Abstract
Load management is a crucial aspect of allocating resources inside a data center for use by the Internet of Things-based public cloud networking (IoT-PCN). It’s a significant pain in the IoT-PCN when queries become bogged down due to the complexity of Internet computing. All virtual machines (VM) must have evenly distributed workloads for load balancing to be effective. Scheduling tasks is a huge step toward improving cloud computing’s overall efficiency. Both are crucial to minimize resource consumption and increase service providers’ output by speeding up the processing time. This research proposes a gallant ant colony optimized machine learning framework (GACO-MLF) balance the load in IoT-PCN that arises rapidly. A machine learning strategy is applied to precisely identify the imbalanced load across IoT-PCN. Ant colony optimization is enhanced to optimize and schedule the imbalanced loads across all data centers. GACO-MLF is evaluated using Cloudsim simulator with standard performance metrics, and the results indicate that GACO-MLF has superior performance than the current strategies.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Qu S, Zhao L, Chen Y, Mao W (2021) A discrete-time sliding mode congestion controller for wireless sensor networks. Optik (Stuttg), 225
Akhtar N, Khan MA, Ullah A, Javed MY (2019) Congestion avoidance for smart devices by caching information in MANETS and IoT. IEEE Access 7:71459–71471
Song J, Han Z, Wang W, Chen J, Liu Y (2022) A new secure arrangement for privacy-preserving data collection. Comput Stand Interfaces 80:103582
Junior NF, Silva AAA, Guelfi AE, Kofuji ST (2021) Privacy-preserving cloud-connected IoT data using context-aware and end-to-end secure messages. Procedia Comput Sci 191:25–32
Anuradha M, Jayasankar T, Prakash NB, Sikkandar MY, Hemalakshmi GR, Bharatiraja C, Britto ASF (2021) IoT enabled cancer prediction system to enhance the authentication and security using cloud computing. Microprocess Microsyst 80:103301
Chen Y, Zhu J, Wan L, Fang X, Tong F, Xu X (2022) Routing failure prediction and repairing for AUV-assisted underwater acoustic sensor networks in uncertain ocean environments. Appl Acoust 186:108479
Gherairi S (2022) Healthcare: a priority-based energy harvesting scheme for managing sensor nodes in WBANs. Ad Hoc Netw 133:102876
Ouhab A, Abreu T, Slimani H, Mellouk A (2020) Energy-efficient clustering and routing algorithm for large-scale SDN-based IoT monitoring. In: IEEE International conference on communications. IEEE, pp 1–6
Manikandan N, Gobalakrishnan N, Pradeep K (2022) Bee optimization based random double adaptive whale optimization model for task scheduling in cloud computing environment. Comput Commun 187:35–44
Ashenden SK, Deswal S, Bulusu KC, Bartosik A, Shameer K (2021) Data types and resources. In: Ashenden machine learning, and data science in the pharmaceutical industry, SKBT-TE of AI (ed) The era of artificial intelligence, machine learning, and data science in the pharmaceutical industry. Academic Press, pp. 27–60
Wang H, Xiao M, Wu C, Zhang J (2021) Distributed classification for imbalanced big data in distributed environments. Wirel Netw, pp 1–12
Ali S, Pandey M, Tyagi N (2022) SDFog-Mesh: a software-defined fog computing architecture over wireless mesh networks for semi-permanent smart environments. Comput Netw 211:108985
Bouchaala M, Ghazel C, Saidane LA (2021) TRAK-CPABE: a novel traceable, revocable and accountable ciphertext-policy attribute-based encryption scheme in cloud computing. J Inf Secur Appl 61:102914
Adhikari M, Nandy S, Amgoth T (2019) Meta heuristic-based task deployment mechanism for load balancing in IaaS cloud. J Netw Comput Appl 128:64–77
Deeban Chakravarthy V, Amutha B (2020) Software-defined network assisted packet scheduling method for load balancing in mobile user concentrated cloud. Comput Commun 150:144–149
Rjoub G, Bentahar J, Wahab OA (2020) BigTrustScheduling: trust-aware big data task scheduling approach in cloud computing environments. Futur Gener Comput Syst 110:1079–1097
Luo J, Yin L, Hu J, Wang C, Liu X, Fan X, Luo H (2019) Container-based fog computing architecture and energy-balancing scheduling algorithm for energy IoT. Futur Gener Comput Syst 97:50–60
Ramkumar J, Vadivel R (2021) Multi-adaptive routing protocol for Internet of Things based ad-hoc networks. Wirel Pers Commun 120:887–909
Ramkumar J, Kumuthini C, Narasimhan B, Boopalan S (2022) Energy consumption minimization in cognitive radio mobile ad-hoc networks using enriched ad-hoc on-demand distance vector protocol. In: 2022 International conference on advanced computing technologies and applications ICACTA 2022, pp 1–6
Jaganathan R, Vadivel R (2021) Intelligent fish swarm inspired protocol (IFSIP) for dynamic ideal routing in cognitive radio ad-hoc networks. Int J Comput Digit Syst 10:1063–1074
Jaganathan R (2020) Meticulous elephant herding optimization based protocol for detecting intrusions in cognitive radio ad hoc networks. Int J Emerg Trends Eng Res 8:4548–4554
Ramkumar J, Vadivel R (2020) Improved Wolf prey inspired protocol for routing in cognitive radio ad hoc networks. Int J Comput Netw Appl 7:126–136
Ramkumar J, Vadivel R (2021) Whale optimization routing protocol for minimizing energy consumption in cognitive radio wireless sensor network. Int J Comput Netw Appl 8:455–464
Ramkumar J, Vadivel R (2019) Performance modeling of bio-inspired routing protocols in cognitive radio ad hoc network to reduce end-to-end delay. Int J Intell Eng Syst 12:221–231
Ramkumar J, Vadivel R, Narasimhan B (2021) Constrained cuckoo search optimization based protocol for routing in cloud network. Int. J. Comput. Networks Appl. 8:795–803
Ramkumar J (2020) Bee inspired secured protocol for routing in cognitive radio ad hoc networks. Indian J Sci Technol 13:2159–2169
Ramkumar J, Vadivel R (2017) CSIP—cuckoo search inspired protocol for routing in cognitive radio ad hoc networks. In: Advances in intelligent systems and computing. Springer Verlag, pp 145–153
Menakadevi P, Ramkumar J (2022) Robust optimization based extreme learning machine for sentiment analysis in Big Data. In: 2022 International conference on advanced computing technologies and applications ICACTA 2022, pp 1–5 (2022)
Ramkumar J, Vadivel R (2018) Improved frog leap inspired protocol (IFLIP)—for routing in cognitive radio ad hoc networks (CRAHN). World J Eng 15:306–311
Vadivel R, Ramkumar J (2019) QoS-enabled improved cuckoo search-inspired protocol (ICSIP) for IoT-based healthcare applications. In: Incorporating the Internet of Things in healthcare applications and wearable devices, pp 109–121
Abbasi M, Yaghoobikia M, Rafiee M, Jolfaei A, Khosravi MR (2020) Efficient resource management and workload allocation in fog–cloud computing paradigm in IoT using learning classifier systems. Comput Commun 153:217–228
Liu G, Xiao Z, Tan GH, Li K, Chronopoulos AT (2020) Game theory-based optimization of distributed idle computing resources in cloud environments. Theor Comput Sci 806:468–488
Kim T, Min H, Choi E, Jung J (2020) Optimal job partitioning and allocation for vehicular cloud computing. Futur Gener Comput Syst 108:82–96
Lavanya M, Shanthi B, Saravanan S (2020) Multi objective task scheduling algorithm based on SLA and processing time suitable for cloud environment. Comput Commun 151:183–195
Yang J, Jiang B, Lv Z, Choo KKR (2020) A task scheduling algorithm considering game theory designed for energy management in cloud computing. Futur Gener Comput Syst 105:985–992
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ramkumar, J., Vadivel, R., Narasimhan, B., Boopalan, S., Surendren, B. (2024). Gallant Ant Colony Optimized Machine Learning Framework (GACO-MLF) for Quality of Service Enhancement in Internet of Things-Based Public Cloud Networking. In: Tavares, J.M.R.S., Rodrigues, J.J.P.C., Misra, D., Bhattacherjee, D. (eds) Data Science and Communication. ICTDsC 2023. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. https://doi.org/10.1007/978-981-99-5435-3_30
Download citation
DOI: https://doi.org/10.1007/978-981-99-5435-3_30
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-5434-6
Online ISBN: 978-981-99-5435-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)