Abstract
A burning challenge is balancing the demand for servers in computer data centers. In the modern era, the bulk of computer systems are used for big data and cloud computing. The Internet of Things (IoT) is becoming a hyper-world of physical, cyber and social worlds with big data as a connection. In the latest systems architectures, managing the load in cloud applications is a major challenge. While several techniques and algorithms have been framed to achieve optimality in the load balancing definition, they are limited to the current problems of that time. However, due to a massive increase in data to be handled for the computing activities that are running on servers, exponential petabytes of data that are stored, the complexity of the problem increases day by day. By efficiently balancing the load and high utilization of the available resources from the computing pool, the solution to such problems can be achieved. When these are performed professionally, optimum values can be accomplished with the prominent use of available resources. Genetic Algorithms, Bacteria Foraging, optimizes the algorithm, Artificial Bee Colony, Optimized ABC are current trending algorithms used in load balancing concepts. All these trending algorithms, while strong, lack certain factors. To overcome them, we proposed an EBGO algorithm that has overcome the disadvantages of the trending load balancing algorithms in terms of certain significant parameters such as response time, energy consumption, weighted total cost, procession time, and many. This model aims to achieve overall optimal parameter values, thus efficiently balancing the demand on servers in data centers, resulting in optimal resource use. This model is often appropriate for health care systems, where large number of different types of systems and data are shared between them.
Similar content being viewed by others
References
Ahmed A, Latif R, Latif S (2018) Malicious insiders attack in IoT based Multi-Cloud e-Healthcare environment: A Systematic Literature Review. Multimed Tools Appl 77:21947–21965
Aldribi A, Traore I, Moa B (2018) Data sources and datasets for cloud intrusion detection modeling and evaluation. In: Mishra B, Das H, Dehuri S, Jagadev A (eds) Cloud computing for optimization: foundations, applications, and challenges. Studies in big data, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-319-73676-1_13
Amiri M, Mohammad-khanli L (2017) Survey on prediction models of applications for resources provisioning in cloud. J NetwComput Appl 82:93–113
Aslam S, Shah MA (2015) Load balancing algorithms in cloud computing: A survey of modern techniques. 2015 National Software Engineering Conference (NSEC), Rawalpindi, pp 30–35. https://doi.org/10.1109/NSEC.2015.7396341
Babou CSM Hierarchical Load Balancing and Clustering Technique for Home Edge Computing. IEEE Access. https://doi.org/10.1109/ACCESS.2020.3007944
Barik RK (2018) Fog assisted cloud computing in era of big data and internet-of-things: systems, architectures, and applications. In: Mishra B, Das H, Dehuri S, Jagadev A (eds) Cloud computing for optimization: foundations, applications, and challenges. Studies in big data, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-319-73676-1_14
Barik H, Swain NK, Adhikari N (2020) Load Balanced Double Star: A High-Performance Architecture for Data Intensive Applications, vol 2020. International conference on computer science, engineering and applications (ICCSEA), Gunupur, pp 1–6. https://doi.org/10.1109/ICCSEA49143.2020.9132838
Chaudhury KS, Pattnaik S, Moharana HS, Pradhan S (2020) Static load balancing algorithms in cloud computing: challenges and solutions. In: Reddy V, Prasad V, Wang J, Reddy K (eds) Soft computing and signal processing. ICSCSP 2019.Advances in intelligent systems and computing, vol 1118. Springer, Singapore. https://doi.org/10.1007/978-981-15-2475-2_24
Dam S, Mandal G, Dasgupta K, Dutta P (2014) An ant Colony based load balancing strategy in cloud computing. In: Kumar Kundu M, Mohapatra D, Konar A, Chakraborty A (eds) Advanced computing, networking and informatics- volume 2. Smart innovation, systems and technologies, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-319-07350-7_45
Das S, Dasgupta S, Biswas A, Abraham A, Konar A (2009) On stability of the chemotactic dynamics in bacterial-foraging optimization algorithm. IEEE Trans Syst Man Cybernet- Part A: Syst Humans 39(3):670–679. https://doi.org/10.1109/TSMCA.2008.2011474
Du KL, Swamy MNS (2019) Big data, cloud computing, and internet of things. In: Neural Networks and Statistical Learning. Springer, London. https://doi.org/10.1007/978-1-4471-7452-3_31
Ebadifard F, Babamir SM, Barani S (2020) A dynamic task scheduling algorithm improved by load balancing in cloud computing. 2020 6th international conference on web research (ICWR). doi:https://doi.org/10.1109/icwr49608.2020.9122287
Ebadifard F, Babamir SM, Barani S (2020) A Dynamic Task Scheduling Algorithm Improved by Load Balancing in Cloud Computing. 2020 6th international conference on web research (ICWR), Tehran, pp 177–183. https://doi.org/10.1109/ICWR49608.2020.9122287
Elrotub M, Gherbi A (2018) Virtual machine classification-BasedApproach to enhanced workload balancing for cloud computing applications. Procedia Comput Sci 130:683–688
Elrotub M, Gherbi A (2018) Virtual machine classification-BasedApproach to enhanced workload balancing for cloud computing applications. Procedia Comput Sci 130:683–688
Essa YM, Hemdan EE, El-Mahalawy A (2019) IFHDS: intelligent framework for securing healthcare BigData. J Med Syst 43:124. https://doi.org/10.1007/s10916-019-1250-4
Gu X, Liao Z (2017) Short-term load forecasting based on phase space reconstruction and Gaussian process regression[J]. Power Syst Protect Control 45(5):73–79
Hu J, Wei X, Yang M, Tang B, Lin K, Zhong Y (2020) A Practical RBF Framework for Database Load Balancing Prediction. 2020 3rd international conference on artificial intelligence and big data (ICAIBD), Chengdu, pp 83–86. https://doi.org/10.1109/ICAIBD49809.2020.9137481
Huankai C, Wang F, Helian N, Akanmu G (2013) User-priority guided Min-Min scheduling algorithm for load balancing in cloud computing. 2013 National Conference on Parallel Computing Technologies (PARCOMPTECH), Bangalore, pp 1–8. https://doi.org/10.1109/ParCompTech.2013.6621389
Kapoor S, Dabas C (2015) Cluster based load balancing in cloud computing. 2015 Eighth International Conference on Contemporary Computing (IC3), Noida, pp 76–81. https://doi.org/10.1109/IC3.2015.7346656
Kong W, Dong ZY, Jia Y (2017) Short-term residential load forecasting based on LSTM recurrent neural network[J]. IEEE Trans Smart Grid:1–1
Lim J, Lee D (2020) A load balancing algorithm for Mobile devices in edge cloud computing environments. Electronics 9(4):686
Mohiddin S. (2019) Unique methodology to mitigate anti-forensics in cloud using attack-graphs. 8. 1569-1574. https://doi.org/10.35940/ijitee.A1037.0881019
Mohiddin SK, Babu DYS (2019) A relevance technical approach for screening the significance of IDS in cloud Forensics.IJITEE) ISSN, pp.2278-3075
Mohiddin SK, Babu YS (2020) Role of cloud forensics in cloud computing. In soft computing for problem solving (pp. 91-107). Springer, Singapore
Mohiddin SK, Yalavarthi SB, Kondragunta V (2019) An analytical comparative approach of cloud forensic tools during cyber attacks in cloud. In soft computing for problem solving (pp. 509-517). Springer, Singapore
Mulla BP, Krishna CR, Tickoo RK (2020) Load Balancing Algorithm for Efficient VM Allocation in Heterogeneous Cloud (March 24, 2020). International Journal of Computer Networks & Communications (IJCNC) Vol.12, No.1
Muthulakshmi B, Somasundaram K (2017) A hybrid ABC-SA based optimized scheduling and resource allocation for cloud environment. Clust Comput 22:10769–10777. https://doi.org/10.1007/s10586-017-1174-z
Nace D, Pioro M (2008) Max-min fairness and its applications to routing and load-balancing in communication networks: a tutorial," in IEEE Communications Surveys & Tutorials, vol. 10, no. 4, pp. 5–17, Fourth Quarter, doi: https://doi.org/10.1109/SURV.2008.080403
Nasr AA, El-Bahnasawy NA, Attiya G, El-Sayed A (2019) Cloudlet scheduling based load balancing on virtual Machines in Cloud Computing Environment. J Internet Technol 20(5):1371–1378
Nayak L, Jayalakshmi V (2020) A Survey on Privacy Preserving Approaches on Health Care Big Data in Cloud. In: Pandian A, Palanisamy R, Ntalianis K (eds) Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019). ICCBI 2019. Lecture notes on data engineering and communications technologies, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-030-43192-1_4
Nayyar A, Singh R (2019) IEEMARP- a novel energy efficient multipath routing protocol based on ant Colony optimization (ACO) for dynamic sensor networks. Multimed Tools Appl 79:35221–35252. https://doi.org/10.1007/s11042-019-7627-z
Peng K, Huang H, Pan W, Wang J (2020) Joint optimization for time consumption and energy consumption of multi-application and load balancing of cloudlets in mobile edge computing. IET Cyber-Phys Syst: Theory Appl 5(2):196–206, 6. https://doi.org/10.1049/iet-cps.2019.0085
Remesh Babu KR, Samuel P (2016) Enhanced bee Colony algorithm for efficient load balancing and scheduling in cloud. In: Snášel V, Abraham A, Krömer P, Pant M, Muda A (eds) Innovations in bio-inspired computing and applications. Advances in intelligent systems and computing, vol 424. Springer, Cham. https://doi.org/10.1007/978-3-319-28031-8_6
Remesh Babu KR, Samuel P (2016) Enhanced bee Colony algorithm for efficient load balancing and scheduling in cloud. In: Snášel V, Abraham A, Krömer P, Pant M, Muda A (eds) Innovations in bio-inspired computing and applications. Advances in intelligent systems and computing, vol 424. Springer, Cham. https://doi.org/10.1007/978-3-319-28031-8_6
Saif T, Javaid N, Rahman M, Butt H, Kamal MB, Ali MJ (2019) Round Robin Inspired History Based Load Balancing Using Cloud Computing. In: Xhafa F, Leu FY, Ficco M, Yang CT (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2018. Lecture notes on data engineering and communications technologies, vol 24. Springer, Cham. https://doi.org/10.1007/978-3-030-02607-3_46
Seema B, Yao N, Carie A (2020) Efficient data transfer in clustered IoT network with cooperative member nodes. Multimed Tools Appl 79:34241–34251. https://doi.org/10.1007/s11042-020-08775-z
Semmoud A, Hakem M, Benmammar B, Charr J (2020) Load balancing in cloud computing environments based on adaptive starvation threshold. Concurrency Computation: Pract Experience. https://doi.org/10.1002/cpe.5652
Stanojevic R, Shorten R (2009) Load Balancing vs. Distributed Rate Limiting: An Unifying Framework for Cloud Control, vol 2009. IEEE International conference on communications, Dresden, pp 1–6. https://doi.org/10.1109/ICC.2009.5199141
Stavrinides GL, Karatza HD (2020) Dynamic scheduling of bags-of-tasks with sensitive input data and end-to-end deadlines in a hybrid cloud. Multimed Tools Appl. https://doi.org/10.1007/s11042-020-08974-8
Uma Maheswari S, Vasanthanayaki C (2020) Secure medical health care content protection system (SMCPS) with watermark detection for multi cloud computing environment. Multimed Tools Appl 79:4075–4097. https://doi.org/10.1007/s11042-019-7724-z
Wang J, Yang W, Pei D, etc (2018) Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system[J]. Energy 148:59–78
Xu M, Tian W, Buyya R (2017) A survey on load balancing algorithms for virtual machines placement in cloud computing. Concurr ComputPract Exp 29(12):1–22
Yao J, He J-h (2012) Load balancing strategy of cloud computing based on artificial bee algorithm. 2012 8th International Conference on Computing Technology and Information Management (NCM and ICNIT), Seoul, pp 185–189
Zomaya AY, Teh Y-H (2001) Observations on using genetic algorithms for dynamic load-balancing. IEEE Trans Parallel Distributed Syst 12(9):899–911. https://doi.org/10.1109/71.954620
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.
Rights and permissions
About this article
Cite this article
Velpula, P., Pamula, R. EBGO: an optimal load balancing algorithm, a solution for existing tribulation to balance the load efficiently on cloud servers. Multimed Tools Appl 81, 34653–34675 (2022). https://doi.org/10.1007/s11042-021-11012-w
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-021-11012-w