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CSO-ILB: chicken swarm optimized inter-cloud load balancer for elastic containerized multi-cloud environment

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Abstract

The dynamic nature of the cloud environment increases the complexity of managing its resources and the distribution of user workload between the available containers in the data center. However, the workload must be balanced to improve the cloud system’s overall performance. Generally, most of the existing load balancing techniques suffer from performance degradation due to the communication overheads among the containers. Moreover, less attention is given to stabilize the load in a multi-cloud environment. Therefore, to overcome this problem, there is a need to develop an elastic load balancing method to improve the performance of cloud systems. This paper proposed an autonomic CSO-ILB load balancer to ensure the elasticity of the cloud system and balance the user workload among the available containers in a multi-cloud environment. The concept of multi-loop has been utilized in our approach to enabling efficient self-management before load balancing. The tasks are scheduled to the containers using an extended scheduling algorithm called Deadline-Constrained Make-span Minimization for Multi-Task Scheduling (DCMM-MTS). Based on the task scheduling, the load in each container is computed and then balanced using the proposed load balancer algorithm CSO-ILB. The proposed approach is evaluated in the Container CloudSim platform, and the performance is compared with the existing meta-heuristic algorithms such as Ant Colony Optimization, Bee Colony Optimization, Shuffled Frog Leaping Algorithm and Cat Swarm Optimization (CSO). The simulations proved that the proposed approach outperformed the other approaches in terms of reliability, CPU utilization, make-span, energy utilization, response time, execution cost, idle time, and task migration.

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References

  1. Saif M, Niranjan S, Al-ariki H (2021) Efficient autonomic and elastic resource management techniques in cloud environment: taxonomy and analysis. Wireless Netw 27:2829–2866

    Article  Google Scholar 

  2. Dehraj P, Sharma A (2020) An empirical assessment of autonomicity for autonomic query optimizers using fuzzy-AHP technique. Appl Soft Comput 90:106137

    Article  Google Scholar 

  3. Dehraj P, Sharma A (2020) An approach to design and develop generic integrated architecture for autonomic software system. Int J Syst Assur Eng Manag 11:690–703

    Article  Google Scholar 

  4. Jin T, Zhang F, Sun Q, Romanus M, Bui H, Parashar M (2020) Towards autonomic data management for staging-based coupled scientific workflows. J Parallel Distrib Comput 146:35–51

    Article  Google Scholar 

  5. Kosińska J, Zieliński K (2020) Autonomic management framework for cloud-native applications. J Grid Comput 18:779–796

    Article  Google Scholar 

  6. Ebadifard F, Babamir S (2020) Autonomic task scheduling algorithm for dynamic workloads through a load balancing technique for the cloud-computing environment. Clust Comput 24:1075–1101

    Article  Google Scholar 

  7. Da Rosa RR, Correa E, Gomes M, da Costa C (2020) Enhancing performance of IoT applications with load prediction and cloud elasticity. Futur Gener Comput Syst 109:689–701

    Article  Google Scholar 

  8. Hanafy W, Mohamed A, Salem S (2019) A new infrastructure elasticity control algorithm for containerized cloud. IEEE Access 7:39731–39741

    Article  Google Scholar 

  9. Kehrer S, Blochinger W (2021) Correction to: equilibrium: an elasticity controller for parallel tree search in the cloud. J Supercomput 77:10742–10742

    Article  Google Scholar 

  10. Al-Dhuraibi Y, Zalila F, Djarallah N, Merle P (2021) Model-driven elasticity management with OCCI. IEEE Trans Cloud Comput 9:1549–1562

    Article  Google Scholar 

  11. Sridharan R, Domnic S (2020) Network policy aware placement of tasks for elastic applications in IaaS-cloud environment. Clust Comput 24:1381–1396

    Article  Google Scholar 

  12. Ghobaei-Arani M, Shahidinejad A (2020) An efficient resource provisioning approach for analyzing cloud workloads: a metaheuristic-based clustering approach. J Supercomput 77:711–750

    Article  Google Scholar 

  13. Rodriguez M, Buyya R (2018) Scheduling dynamic workloads in multi-tenant scientific workflow as a service platforms. Futur Gener Comput Syst 79:739–750

    Article  Google Scholar 

  14. Shahidinejad A, Ghobaei-Arani M, Masdari M (2020) Resource provisioning using workload clustering in cloud computing environment: a hybrid approach. Clust Comput 24:319–342

    Article  Google Scholar 

  15. Rawat P, Gupta P, Dimri P, Saroha G (2020) Power efficient resource provisioning for cloud infrastructure using bio-inspired artificial neural network model. Sustain Comput Inform Syst 28:100431

    Google Scholar 

  16. Nastic S, Morichetta A, Pusztai T, Dustdar S, Ding X, Vij D, Xiong Y, Dustdar S (2020) SLOC: service level objectives for next generation cloud computing. IEEE Internet Comput 24:39–50

    Article  Google Scholar 

  17. Tadakamalla V, Menasce D (2020) Autonomic Elasticity Control for Multi-server Queues under Generic Workload Surges in Cloud Environments. IEEE Trans Cloud Comput 1–1

  18. Fei B, Zhu X, Liu D, Chen J, Bao W, Liu L (2020) Elastic resource provisioning using data clustering in cloud service platform. IEEE Trans Serv Comput 1–1

  19. Jrad A, Bhiri S, Tata S (2019) STRATFram: a framework for describing and evaluating elasticity strategies for service-based business processes in the cloud. Futur Gener Comput Syst 97:69–89

    Article  Google Scholar 

  20. Srinivasan J, Dhas C (2020) Cloud management architecture to improve the resource allocation in cloud IAAS platform. J Ambient Intell Humaniz Comput 12:5397–5404

    Article  Google Scholar 

  21. Mapetu J, Kong L, Chen Z (2020) A dynamic VM consolidation approach based on load balancing using Pearson correlation in cloud computing. J Supercomput 77:5840–5881

    Article  Google Scholar 

  22. Tamilarasi P, Akila D (2020) Task Allocation and Re-allocation for Big Data Applications in Cloud Computing Environments. In Intelligent Computing and Innovation on Data Science. Springer, Singapore 679–686

  23. Kumar J, Saxena D, Singh A, Mohan A (2020) BiPhase adaptive learning-based neural network model for cloud datacenter workload forecasting. Soft Comput 24:14593–14610

    Article  Google Scholar 

  24. Jeddi S, Sharifian S (2020) A hybrid wavelet decomposer and GMDH-ELM ensemble model for Network function virtualization workload forecasting in cloud computing. Appl Soft Comput 88:105940

    Article  Google Scholar 

  25. Mishra S, Sahoo B, Parida P (2020) Load balancing in cloud computing: a big picture. J King Saud Univ Comput Inf Sci 32:149–158

    Google Scholar 

  26. Ghobaei-Arani M (2020) A workload clustering based resource provisioning mechanism using Biogeography based optimization technique in the cloud based systems. Soft Comput 25:3813–3830

    Article  Google Scholar 

  27. Liang H, Du Y, Gao E, Sun J (2020) Cost-driven scheduling of service processes in hybrid cloud with VM deployment and interval-based charging. Futur Gener Comput Syst 107:351–367

    Article  Google Scholar 

  28. Kumar J, Singh A (2019) Cloud datacenter workload estimation using error preventive time series forecasting models. Clust Comput 23:1363–1379

    Article  Google Scholar 

  29. Kim I, Wang W, Qi Y, Humphrey M (2020) Forecasting Cloud Application Workloads with CloudInsight for Predictive Resource Management. IEEE Trans Cloud Comput 1–1

  30. Ullah A, Li J, Hussain A (2020) Design and evaluation of a biologically-inspired cloud elasticity framework. Clust Comput 23:3095–3117

    Article  Google Scholar 

  31. Khebbeb K, Hameurlain N, Belala F (2020) Formalizing and simulating cross-layer elasticity strategies in Cloud systems. Clust Comput 23:1603–1631

    Article  Google Scholar 

  32. Singh P, Kaur A, Gupta P, Gill S, Jyoti K (2020) RHAS: robust hybrid auto-scaling for web applications in cloud computing. Clust Comput 1–21

  33. Shahidinejad A, Ghobaei-Arani M, Esmaeili L (2019) An elastic controller using Colored Petri Nets in cloud computing environment. Clust Comput 1–27

  34. Junaid M, Sohail A, Ahmed A, Baz A, Khan I, Alhakami H (2020) A hybrid model for load balancing in cloud using file type formatting. IEEE Access 8:118135–118155

    Article  Google Scholar 

  35. Arul Xavier V, Annadurai S (2018) Chaotic social spider algorithm for load balance aware task scheduling in cloud computing. Clust Comput 22:287–297

    Article  Google Scholar 

  36. Arvindhan M, Anand A (2019) Scheming a proficient auto scaling technique for minimizing response time in load balancing on amazon AWS cloud. SSRN Electron J

  37. Pourghaffari A, Barari M, Kashi SS (2019) An efficient method for allocating resources in a cloud computing environment with a load balancing approach. Concurr Comput Pract Exp e5285

  38. Gamal M, Rizk R, Mahdi H, Elhady B (2017) Bio-inspired load balancing algorithm in cloud computing. In: International Conference on Advanced Intelligent Systems and Informatics. Springer, Cham 579–589

  39. Polepally V, Chatrapati KS (2017) Dragonfly optimization and constraint measure-based load balancing in cloud computing. Clust Comput 22:1099–1111

    Article  Google Scholar 

  40. Jain RK, Singh YP, Sharma S (2020) Improve the efficiency of intercloud load balancing using directed acyclic graph for vertical scaling. Sci J India 5(1):76–81

    Google Scholar 

  41. Razzaq MA, Mahar JA, Ahmad M, Saher N, Mehmood AGS (2021) Choi hybrid auto-scaled service-cloud-based predictive workload modeling and analysis for smart campus system. IEEE Access 9:42081–42089

    Article  Google Scholar 

  42. Princess GAP, Radhamani AS (2021) A hybrid meta-heuristic for optimal load balancing in cloud computing. J Grid Comput 19(2):1–22

    Google Scholar 

  43. Latchoumi TP, Parthiban L (2022) Quasi oppositional dragonfly algorithm for load balancing in cloud computing environment. Wireless Pers Commun 122(3):2639–2656

    Article  Google Scholar 

  44. Muteeh A, Sardaraz M, Tahir M (2021) MrLBA: multi-resource load balancing algorithm for cloud computing using ant colony optimization. Clust Comput 24(4):3135–3145

    Article  Google Scholar 

  45. Shafiq DA, Jhanjhi NZ, Abdullah A, Alzain MA (2021) A load balancing algorithm for the data centres to optimize cloud computing applications. IEEE Access 9:41731–41744

    Article  Google Scholar 

  46. Negi S, Rauthan MMS, Vaisla KS, Panwar N (2021) CMODLB: an efficient load balancing approach in cloud computing environment. J Supercomput 77(8):8787–8839

    Article  Google Scholar 

  47. Miao Z, Yong P, Mei Y, Quanjun Y, Xu X (2021) A discrete PSO-based static load balancing algorithm for distributed simulations in a cloud environment. Futur Gener Comput Syst 115:497–516

    Article  Google Scholar 

  48. Lal A, Krishna CR (2018) Critical path-based ant colony optimization for scientific workflow scheduling in cloud computing under deadline constraint. In Ambient Communications and Computer Systems, Springer, Singapore 447–461

  49. Zhou J, Yao X (2016) A hybrid artificial bee colony algorithm for optimal selection of QoS-based cloud manufacturing service composition. Int J Adv Manuf Technol 88:3371–3387

    Article  Google Scholar 

  50. Eusuff M, Lansey K, Pasha F (2006) Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng Optim 38:129–154

    Article  MathSciNet  Google Scholar 

  51. Gabi D, Ismail AS, Zainal A, Zakaria Z, Abraham A, Dankolo NM (2020) Cloud customers service selection scheme based on improved conventional cat swarm optimization. Neural Comput Appl 1–22

  52. Zhou N, Li F, Xu K, Qi D (2018) Concurrent workflow budget- and deadline-constrained scheduling in heterogeneous distributed environments. Soft Comput 22:7705–7718

    Article  Google Scholar 

  53. Piraghaj S, Dastjerdi A, Calheiros R, Buyya R (2016) ContainerCloudSim: an environment for modeling and simulation of containers in cloud data centers. Softw Pract Exp 47:505–521

    Article  Google Scholar 

  54. Siqi S, Beek VV, Iosup A (2015) Statistical Characterization of Business-Critical Workloads Hosted in Cloud Datacenters, the 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), ShenZhen, China

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Authors and Affiliations

  1. Department of Computer Applications, Sri Jayachamarajendra College of Engineering, VTU, Mysore, India

    Mufeed Ahmed Naji Saif & S. K. Niranjan

  2. Department of Studies in Computer Science, University of Mysore, Mysore, India

    Belal Abdullah Hezam Murshed

  3. Department of Computer Science, College of Engineering & IT, University of Amran, Amran, Yemen

    Belal Abdullah Hezam Murshed

  4. Department of Computer Science and Engineering, PES College of Engineering, University of Mysore, Mandaya, India

    Fahd A. Ghanem

  5. Department of Computer Science, College of Education-Zabid, Hodeidah University, Hodeidah, Yemen

    Fahd A. Ghanem

  6. Facality of Engineering, International University of Technology Twintech, Sana’a, Yemen

    Ammar Abdullah Qasem Ahmed

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  1. Mufeed Ahmed Naji Saif
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  2. S. K. Niranjan
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  4. Fahd A. Ghanem
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Correspondence to Mufeed Ahmed Naji Saif.

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Saif, M.A.N., Niranjan, S.K., Murshed, B.A.H. et al. CSO-ILB: chicken swarm optimized inter-cloud load balancer for elastic containerized multi-cloud environment. J Supercomput 79, 1111–1155 (2023). https://doi.org/10.1007/s11227-022-04688-w

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