Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Multi-agent QoS-aware autonomic resource provisioning framework for elastic BPM in containerized multi-cloud environment

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Cloud computing enables businesses to improve their market competitiveness, enabling instant and easy access to a pool of virtualized and distributed resources such as virtual machines (VM) and containers for executing their business operations efficiently. Though the cloud enables the deployment and management of business processes (BPs), it is challenging to deal with the enormous fluctuating resource demands and ensure smooth execution of business operations in containerized multi-cloud. Therefore, there is a need to ensure elastic provisioning of resources to tackle the over and under-provisioning problems and satisfy the objectives of cloud providers and end-users considering the quality of service (QoS) and service level agreement (SLA) constraints. In this article, an efficient multi-agent autonomic resource provisioning framework is proposed to ensure the effective execution of BPs in a containerized multi-cloud environment with guaranteed QoS. To improve the performance and ensure elastic resource provisioning, autonomic computing is utilized to monitor the resource usage and predict the future resource demands, then resources are scaled based on demand. Initially, the required resources for executing the incoming workloads are identified by clustering the workloads into CPU and I/O intensive, and the local agent achieves this with the help of an initialization algorithm and K-means clustering. Then, the analysis phase predicts the workload demand using the proposed enhanced deep stacked auto-encoder (EDSAE), further, the containers are scaled based on the prediction outcomes, finally, the multi-objective termite colony optimization (MOTCO) algorithm is used by the global agent to find suitable containers for executing the clustered workloads. The proposed framework has been implemented in the Container Cloudsim platform and evaluated using the business workload traces. The overall simulation results proved the effectiveness of the proposed approach compared to other approaches in terms of SLA violation rate, CPU utilization, response time, execution cost, energy consumption, make-span, and throughput.

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data Availability Statement

Data sharing does not apply to this article as no new data were created or analyzed in this study.

References

  • Abrol P, Gupta S, Singh S (2020) A QoS Aware Resource Placement Approach Inspired on the Behavior of the Social Spider Mating Strategy in the Cloud Environment. Wirel Personal Commun 1–39

  • Asghari A, Sohrabi MK (2021) Combined use of coral reefs optimization and multi-agent deep Q-network for energy-aware resource provisioning in cloud data centers using DVFS technique. Cluster Comput 1–22

  • Asghari A, Sohrabi MK, Yaghmaee F (2020) Online scheduling of dependent tasks of cloud’s workflows to enhance resource utilization and reduce the makespan using multiple reinforcement learning-based agents. Soft Computing, 1–23

  • Asghari Ali, Sohrabi MK, Yaghmaee F (2021) Task scheduling, resource provisioning, and load balancing on scientific workflows using parallel SARSA reinforcement learning agents and genetic algorithm. J Supercomput 77(3):2800–2828

    Google Scholar 

  • Ashraf A, Porres I (2018) Multi-objective dynamic virtual machine consolidation in the cloud using ant colony system. Int J Parallel Emergent Distrib Syst 33(1):103–120

    Google Scholar 

  • Benifa JVB, Dejey D (2019) Rlpas: Reinforcement learning-based proactive auto-scaler for resource provisioning in cloud environment. Mobile Netw Appl 24(4):1348–1363

    Google Scholar 

  • Bhardwaj T, Sharma SC (2018) Fuzzy logic-based elasticity controller for autonomic resource provisioning in parallel scientific applications: a cloud computing perspective. Comput Electr Eng 70:1049–1073

    Google Scholar 

  • Boukadi K, Grati R, Rekik M, Ben-Abdallah H (2019) Business process outsourcing to cloud containers: how to find the optimal deployment? Futur Gen Comput Syst 97:397–408

    Google Scholar 

  • Ding W, Luo F, Gu C and Lu H (2019) QARPF: A QoS-Aware Active Resource Provisioning Framework Based on OpenStack. In: 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), IEEE, 1568–1576

  • Faragardi HR, Sedghpour MRS, Fazliahmadi S, Fahringer T, Rasouli N (2019) GRP-HEFT: a budget-constrained resource provisioning scheme for workflow scheduling in IaaS clouds. IEEE Trans Parallel Distrib Syst 31(6):1239–1254

    Google Scholar 

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

  • Feng D, Wu Z, Zuo D, Zhang Z (2019) ERP: an elastic resource provisioning approach for cloud applications. PLoS ONE 14(4):e0216067

    Google Scholar 

  • Ghobaei-Arani M (2021) A workload clustering based resource provisioning mechanism using Biogeography based optimization technique in the cloud based systems. Soft Comput 25(5):3813–3830

    Google Scholar 

  • Ghobaei-Arani M, Shahidinejad A (2021) An efficient resource provisioning approach for analyzing cloud workloads: a metaheuristic-based clustering approach. J Supercomput 77(1):711–750

    Google Scholar 

  • Ghobaei-Arani M, Jabbehdari S, Pourmina MA (2018) An autonomic resource provisioning approach for service-based cloud applications: a hybrid approach. Futur Gen Comput Syst 78:191–210

    Google Scholar 

  • Ghobaei-Arani M, Khorsand R, Ramezanpour M (2019) An autonomous resource provisioning framework for massively multiplayer online games in cloud environment. J Netw Comput Appl 142:76–97

    Google Scholar 

  • Gill SS, Buyya R (2019) Resource provisioning based scheduling framework for execution of heterogeneous and clustered workloads in clouds: from fundamental to autonomic offering. J Grid Comput 17(3):385–417

    Google Scholar 

  • Gill SS, Buyya R, Chana I, Singh M, Abraham A (2018) BULLET: particle swarm optimization based scheduling technique for provisioned cloud resources. J Netw Syst Manag 26(2):361–400

    Google Scholar 

  • Guerrero C, Lera I, Juiz C (2018) Genetic algorithm for multi-objective optimization of container allocation in cloud architecture. J Grid Comput 16(1):113–135

    Google Scholar 

  • Halima RB, Kallel S, Gaaloul W, Maamar Z, Jmaiel M (2020) Toward a correct and optimal time-aware cloud resource allocation to business processes. Futur Gen Comput Syst 112:751–766

    Google Scholar 

  • Hanafy WA, Mohamed AE, Salem SA (2019) A new infrastructure elasticity control algorithm for containerized cloud. IEEE Access 7:39731–39741

    Google Scholar 

  • He L, Qian Z (2020) Intent-based resource matching strategy in cloud. Inf Sci 538:1–18

    MathSciNet  Google Scholar 

  • Hsieh M-J, Chang C-R, Ho L-Y, Wu J-J and Liu P (2011) SQLMR: A scalable database management system for cloud computing. In 2011 International Conference on Parallel Processing, IEEE, 315–324. http://gwa.ewi.tudelft.nl/datasets/gwa-t-12-bitbrains

  • Janagoudar NV, Narayan DG, Mulla MM (2020) Multi-objective scheduling using logistic regression for openstack-based cloud. Procedia Comput Sci 171:1429–1438

    Google Scholar 

  • Jyoti A, Shrimali M (2020) Dynamic provisioning of resources based on load balancing and service broker policy in cloud computing. Clust Comput 23(1):377–395

    Google Scholar 

  • Khorsand R, Ghobaei-Arani M, Ramezanpour M (2018) FAHP approach for autonomic resource provisioning of multitier applications in cloud computing environments. Softw Pract Exp 48(12):2147–2173

    Google Scholar 

  • Khorsand R, Ghobaei-Arani M, Ramezanpour M (2019) A self-learning fuzzy approach for proactive resource provisioning in cloud environment. Softw Pract Exp 49(11):1618–1642

    Google Scholar 

  • Kirthica S, Sridhar R (2018) A residue-based approach for resource provisioning by horizontal scaling across heterogeneous clouds. Int J Approx Reason 101:88–106

    Google Scholar 

  • Kumar M, Sharma SC, Goel S, Mishra SK, Husain A (2020) Autonomic cloud resource provisioning and scheduling using meta-heuristic algorithm. Neural Comput Appl 32(24):18285–18303

    Google Scholar 

  • Li J-q, Han Y-q (2020) A hybrid multi-objective artificial bee colony algorithm for flexible task scheduling problems in cloud computing system. Cluster Comput 23(4):2483–2499

    Google Scholar 

  • Luu Q-T, Kerboeuf S, Kieffer M (2021) Uncertainty-aware resource provisioning for network slicing. IEEE Trans Netw Serv Manag 18(1):79–93

    Google Scholar 

  • Madni SHH, Latiff MSA, Ali J (2019) Multi-objective-oriented cuckoo search optimization-based resource scheduling algorithm for clouds. Arab J Sci Eng 44(4):3585–3602

    Google Scholar 

  • Mazidi A, Golsorkhtabaramiri M, Tabari MY (2020) Autonomic resource provisioning for multilayer cloud applications with K‐nearest neighbor resource scaling and priority‐based resource allocation. Softw Pract Exp

  • Mazidi A, Mahdavi M, Roshanfar F (2021) An autonomic decision tree-based and deadline-constraint resource provisioning in cloud applications. Concurr Comput Pract Exp 33(10):e6196

    Google Scholar 

  • Moreno-Vozmediano R, Montero RS, Huedo E, Llorente IM (2019) Efficient resource provisioning for elastic Cloud services based on machine learning techniques. J Cloud Comput 8(1):1–18

    Google Scholar 

  • Nagarajan R, Thirunavukarasu R (2018) A review on intelligent cloud broker for effective service provisioning in cloud. In: 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), IEEE, 519–524.

  • Ostad-Ali-Askari K, Shayan M (2021) Subsurface drain spacing in the unsteady conditions by HYDRUS-3D and artificial neural networks. Arab J Geosci 14(18):1–14

    Google Scholar 

  • Ostad-Ali-Askari K, Shayannejad M (2021) Computation of subsurface drain spacing in the unsteady conditions using Artificial Neural Networks (ANN). Appl Water Sci 11(2):1–9

    Google Scholar 

  • Ostad-Ali-Askari K, Shayannejad M, Ghorbanizadeh-Kharazi H (2017) Artificial neural network for modeling nitrate pollution of groundwater in marginal area of Zayandeh-rood River, Isfahan, Iran. KSCE J Civil Eng 21(1):134–140

    Google Scholar 

  • Piraghaj SF, Dastjerdi AV, Calheiros RN, Buyya R (2017) ContainerCloudSim: an environment for modeling and simulation of containers in cloud data centers. Software: Pract Experience 47(4):505–521

    Google Scholar 

  • Qian Z, Wang X, Liu X, Xie X, Song T (2020) An approach to dynamically assigning cloud resource considering user demand and benefit of cloud platform. Computing 102:1817–1842

    MathSciNet  Google Scholar 

  • Rajganesh N, Ramkumar T (2016) A review on broker based cloud service model. J Comput Inf Technol 24(3):283–292

    Google Scholar 

  • Rawat PS, Dimri P, Gupta P, Saroha GP (2021) Resource provisioning in scalable cloud using bio-inspired artificial neural network model. Appl Soft Comput 99:106876

    Google Scholar 

  • Ren H, Xu Z, Liang W, Xia Q, Zhou P, Rana OF, Galis A, Wu G (2020) Efficient algorithms for delay-aware NFV-enabled multicasting in mobile edge clouds with resource sharing. IEEE Trans Parallel Distrib Syst 31(9):2050–2066

    Google Scholar 

  • Saif MAN, Niranjan SK, Al-Ariki HDE (2021) Efficient autonomic and elastic resource management techniques in cloud environment: taxonomy and analysis. Wirel Netw 27(4):2829–2866. https://doi.org/10.1007/s11276-021-02614-1

    Article  Google Scholar 

  • Satpathy A, Addya SK, Turuk AK, Majhi B, Sahoo G (2018) Crow search based virtual machine placement strategy in cloud data centers with live migration. Comput Electr Eng 69:334–350

    Google Scholar 

  • Shahidinejad A, Ghobaei-Arani M, Masdari M (2021) Resource provisioning using workload clustering in cloud computing environment: a hybrid approach. Clust Comput 24(1):319–342

    Google Scholar 

  • Shahidinejad A, Ghobaei-Arani M and Esmaeili L (2019) An elastic controller using Colored Petri Nets in cloud computing environment." Cluster Computing 1–27.

  • Shen 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

  • Sohani M, Jain SC (2021) A predictive priority-based dynamic resource provisioning scheme with load balancing in heterogeneous cloud computing. IEEE Access 9:62653–62664

    Google Scholar 

  • Suresh A, Varatharajan R (2019) Competent resource provisioning and distribution techniques for cloud computing environment. Cluster Comput 1–8

  • Tuli S, Sandhu R, Buyya R (2020) Shared data-aware dynamic resource provisioning and task scheduling for data intensive applications on hybrid clouds using Aneka. Futur Gene Comput Syst 106:595–606

    Google Scholar 

  • Wen Y, Wang Y, Liu J, Cao B, Fu Q (2020) CPU usage prediction for cloud resource provisioning based on deep belief network and particle swarm optimization. Concurr Comput Pract Exp 32(14):e5730

    Google Scholar 

  • Wilczyński A, Kołodziej J (2020) Modelling and simulation of security-aware task scheduling in cloud computing based on Blockchain technology. Simul Model Pract Theory 99:102038

    Google Scholar 

  • Yang J, Jiang B, Lv Z, Choo K-KR (2020) A task scheduling algorithm considering game theory designed for energy management in cloud computing. Futur Gener Comput Syst 105:985–992

    Google Scholar 

  • Yu H, Yang J, Fung C (2020) Fine-grained CLOUD RESOURCE PROVISIONING FOR VIRTUAL NETWORK FUNCTION. IEEE Trans Netw Serv Manag

  • Zhou S, Xue Z, Du P (2019) Semisupervised stacked autoencoder with cotraining for hyperspectral image classification. IEEE Trans Geosci Remote Sens 57(6):3813–3826

    Google Scholar 

Download references

Acknowledgements

The authors wish to acknowledge the Department of Computer Applications, Sri Jayachamarajendra College of Engineering (Affiliated to VTU), Mysore-570006, Karnataka, India, for their support and all the facilities provided for this research work.

Author information

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, Karnataka, India

    Belal Abdullah Hezam Murshed

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

    Belal Abdullah Hezam Murshed

  4. Department of Information Technology, Faculty of Engineering and Information Technology, Al-ata’a University for Science and Technology, Taiz, Yemen

    Hasib Daowd Esmail Al-ariki

  5. Department of Computer Networks Engineering and Technologies, Sana’a Community College, Sana’a, Yemen

    Hasib Daowd Esmail Al-ariki

  6. Department of Computer Application, Ramaiah Institute of Technology, VTU, Bangalore, India

    Hudhaifa Mohammed Abdulwahab

Authors
  1. Mufeed Ahmed Naji Saif
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. K. Niranjan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Belal Abdullah Hezam Murshed
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hasib Daowd Esmail Al-ariki
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hudhaifa Mohammed Abdulwahab
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mufeed Ahmed Naji Saif.

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

Saif, M.A.N., Niranjan, S.K., Murshed, B.A.H. et al. Multi-agent QoS-aware autonomic resource provisioning framework for elastic BPM in containerized multi-cloud environment. J Ambient Intell Human Comput 14, 12895–12920 (2023). https://doi.org/10.1007/s12652-022-04120-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-022-04120-4

Keywords

Search

Navigation