Abstract
The growth of the Internet of Things (IoT) can lead to improved productivity, scalability, connectivity, and saving time and money. However, the increasing number of IoT-based applications has MAPE the centralized cloud computing paradigm face several challenges such as latency and bandwidth. Nowadays, fog computing with distributed architecture has emerged to support latency-sensitive IoT applications with limited resources. Using the fog instead of the cloud can bring the storage and computing facilities closer to the edge of the network and thus provide better performance for the end user. Meanwhile, IoT applications usually have complex multi-component structures whose efficient placement on fog nodes can overcome the resource restrictions of IoT devices. This problem as the IoT Service Placement Problem (SPP) is NP-hard, where nature-inspired approaches widely provide robust solutions to solve it. In this paper, a meta-heuristic-based evolutionary approach named SPP-ICA is presented to address SPP, which originates from the Imperialist Competitive Algorithm (ICA). SPP-ICA is developed based on MAPE-K autonomous planning model, so that it applies service deployment priority and resource consumption distribution in the placement process. ICA embedded in SPP-ICA leads to more effective placement of IoT services in terms of latency and resource utilization by considering the concepts of elitism and balanced resource consumption. Experimental results show that SPP-ICA performs significantly better than state-of-the-art algorithms with meta-heuristic structure. On average, SPP-ICA deploys more IoT services on fog and reduces service latency by 9%.
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References
Shakarami A, Shakarami H, Ghobaei-Arani M, Nikougoftar E, Faraji-Mehmandar M (2022) Resource provisioning in edge/fog computing: a comprehensive and systematic review. J Syst Archit 122:102362
Li S, Geng Z (2023) Bicriteria scheduling on an unbounded parallel-batch machine for minimizing makespan and maximum cost. Inf Process Lett 180:106343
Zhao L, Wang L (2022) A new lightweight network based on MobileNetV3. KSII Trans Internet Inf Syst (TIIS) 16(1):1–15
Zhang H, Zou Q, Ju Y, Song C, Chen D (2022) Distance-based support vector machine to predict DNA N6-methyladenine modification. Curr Bioinform 17(5):473–482
Kumar D, Baranwal G, Shankar Y, Vidyarthi DP (2022) A survey on nature-inspired techniques for computation offloading and service placement in emerging edge technologies. World Wide Web 25(5):2049–2107
Cao C, Wang J, Kwok D, Cui F, Zhang Z, Zhao D, Zou Q (2022) webTWAS: a resource for disease candidate susceptibility genes identified by transcriptome-wide association study. Nucleic Acids Res 50(D1):D1123–D1130
Cheng F, Wang H, Zhang L, Ahmad AM, Xu N (2022) Decentralized adaptive neural two-bit-triggered control for nonstrict-feedback nonlinear systems with actuator failures. Neurocomputing 500:856–867
Wang M, Yang M, Fang Z, Wang M, Wu Q (2022) A practical feeder planning model for urban distribution system. IEEE Trans Power Syst. https://doi.org/10.1109/TPWRS.2022.3170933
Tan J, Liu L, Li F, Chen Z, Chen GY, Fang F, Zhou X (2022) Screening of endocrine disrupting potential of surface waters via an affinity-based biosensor in a rural community in the Yellow River Basin, China. Environ Sci Technol 56(20):14350–14360
Li Y, Niu B, Zong G, Zhao J, Zhao X (2022) Command filter-based adaptive neural finite-time control for stochastic nonlinear systems with time-varying full-state constraints and asymmetric input saturation. Int J Syst Sci 53(1):199–221
Yang R, Yang C, Peng X, Rezaeipanah A (2022) A novel similarity measure of link prediction in multi‐layer social networks based on reliable paths. Concurrency Comput Pract Experience 34(10):e6829
Santos GL, Bezerra DDF, Rocha EDS, Ferreira L, Moreira ALC, Gonçalves GE, Endo PT (2022) Service function chain placement in distributed scenarios: a systematic review. J Netw Syst Manag 30(1):4
Cao Z, Niu B, Zong G, Xu N (2023) Small-gain technique-based adaptive output constrained control design of switched networked nonlinear systems via event-triggered communications. Nonlinear Anal Hybrid Syst 47:101299
He Y, Chang XH, Wang H, Zhao X (2022) Command-filtered adaptive fuzzy control for switched MIMO nonlinear systems with unknown dead zones and full state constraints. Int J Fuzzy Syst. https://doi.org/10.1007/s40815-022-01384-y
Varshney P, Simmhan Y (2017) Demystifying fog computing: characterizing architectures, applications and abstractions. In: 2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC). IEEE, pp 115–124
Hong CH, Varghese B (2019) Resource management in fog/edge computing: a survey on architectures, infrastructure, and algorithms. ACM Comput Surv (CSUR) 52(5):1–37
Gasmi K, Dilek S, Tosun S, Ozdemir S (2022) A survey on computation offloading and service placement in fog computing-based IoT. J Supercomput 78(2):1983–2014
Liu C, Wang J, Zhou L, Rezaeipanah A (2022) Solving the multi-objective problem of IoT service placement in fog computing using cuckoo search algorithm. Neural Process Lett 54(3):1823–1854
Rezaeipanah A, Mojarad M, Fakhari A (2022) Providing a new approach to increase fault tolerance in cloud computing using fuzzy logic. Int J Comput Appl 44(2):139–147
Ghobaei-Arani M, Shahidinejad A (2022) A cost-efficient IoT service placement approach using whale optimization algorithm in fog computing environment. Expert Syst Appl 200:117012
Wu B, Lv X, Shamsi WD, Dizicheh EG (2022) Optimal deploying IoT services on the fog computing: a metaheuristic-based multi-objective approach. J King Saud Univ-Comput Inf Sci 34(10):10010–10027
Berahmand K, Mohammadi M, Saberi-Movahed F, Li Y, Xu Y (2022) Graph regularized nonnegative matrix factorization for community detection in attributed networks. IEEE Trans Netw Sci Eng 10(1):372–385
Jazayeri F, Shahidinejad A, Ghobaei-Arani M (2021) Autonomous computation offloading and auto-scaling the in the mobile fog computing: a deep reinforcement learning-based approach. J Ambient Intell Humaniz Comput 12:8265–8284
Zhao Y, Tang F, Zong G, Zhao X, Xu N (2022) Event-based adaptive containment control for nonlinear multiagent systems with periodic disturbances. IEEE Trans Circuits Syst II Express Briefs 69(12):5049–5053
Nasiri E, Berahmand K, Li Y (2022) Robust graph regularization nonnegative matrix factorization for link prediction in attributed networks. Multimed Tools Appl 82:3745–3768
Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: 2007 IEEE Congress on Evolutionary Computation. IEEE, pp 4661–4667)
Liu Z, Zheng Z, Sudhoff SD, Gu C, Li Y (2016) Reduction of common-mode voltage in multiphase two-level inverters using SPWM with phase-shifted carriers. IEEE Trans Power Electron 31(9):6631–6645
Cao Z, Zhang L, Ahmad AM, Alsaadi FE, Alassafi MO (2022) Adaptive neural prescribed performance control for switched pure-feedback non-linear systems with input quantization. Assem Autom 42(6):869–880
Zhong Y, Chen L, Dan C, Rezaeipanah A (2022) A systematic survey of data mining and big data analysis in internet of things. J Supercomput 78:18405–18453
Zhang Y, Zhang F, Tong S, Rezaeipanah A (2022) A dynamic planning model for deploying service functions chain in fog-cloud computing. J King Saud Univ Comput Inf Sci 34(10):7948–7960
Sulimani H, Sajjad AM, Alghamdi WY, Kaiwartya O, Jan T, Simoff S, Prasad M (2022) Reinforcement optimization for decentralized service placement policy in IoT-centric fog environment. Trans Emerg Telecommun Technol. https://doi.org/10.1002/ett.4650
Cao B, Sun Z, Zhang J, Gu Y (2021) Resource allocation in 5G IoV architecture based on SDN and fog-cloud computing. IEEE Trans Intell Transp Syst 22(6):3832–3840
Wang Y, Niu B, Ahmad A, Liu Y, Wang H, Zong G, Alsaadi F (2022) Adaptive command filtered control for switched multi-input multi-output nonlinear systems with hysteresis inputs. Int J Adapt Control Signal Process 36(12):3023–3042
Si Z, Yang M, Yu Y, Ding T (2021) Photovoltaic power forecast based on satellite images considering effects of solar position. Appl Energy 302:117514
Li P, Yang M, Wu Q (2021) Confidence interval based distributionally robust real-time economic dispatch approach considering wind power accommodation risk. IEEE Trans Sustain Energy 12(1):58–69
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Zare, M., Elmi Sola, Y. & Hasanpour, H. An autonomous planning model for solving IoT service placement problem using the imperialist competitive algorithm. J Supercomput 79, 12671–12690 (2023). https://doi.org/10.1007/s11227-023-05172-9
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DOI: https://doi.org/10.1007/s11227-023-05172-9