Abstract
In cloud computing environment, load balancing is an important task. So many of the researchers had focused on load balanced scheduling technique. Those provides better load balancing in cloud but there are some issues like resource allocation and cost maintenance. One of the major issue in load balancing techniques is service level agreement (SLA) management because many of them are affected by this SLA-violation. Many researchers have proposed various risk based framework but few of them has guides the service provider to take steps for SLA violation abatement and they also need some improvements. To tackle this problem, a new SLA-aware risk management framework (SA-RMF) is proposed in this work for efficient load balancing in cloud. A new technique is presented here based on CPU parameter for generating efficient dynamic threshold. A better quality of service values prediction is achieved by using hybrid approach in SA-RMF. Through experiments, the appropriateness of proposed system is demonstrated and validated that it helps cloud providers to mitigate future violations of services and consequences.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Change history
04 July 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s12652-022-04287-w
References
Ala'Anzy M, Othman M (2019) Load balancing and server consolidation in cloud computing environments: a meta-study. IEEE Access 7:141868–141887
Alencar DB, Affonso CM, Oliveira RCL, Filho JCR (2018) Hybrid approach combining SARIMA and neural networks for multi-step ahead wind speed forecasting in Brazil. IEEE Access 6:55986–55994
Alsarhan A, Itradat A, Al-Dubai AY, Zomaya AY, Min G (2018) Adaptive resource allocation and provisioning in multi-service cloud environments. IEEE Trans Parallel Distrib Syst 29(1):31–42
Ammar A-M, Luo J, Tang Z, Wajdy O (2019) Intra-balance virtual machine placement for effective reduction in energy consumption and SLA violation. IEEE Access 7:72387-72402.S
Dey NS, Gunasekhar T (2019) A comprehensive survey of load balancing strategies using Hadoop queue scheduling and virtual machine migration. IEEE Access 7:92259–92284
Djemame K, Armstrong D, Guitart J, Macias M (2016) A Risk Assessment Framework for Cloud Computing. IEEE Trans Cloud Comput 4(3):265–278. https://doi.org/10.1109/TCC.2014.2344653
Godfrey LB, Gashler MS (2018) Neural decomposition of time-series data for effective generalization. IEEE Trans Neural Netw Learn Syst 29(7):2973–2985
Hussain W, Sohaib O (2019) Analysing cloud QoS prediction approaches and its control parameters: considering overall accuracy and freshness of a dataset. IEEE Access 7:82649–82671
Hussain W, Hussain FK, Hussain OK (2017) Risk management framework to avoid SLA violation in cloud from a provider’s perspective. In: Xhafa F, Barolli L, Amato F (eds) Advances on P2P, parallel, grid, cloud and internet computing. 3PGCIC 2016. Lecture notes on data engineering and communications technologies, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-49109-7_22
Hussain A, Aleem M, Khan A, Iqbal MA, Islam MA (2018a) RALBA: a computation-aware load balancing scheduler for cloud computing. Cluster Comput 21:1667–1680. https://doi.org/10.1007/s10586-018-2414-6
Hussain W, Hussain FK, Hussain O, Bagia R, Chang E (2018b) Risk-based framework for SLA violation abatement from the cloud service provider’s perspective. Comput J 61(9):1306–1322. https://doi.org/10.1093/comjnl/bxx118
Khan FA, Shaheen S, Asif M et al (2019) Towards reliable and trustful personal health record systems: a case of cloud-dew architecture based provenance framework. J Ambient Intell Human Comput 10:3795–3808. https://doi.org/10.1007/s12652-019-01292-4
Kumar P, Kumar R (2019) Issues and challenges of load balancing techniques in cloud computing: a survey. ACM Comput Surv 51(6):1–35
Li L, Dong J, Zuo D, Wu J (2019) SLA-aware and energy-efficient VM consolidation in cloud data centers using robust linear regression prediction model. IEEE Access 7:9490–9500
Liaqat M, Naveed A, Ali RL, Shuja J, Ko K (2019) Characterizing dynamic load balancing in cloud environments using virtual machine deployment models. IEEE Access 7:145767–145776
Liu F, Ma Z, Wang B, Lin W (2020) A virtual machine consolidation algorithm based on Ant Colony system and extreme learning machine for cloud data center. IEEE Access 8:53–67
Melhem SB, Agarwal A, Goel N, Zaman M (2018) Markov prediction model for host load detection and VM placement in live migration. IEEE Access 6:7190–7205
Punitha AAA, Indumathi G (2020) A novel centralized cloud information accountability integrity with ensemble neural network based attack detection approach for cloud data. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-01916-0
Sharma NK, Reddy GRM (2019) Multi-objective energy efficient virtual machines allocation at the cloud data center. IEEE Trans Serv Comput 12(1):158–171
Tang F, Yang LT, Tang C, Li J, Guo M (2018) A dynamical and load-balanced flow scheduling approach for big data centers in clouds. IEEE Trans Cloud Comput 6(4):915–928
Yadav R, Zhang W, Kaiwartya O, Singh PR, Elgendy IA, Tian Y (2018) Adaptive energy-aware algorithms for minimizing energy consumption and SLA violation in cloud computing. IEEE Access 6:55923–55936
Yao M, Chen D, Shang J (2019) Optimal overbooking policy for cloud service providers: profit and service quality. IEEE Access 7:96132–96147
Zhou Z, Hu Z, Li K (2016) Virtual machine placement algorithm for both energy-awareness and SLA violation reduction in cloud data centers. Sci Program Hindawi. https://doi.org/10.1155/2016/5612039
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-04287-w"
About this article
Cite this article
Gupta, A., Bhadauria, H.S. & Singh, A. RETRACTED ARTICLE: SLA-aware load balancing using risk management framework in cloud. J Ambient Intell Human Comput 12, 7559–7568 (2021). https://doi.org/10.1007/s12652-020-02458-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-020-02458-1