.eti.2021.101802
- Cao et al. (2021) Multi-level energy efficiency evaluation for die casting workshop based on fog-cloud computing https://doi.org/10.1016/j.energy.2021.120397
- Masdari and Zangakani (2020) Green cloud computing using proactive virtual machine placement: challenges and issues (pp. 727-759) https://doi.org/10.1007/s10723-019-09489-9
- Hou and Zhao (2018) Resource Scheduling and load balancing fusion algorithm with deep learning based on cloud computing 13(3) (pp. 54-72) https://doi.org/10.4018/IJITWE.2018070104
- Rostami and Goli (2021) Green cloud computing with reduced energy consumption in live migration prioritizing services 84(4)
- Mandal et al. (2020) An approach toward design and development of an energy-aware VM selection policy with improved SLA violation in the domain of green cloud computing 76(9) (pp. 7374-7393) https://doi.org/10.1007/s11227-020-03165-6
- Aghasi et al. (2020) A thermal-aware energy-efficient virtual machine placement algorithm based on fuzzy controlled binary gravitational search algorithm (FC-BGSA) (pp. 1015-1033) https://doi.org/10.1007/s10586-021-03476-0
- Malekloo et al. (2018) An energy efficient and SLA compliant approach for resource allocation and consolidation in cloud computing environments (pp. 9-24)
- Jeba et al. (2019) Towards green cloud computing an algorithmic approach for energy minimization in cloud data centers 9(1) (pp. 59-81)
- AlIsmail and Kurdi (2016) Review of energy reduction techniques for green cloud computing (pp. 189-195)
- Garg and Buyya (2012) Green cloud computing and environmental sustainability (pp. 315-340)
10.1007/s40095-022-00497-2