[go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Energy efficient task allocation and consolidation in multicast cloud network

  • Original Paper
  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Information Technology is now a revolution due to its user dependence for various services available over Internet through cloud computing (CC). The usability is now reached to nearly 60% of globally population. The number is increasing rapidly everyday due to its services in low cost. It is becoming a challenge to manage the load, speed, computing, processing with respect to cost in CC. The resources generate enormous heat in computing and effecting the environment adversely. Hence authors have adopted multicasting, load balancing and greening environment using energy efficiency policies to get minimum consumption of energy. In this work we focus on local communication between Simple Storage Service (S3) and Elastic Computing Cloud (EC2) considering the load of virtual machines (VMs). We suggest two algorithms (i) energy-aware task consolidation (EaTC) and (ii) Low Energy Saving Task Consolidation (LESTC) considering dynamic download for different servers. The proposed mechanism also reduces migration cost and the migration mechanism performs better. Overall, the experimental study concludes that multicasting among local servers even in heterogeneous environments open doors for energy efficient optimized load to improve the performance of the system. The proposed mechanism is compared with various existing mechanism of its class and found better.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Velliangiri, S., Manoharn, R., Ramachandran, S., Venkatesan, K., Vani, R., Karthikeyan, P., Kumar, P., Kumar, A., & Dhanabalan, S. S. (2021). An efficient lightweight privacy preserving mechanism for industry 4.0 based on elliptic curve cryptography. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/TII.2021.3139609

    Google Scholar 

  2. Lagen, S., Pascual-Iserte, A., Munoz, O., & Vidal, J. (2018). Energy efficiency in latency-constrained application offloading from mobile clients to multiple virtual machines. IEEE Transactions on Signal Processing, 66(4), 1065–1079. https://doi.org/10.1109/TSP.2017.2778692

    Article  MathSciNet  MATH  Google Scholar 

  3. Arulkumar, V., & Bhalaji, N. (2020). Performance analysis of nature inspired load balancing algorithm in cloud environment. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-019-01655-x

    Google Scholar 

  4. Arulkumar, V., & Bhalaji, N. (2019). Load balancing in cloud computing using water wave algorithm. Concurrency Computation. https://doi.org/10.1002/cpe.5492

    Google Scholar 

  5. Fernández-Cerero, D., Fernández-Montes, A., & Velasco, F. (2018). Productive efficiency of energy-aware data centers. Energies (Basel), 11(8), 1–17. https://doi.org/10.3390/en11082053

    Google Scholar 

  6. Alagarsamy, M., Sundarji, A., Arunachalapandi, A., & Kalyanasundaram, K. (2021). Cost-aware ant colony optimization based model for load balancing in cloud computing. The International Arab Journal of Information Technology, 18(5), 719–729. https://doi.org/10.34028/iajit/18/5/12

    Article  Google Scholar 

  7. Bhoi, A. K., Kabat, M. R., Nayak, S. C., & Palai, G. (2022). Renewable energy source based quality of service (QoS)-aware routing mechanism in cloud network. Wireless Networks, 28(4), 1703–1718. https://doi.org/10.1007/s11276-022-02935-9

    Article  Google Scholar 

  8. Sathyadevaki, R., Sundar, D. S., & Raja, A. S. (2018). Photonic crystal 4 × 4 dynamic hitless routers for integrated photonic NoCs. Photonic Network Communications, 36(1), 82–95. https://doi.org/10.1007/s11107-018-0758-8

    Article  Google Scholar 

  9. Borylo, P., Tornatore, M., Jaglarz, P., Shahriar, N., Chołda, P., & Boutaba, R. (2020). Latency and energy-aware provisioning of network slices in cloud networks. Computer Communications, 157, 1–19. https://doi.org/10.1016/j.comcom.2020.03.050

    Article  Google Scholar 

  10. Brown, R., Masanet, E., Nordman, B., Tschudi, B., Shehabi, A., Stanley, J., Koomey, J., Sartor, D., & Chan, P. (2008). Report to Congress on Server and Data Center Energy Efficiency Public Law 109–431. Environmental Energy Technologies Division Alliance to Save Energy ICF Incorporated, August, 2008.

  11. Rodge, A. S., Pramanik, C., Bose, J., & Soni, S. K. (2015). Multicast routing with load balancing using Amazon web service. In 11th IEEE India conference: Emerging trends and innovation in technology (INDICON 2014). https://doi.org/10.1109/INDICON.2014.7030543.

  12. Parida, S., Nayak, S. C., & Priyadarshi, P. (2018). Petri Net: Design and analysis of parallel task scheduling algorithm. In: Proceedings in ICACIE (pp. 765–776). Springer.

  13. Sobhanayak, S. (2019). Energy-efficient task scheduling in cloud data center—a temperature aware approach. In 2019 3rd International conference on electronics, communication and aerospace technology (ICECA) (pp. 1205–1208).

  14. Paya, A., & Marinescu, D. C. (2017). Energy-aware load balancing and application scaling for the cloud ecosystem. IEEE Transactions on Cloud Computing, 5(1), 15–27. https://doi.org/10.1109/TCC.2015.2396059

    Article  Google Scholar 

  15. Bhoi, A. K., Ranjan, M., Purna, K., & Sethi, C. An efficient QoS-aware multi objective load balancing and optimized routing in cloud data center networks.

  16. SimãoFilho, M., Pinheiro, P. R., Albuquerque, A. B., Simão, R. P. S., Azevedo, R. S. N., & Nunes, L. C. (2019). A multicriteria approach to support task allocation in projects of distributed software development. Complexity. https://doi.org/10.1155/2019/3926798

    Article  Google Scholar 

  17. Satheesh Kumar, M., Vimal, S., Jhanjhi, N. Z., Dhanabalan, S. S., & Alhumyani, H. A. (2021). Blockchain based peer to peer communication in autonomous drone operation. Energy Reports, 7, 7925–7939. https://doi.org/10.1016/j.egyr.2021.08.073

    Article  Google Scholar 

  18. Nayak, S. C., & Tripathy, C. (2019). An improved task scheduling mechanism using multi-criteria decision making in cloud computing. International Journal of Information Technology and Web Engineering, 14(2), 92–117. https://doi.org/10.4018/IJITWE.2019040106

    Article  Google Scholar 

  19. Beloglazov, A., & Buyya, R. (2013). Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Transactions on Parallel and Distributed Systems, 24(7), 1366–1379. https://doi.org/10.1109/TPDS.2012.240

    Article  Google Scholar 

  20. Jiang, C., Han, G., Lin, J., Jia, G., Shi, W., & Wan, J. (2019). Characteristics of co-allocated online services and batch jobs in internet data centers: A case study from Alibaba Cloud. IEEE Access, 7, 22495–22508. https://doi.org/10.1109/ACCESS.2019.2897898

    Article  Google Scholar 

  21. Qiu, Y., Jiang, C., Wang, Y., Ou, D., Li, Y., & Wan, J. (2019). Energy aware virtual machine scheduling in data centers. Energies (Basel). https://doi.org/10.3390/en12040646

    Google Scholar 

  22. Hsu, C. H., Slagter, K. D., Chen, S. C., & Chung, Y. C. (2014). Optimizing energy consumption with task consolidation in clouds. Information Sciences, 258, 452–462. https://doi.org/10.1016/j.ins.2012.10.041

    Article  Google Scholar 

  23. Panda, S. K., & Jana, P. K. (2016). An efficient task consolidation algorithm for cloud computing systems. Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) (Vol. 9581, pp. 61–74). Springer. https://doi.org/10.1007/978-3-319-28034-9_8

  24. Deelman, E., Singh, G., Livny, M., Berriman, B., & Good, J. (2008). The cost of doing science on the cloud: The montage example. In 2008 SC—International conference for high performance computing, networking, storage and analysis (SC 2008), November, 2008. https://doi.org/10.1109/SC.2008.5217932.

  25. Garfinkel, S. L. (2007). An evaluation of Amazon’s grid computing services: EC2, S3, and SQS. The Harvard Community has made this article openly available. Please share how this access benefits you. Your story matters (p. 15).

  26. Cheocherngngarn, T., Andrian, J., & Pan, D. (2012). Deployment of a hybrid multicast switch in energy-aware data center network: A case of fat-tree topology. ISRN Communications and Networking, 2012, 1–10. https://doi.org/10.5402/2012/209573

    Article  Google Scholar 

  27. Jiang, D., Xu, Z., Li, W., Yao, C., Lv, Z., & Li, T. (2016). An energy-efficient multicast algorithm with maximum network throughput in multi-hop wireless networks. Journal of Communications and Networks, 18(5), 713–724. https://doi.org/10.1109/JCN.2016.000101

    Article  Google Scholar 

  28. Lien, C.-H., Liu, M. F., Bai, Y.-W., Lin, C. H., & Lin, M.-B. (2007). Measurement by the software design for the power consumption of streaming media servers. In Proceedings of the IEEE instrumentation and measurement technology conference, 2006 (IMTC 2006), May 2007 (pp. 1597–1602). https://doi.org/10.1109/imtc.2006.328685.

  29. Xu, X., Zhang, X., Khan, M., Dou, W., Xue, S., & Yu, S. (2020). A balanced virtual machine scheduling method for energy-performance trade-offs in cyber-physical cloud systems. Future Generation Computer Systems, 105, 789–799. https://doi.org/10.1016/j.future.2017.08.057

    Article  Google Scholar 

  30. Armstrong, D., Djemame, K., & Kavanagh, R. (2017). Towards energy aware cloud computing application construction. Journal of Cloud Computing. https://doi.org/10.1186/s13677-017-0083-2

    Article  Google Scholar 

  31. Itani, W., Ghali, C., Kayssi, A., Chehab, A., & Elhajj, I. (2015). G-Route: An energy-aware service routing protocol for green cloud computing. Cluster Computing, 18(2), 889–908. https://doi.org/10.1007/s10586-015-0443-y

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, VSS University of Technology, Burla, Sambalpur, 768018, India

    Ashok Kumar Bhoi & Manas Ranjan Kabat

  2. Department of Computer Science and Engineering, Gandhi Institute for Technological Advancement, Bhubaneswar, India

    Suvendu Chandan Nayak

  3. Department of Electronics and Communication Engineering, Gandhi Institute for Technological Advancement, Bhubaneswar, India

    G. Palai

Authors
  1. Ashok Kumar Bhoi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manas Ranjan Kabat
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Suvendu Chandan Nayak
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. G. Palai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to G. Palai.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bhoi, A.K., Kabat, M.R., Nayak, S.C. et al. Energy efficient task allocation and consolidation in multicast cloud network. Wireless Netw 28, 3349–3366 (2022). https://doi.org/10.1007/s11276-022-03029-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-022-03029-2

Keywords