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Energy Optimisation in a Cloud Infrastructure Using Ant Colony Optimiser

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Mobile Computing and Sustainable Informatics

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 166))

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Abstract

Cloud services in a few decades have received considerable attention resulting in the quest for an efficient infrastructure to support the high demands from clients. In meeting clients’ needs, there is also the need to manage the substantial financial cost associated with energy consumption in these data centres. In this study, an ant colony optimiser was proposed to manage cloudlets’ scheduling effectively. Implementing the optimiser in a CloudSim comparatively reveals significant improvement in energy consumption over the first come,-first serve algorithm initially proposed by the authors of CloudSim.

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References

  1. Tchernykh A, Schwiegelsohn U, Alexandrov V, Talbi EG (2015) Towards understanding uncertainty in cloud computing resource provisioning. Procedia Comput Sci 51:1772–1781

    Article  Google Scholar 

  2. Nodari A, Nurminen JK, Frühwirth C (2016) Inventory theory applied to cost optimization in cloud computing. In: Proceedings of the 31st annual ACM symposium on applied computing, pp 470–473

    Google Scholar 

  3. Ni J, Bai X (2017) A review of air conditioning energy performance in data centers. Renew Sustain Energy Rev 67:625–640

    Article  Google Scholar 

  4. Devi DC, Uthariaraj VR (2016) Load balancing in cloud computing environment using improved weighted round robin algorithm for non pre-emptive dependent tasks

    Google Scholar 

  5. Koronen C, Åhman M, Nilsson LJ (2020) Data centres in future European energy systems—energy efficiency, integration and policy. Energ Effi 13(1):129–144

    Article  Google Scholar 

  6. Ismaeel S, Karim R, Miri A (2018) Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres. J Cloud Comput 7(1):1–28

    Article  Google Scholar 

  7. Katal A, Dahiya S, Choudhury T (2022) Energy efficiency in cloud computing data centers: a survey on software technologies. Cluster Comput 1–31

    Google Scholar 

  8. Sabbaghi A, Vaidyanathan G (2012) Green information technology and sustainability: a conceptual taxonomy

    Google Scholar 

  9. Beitelmal H, Fabris D (2014) Servers and data centers energy performance metrics. Energy Build. 80:562–569

    Article  Google Scholar 

  10. Cerotti D, Gribaudo M, Piazzolla P, Pinciroli R, Serazzi G (2016) Modeling power consumption in multicore CPUs with multithreading and frequency scaling. Springer, Cham, pp 81–90

    Google Scholar 

  11. Krishnadoss P, Jacob P (2018) OCSA: task scheduling algorithm in cloud computing environment. Intern J Intell Eng Syst 11(3):271–279

    Google Scholar 

  12. Dou H, Qi Y (2017) An online electricity cost budgeting algorithm for maximising green energy usage across data centers. Front Comput Sci 1–14

    Google Scholar 

  13. Dhurandher SK, Obaidat MS, Woungang I, Agarwal P, Gupta A, Gupta P (2014) A cluster-based load balancing algorithm in cloud computing. In: 2014 IEEE international conference communication (ICC), pp 2921–2925

    Google Scholar 

  14. Tong Z, Chen H, Deng X, Li K, Li K (2019) A novel task scheduling scheme in a cloud computing environment using hybrid biogeography-based optimization. Soft Comput 23(21):11035–11054

    Article  Google Scholar 

  15. Sun Q, Shen Q, Li C, Wu Z (2016) SeLance: secure load balancing of virtual machines in cloud. IEEE Trustcom/BigDataSE/ISPA 2016:662–669

    Article  Google Scholar 

  16. Domanal SG, Reddy GR, Damanal SG (2014) Optimal load balancing in cloud computing by efficient utilisation of virtual machines. Int J Adv Technol Eng Sci 3(2):122–129

    Google Scholar 

  17. Dorigo M, Stützle T (2019) Ant colony optimization: overview and recent advances. In: Handbook of metaheuristics, pp 311–351

    Google Scholar 

  18. Milani AS, Navimipour NJ (2016) Load balancing mechanisms and techniques in the cloud environments: systematic literature review and future trends. J Netw Comput Appl 71:86–98

    Article  Google Scholar 

  19. Jin C, Bai X, Yang C, Mao W, Xu X (2020) A review of power consumption models of servers in data centers. Appl Energy 265:114806

    Google Scholar 

  20. Xianfeng Y, HongTao L (2015) Load balancing of virtual machines in cloud computing environment using improved ant colony algorithm. Int J Grid Distrib Comput 8(6):19–30

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Ghana, Legon, Accra, Ghana

    Ebenezer Owusu, Justice Kwame Appati & Solomon Mensah

  2. Department of Management Science and Economics, University of Electronic Science and Technology of China, Chengdu, China

    Godwin Banafo Akrong

Authors
  1. Ebenezer Owusu
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  2. Godwin Banafo Akrong
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  3. Justice Kwame Appati
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  4. Solomon Mensah
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Corresponding author

Correspondence to Justice Kwame Appati .

Editor information

Editors and Affiliations

  1. Pulchowk Campus, Tribhuvan University, Kathmandu, Nepal

    Subarna Shakya

  2. Department of Computer Science, International Hellenic University, Kavala, Greece

    George Papakostas

  3. Computer Science Department, Texas Southern University, Houston, TX, USA

    Khaled A. Kamel

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Owusu, E., Akrong, G.B., Appati, J.K., Mensah, S. (2023). Energy Optimisation in a Cloud Infrastructure Using Ant Colony Optimiser. In: Shakya, S., Papakostas, G., Kamel, K.A. (eds) Mobile Computing and Sustainable Informatics. Lecture Notes on Data Engineering and Communications Technologies, vol 166. Springer, Singapore. https://doi.org/10.1007/978-981-99-0835-6_32

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