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Published in: The Journal of Supercomputing 1/2023

21-07-2022

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

Authors: Mufeed Ahmed Naji Saif, S. K. Niranjan, Belal Abdullah Hezam Murshed, Fahd A. Ghanem, Ammar Abdullah Qasem Ahmed

Published in: The Journal of Supercomputing | Issue 1/2023

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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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Literature
1.
go back to reference Saif M, Niranjan S, Al-ariki H (2021) Efficient autonomic and elastic resource management techniques in cloud environment: taxonomy and analysis. Wireless Netw 27:2829–2866CrossRef Saif M, Niranjan S, Al-ariki H (2021) Efficient autonomic and elastic resource management techniques in cloud environment: taxonomy and analysis. Wireless Netw 27:2829–2866CrossRef
2.
go back to reference Dehraj P, Sharma A (2020) An empirical assessment of autonomicity for autonomic query optimizers using fuzzy-AHP technique. Appl Soft Comput 90:106137CrossRef Dehraj P, Sharma A (2020) An empirical assessment of autonomicity for autonomic query optimizers using fuzzy-AHP technique. Appl Soft Comput 90:106137CrossRef
3.
go back to reference Dehraj P, Sharma A (2020) An approach to design and develop generic integrated architecture for autonomic software system. Int J Syst Assur Eng Manag 11:690–703CrossRef Dehraj P, Sharma A (2020) An approach to design and develop generic integrated architecture for autonomic software system. Int J Syst Assur Eng Manag 11:690–703CrossRef
4.
go back to reference 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 146:35–51CrossRef 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 146:35–51CrossRef
5.
go back to reference Kosińska J, Zieliński K (2020) Autonomic management framework for cloud-native applications. J Grid Comput 18:779–796CrossRef Kosińska J, Zieliński K (2020) Autonomic management framework for cloud-native applications. J Grid Comput 18:779–796CrossRef
6.
go back to reference Ebadifard F, Babamir S (2020) Autonomic task scheduling algorithm for dynamic workloads through a load balancing technique for the cloud-computing environment. Clust Comput 24:1075–1101CrossRef Ebadifard F, Babamir S (2020) Autonomic task scheduling algorithm for dynamic workloads through a load balancing technique for the cloud-computing environment. Clust Comput 24:1075–1101CrossRef
7.
go back to reference 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 109:689–701CrossRef 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 109:689–701CrossRef
8.
go back to reference Hanafy W, Mohamed A, Salem S (2019) A new infrastructure elasticity control algorithm for containerized cloud. IEEE Access 7:39731–39741CrossRef Hanafy W, Mohamed A, Salem S (2019) A new infrastructure elasticity control algorithm for containerized cloud. IEEE Access 7:39731–39741CrossRef
9.
go back to reference Kehrer S, Blochinger W (2021) Correction to: equilibrium: an elasticity controller for parallel tree search in the cloud. J Supercomput 77:10742–10742CrossRef Kehrer S, Blochinger W (2021) Correction to: equilibrium: an elasticity controller for parallel tree search in the cloud. J Supercomput 77:10742–10742CrossRef
10.
go back to reference Al-Dhuraibi Y, Zalila F, Djarallah N, Merle P (2021) Model-driven elasticity management with OCCI. IEEE Trans Cloud Comput 9:1549–1562CrossRef Al-Dhuraibi Y, Zalila F, Djarallah N, Merle P (2021) Model-driven elasticity management with OCCI. IEEE Trans Cloud Comput 9:1549–1562CrossRef
11.
go back to reference Sridharan R, Domnic S (2020) Network policy aware placement of tasks for elastic applications in IaaS-cloud environment. Clust Comput 24:1381–1396CrossRef Sridharan R, Domnic S (2020) Network policy aware placement of tasks for elastic applications in IaaS-cloud environment. Clust Comput 24:1381–1396CrossRef
12.
go back to reference Ghobaei-Arani M, Shahidinejad A (2020) An efficient resource provisioning approach for analyzing cloud workloads: a metaheuristic-based clustering approach. J Supercomput 77:711–750CrossRef Ghobaei-Arani M, Shahidinejad A (2020) An efficient resource provisioning approach for analyzing cloud workloads: a metaheuristic-based clustering approach. J Supercomput 77:711–750CrossRef
13.
go back to reference Rodriguez M, Buyya R (2018) Scheduling dynamic workloads in multi-tenant scientific workflow as a service platforms. Futur Gener Comput Syst 79:739–750CrossRef Rodriguez M, Buyya R (2018) Scheduling dynamic workloads in multi-tenant scientific workflow as a service platforms. Futur Gener Comput Syst 79:739–750CrossRef
14.
go back to reference Shahidinejad A, Ghobaei-Arani M, Masdari M (2020) Resource provisioning using workload clustering in cloud computing environment: a hybrid approach. Clust Comput 24:319–342CrossRef Shahidinejad A, Ghobaei-Arani M, Masdari M (2020) Resource provisioning using workload clustering in cloud computing environment: a hybrid approach. Clust Comput 24:319–342CrossRef
15.
go back to reference 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 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
16.
go back to reference 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 24:39–50CrossRef 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 24:39–50CrossRef
17.
go back to reference 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 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.
go back to reference 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 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.
go back to reference 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 97:69–89CrossRef 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 97:69–89CrossRef
20.
go back to reference Srinivasan J, Dhas C (2020) Cloud management architecture to improve the resource allocation in cloud IAAS platform. J Ambient Intell Humaniz Comput 12:5397–5404CrossRef Srinivasan J, Dhas C (2020) Cloud management architecture to improve the resource allocation in cloud IAAS platform. J Ambient Intell Humaniz Comput 12:5397–5404CrossRef
21.
go back to reference Mapetu J, Kong L, Chen Z (2020) A dynamic VM consolidation approach based on load balancing using Pearson correlation in cloud computing. J Supercomput 77:5840–5881CrossRef Mapetu J, Kong L, Chen Z (2020) A dynamic VM consolidation approach based on load balancing using Pearson correlation in cloud computing. J Supercomput 77:5840–5881CrossRef
22.
go back to reference 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 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.
go back to reference Kumar J, Saxena D, Singh A, Mohan A (2020) BiPhase adaptive learning-based neural network model for cloud datacenter workload forecasting. Soft Comput 24:14593–14610CrossRef Kumar J, Saxena D, Singh A, Mohan A (2020) BiPhase adaptive learning-based neural network model for cloud datacenter workload forecasting. Soft Comput 24:14593–14610CrossRef
24.
go back to reference 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 88:105940CrossRef 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 88:105940CrossRef
25.
go back to reference 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 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
26.
go back to reference Ghobaei-Arani M (2020) A workload clustering based resource provisioning mechanism using Biogeography based optimization technique in the cloud based systems. Soft Comput 25:3813–3830CrossRef Ghobaei-Arani M (2020) A workload clustering based resource provisioning mechanism using Biogeography based optimization technique in the cloud based systems. Soft Comput 25:3813–3830CrossRef
27.
go back to reference 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 107:351–367CrossRef 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 107:351–367CrossRef
28.
go back to reference Kumar J, Singh A (2019) Cloud datacenter workload estimation using error preventive time series forecasting models. Clust Comput 23:1363–1379CrossRef Kumar J, Singh A (2019) Cloud datacenter workload estimation using error preventive time series forecasting models. Clust Comput 23:1363–1379CrossRef
29.
go back to reference 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 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.
go back to reference Ullah A, Li J, Hussain A (2020) Design and evaluation of a biologically-inspired cloud elasticity framework. Clust Comput 23:3095–3117CrossRef Ullah A, Li J, Hussain A (2020) Design and evaluation of a biologically-inspired cloud elasticity framework. Clust Comput 23:3095–3117CrossRef
31.
go back to reference Khebbeb K, Hameurlain N, Belala F (2020) Formalizing and simulating cross-layer elasticity strategies in Cloud systems. Clust Comput 23:1603–1631CrossRef Khebbeb K, Hameurlain N, Belala F (2020) Formalizing and simulating cross-layer elasticity strategies in Cloud systems. Clust Comput 23:1603–1631CrossRef
32.
go back to reference 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 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.
go back to reference Shahidinejad A, Ghobaei-Arani M, Esmaeili L (2019) An elastic controller using Colored Petri Nets in cloud computing environment. Clust Comput 1–27 Shahidinejad A, Ghobaei-Arani M, Esmaeili L (2019) An elastic controller using Colored Petri Nets in cloud computing environment. Clust Comput 1–27
34.
go back to reference 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 8:118135–118155CrossRef 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 8:118135–118155CrossRef
35.
go back to reference Arul Xavier V, Annadurai S (2018) Chaotic social spider algorithm for load balance aware task scheduling in cloud computing. Clust Comput 22:287–297CrossRef Arul Xavier V, Annadurai S (2018) Chaotic social spider algorithm for load balance aware task scheduling in cloud computing. Clust Comput 22:287–297CrossRef
36.
go back to reference 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 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.
go back to reference 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 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.
go back to reference 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 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.
go back to reference Polepally V, Chatrapati KS (2017) Dragonfly optimization and constraint measure-based load balancing in cloud computing. Clust Comput 22:1099–1111CrossRef Polepally V, Chatrapati KS (2017) Dragonfly optimization and constraint measure-based load balancing in cloud computing. Clust Comput 22:1099–1111CrossRef
40.
go back to reference 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 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
41.
go back to reference 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 9:42081–42089CrossRef 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 9:42081–42089CrossRef
42.
go back to reference Princess GAP, Radhamani AS (2021) A hybrid meta-heuristic for optimal load balancing in cloud computing. J Grid Comput 19(2):1–22 Princess GAP, Radhamani AS (2021) A hybrid meta-heuristic for optimal load balancing in cloud computing. J Grid Comput 19(2):1–22
43.
go back to reference Latchoumi TP, Parthiban L (2022) Quasi oppositional dragonfly algorithm for load balancing in cloud computing environment. Wireless Pers Commun 122(3):2639–2656CrossRef Latchoumi TP, Parthiban L (2022) Quasi oppositional dragonfly algorithm for load balancing in cloud computing environment. Wireless Pers Commun 122(3):2639–2656CrossRef
44.
go back to reference Muteeh A, Sardaraz M, Tahir M (2021) MrLBA: multi-resource load balancing algorithm for cloud computing using ant colony optimization. Clust Comput 24(4):3135–3145CrossRef Muteeh A, Sardaraz M, Tahir M (2021) MrLBA: multi-resource load balancing algorithm for cloud computing using ant colony optimization. Clust Comput 24(4):3135–3145CrossRef
45.
go back to reference Shafiq DA, Jhanjhi NZ, Abdullah A, Alzain MA (2021) A load balancing algorithm for the data centres to optimize cloud computing applications. IEEE Access 9:41731–41744CrossRef Shafiq DA, Jhanjhi NZ, Abdullah A, Alzain MA (2021) A load balancing algorithm for the data centres to optimize cloud computing applications. IEEE Access 9:41731–41744CrossRef
46.
go back to reference Negi S, Rauthan MMS, Vaisla KS, Panwar N (2021) CMODLB: an efficient load balancing approach in cloud computing environment. J Supercomput 77(8):8787–8839CrossRef Negi S, Rauthan MMS, Vaisla KS, Panwar N (2021) CMODLB: an efficient load balancing approach in cloud computing environment. J Supercomput 77(8):8787–8839CrossRef
47.
go back to reference 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 115:497–516CrossRef 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 115:497–516CrossRef
48.
go back to reference 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 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.
go back to reference 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 88:3371–3387CrossRef 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 88:3371–3387CrossRef
50.
go back to reference Eusuff M, Lansey K, Pasha F (2006) Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng Optim 38:129–154MathSciNetCrossRef Eusuff M, Lansey K, Pasha F (2006) Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng Optim 38:129–154MathSciNetCrossRef
51.
go back to reference 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 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.
go back to reference Zhou N, Li F, Xu K, Qi D (2018) Concurrent workflow budget- and deadline-constrained scheduling in heterogeneous distributed environments. Soft Comput 22:7705–7718CrossRef Zhou N, Li F, Xu K, Qi D (2018) Concurrent workflow budget- and deadline-constrained scheduling in heterogeneous distributed environments. Soft Comput 22:7705–7718CrossRef
53.
go back to reference 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 47:505–521CrossRef 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 47:505–521CrossRef
54.
go back to reference 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 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
Metadata
Title
CSO-ILB: chicken swarm optimized inter-cloud load balancer for elastic containerized multi-cloud environment
Authors
Mufeed Ahmed Naji Saif
S. K. Niranjan
Belal Abdullah Hezam Murshed
Fahd A. Ghanem
Ammar Abdullah Qasem Ahmed
Publication date
21-07-2022
Publisher
Springer US
Published in
The Journal of Supercomputing / Issue 1/2023
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-022-04688-w

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