Abstract
Internet of Things (IoT) based applications have recently experienced a remarkable diffusion in many different contexts, such as automotive, e-health, public security, industrial applications, energy, and waste management. These kinds of applications are characterized by geographically distributed sensors that collect data to be processed through algorithms of Artificial Intelligence (AI). Due to the vast amount of data to be processed by AI algorithms and the severe latency requirements of some applications, the emerging Edge Computing paradigm may represent the preferable choice for the supporting infrastructure. However, the design of edge computing infrastructures opens several new issues concerning the allocation of data flows coming from sensors over the edge nodes, and the choice of the number and the location of the edge nodes to be activated. The service placement issue can be modeled through a multi-objective optimization aiming at minimizing two aspects: the response time for data transmission and processing in the sensors-edge-cloud path; the (energy or monetary) cost related to the number of turned on edge nodes. Two heuristics, based on Variable Neighborhood Search and on Genetic Algorithms, are proposed and evaluated over a wide range of scenarios, considering a realistic smart city application with 100 sensors and up to 10 edge nodes. Both heuristics can return practical solutions for the given application. The results indicate a suitable topology for a network-bound scenario requires less enabled edge nodes comparatively with a CPU-bound scenario. In terms of performance gain, the VNS outperformed in almost every condition the GA approach, reaching a performance gain up to almost 40% when the network delay plays a significant role and when the load is higher. Hence, the experimental tests demonstrate that the proposed heuristics are useful to support the design of edge computing infrastructures for modern AI-based applications relying on data collected by geographically distributed IoT sensors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
References
Ahmadi-Javid A, Seyedi P, Syam SS (2017) A survey of healthcare facility location. Comput Operat Res 79:223–263
Alavi AH, Jiao P, Buttlar WG, Lajnef N (2018) Internet of things-enabled smart cities: state-of-the-art and future trends. Measurement 129:589 – 606
Ardagna D, Ciavotta M, Lancellotti R (2014) A Receding Horizon Approach for the Runtime Management of IaaS Cloud Systems. In: Proceedings of 16th international symposium on symbolic and numeric algorithms for scientific computing (SYNASC), IEEE
Ardagna D, Ciavotta M, Lancellotti R, Guerriero M (2018) A hierarchical receding horizon algorithm for QoS-driven control of multi-IaaS applications. IEEE Trans Cloud Comput 9:1–1
Bačević A, Vilimonović N, Dabić I, Petrović J, Damnjanović D, Džamić D (2019) Variable neighborhood search heuristic for nonconvex portfolio optimization. Eng Economist 64(3):254–274
Back T, Fogel D, Michalewicz Z (2002) Evolutionary computation 1: basic algorithms and operators. CRC Press, Boca Raton
Binitha S, Sathya SS, et al (2012) A survey of bio inspired optimization algorithms. Int J Soft Comput Eng 2(2):137–151
Bu F, Wang X (2019) A smart agriculture IoT system based on deep reinforcement learning. Future Generation Comput Syst 99:500–507
Caiza G, Saeteros M, Oñate W, Garcia MV (2020) Fog computing at industrial level, architecture, latency, energy, and security: a review. Heliyon 6(4):e03706
Canali C, Lancellotti R (2019) A fog computing service placement for smart cities based on genetic algorithms. In: Proceedings of international conference on cloud computing and services science (CLOSER 2019), Heraklion
Canali C, Lancellotti R (2019) GASP: genetic algorithms for service placement in fog computing systems. Algorithms 12(10):201
Canali C, Lancellotti R (2019) Paffi: performance analysis framework for fog infrastructures in realistic scenarios. In: 2019 4th international conference on computing, communications and security (ICCCS), pp 1–8
Celik Turkoglu D, Erol Genevois M (2020) A comparative survey of service facility location problems. Annals of Operations Research 292:399–468
Cooper L (1963) Location-allocation problems. Oper Res 11(3):331–343
Deng R, Lu R, Lai C, Luan TH, Liang H (2016) Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. IEEE Int Things J 3(6):1171–1181
Dhingra S, Madda RB, Patan R, Jiao P, Barri K, Alavi AH (2020) Internet of things-based fog and cloud computing technology for smart traffic monitoring. Internet of Things 14:100175
Farahani RZ, SteadieSeifi M, Asgari N (2010) Multiple criteria facility location problems: A survey. Appl Math Model 34(7):1689–1709
Foukalas F (2020) Cognitive IoT platform for fog computing industrial applications. Comput Electr Eng 87:106770
Gill SS, Tuli S, Xu M, Singh I, Singh KV, Lindsay D, Tuli S, Smirnova D, Singh M, Jain U, Pervaiz H, Sehgal B, Kaila SS, Misra S, Aslanpour MS, Mehta H, Stankovski V, Garraghan P (2019) Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud computing: evolution, vision, trends and open challenges. Int Things 8:100118
Hansen P, Mladenović N, Moreno Pérez JA (2010) Variable neighbourhood search: methods and applications. Ann Oper Res 175(1):367–407
Harrison PG, Patel NM (1993) Performance modeling of communication networks and computer. Addison-Wesley, Boston
Irawan C, Salhi S (2015) Aggregation and non aggregation techniques for large facility location problems - a survey. Yugoslav J Oper Res 25:313–341
Khorov E, Lyakhov A, Krotov A, Guschin A (2015) A survey on IEEE 802.11 ah: an enabling networking technology for smart cities. Comput Commun 58:53–69
Klinkowski M, Walkowiak K, Goścień R (2013) Optimization algorithms for data center location problem in elastic optical networks. In: 2013 15th international conference on transparent optical networks (ICTON), pp 1–5
Liu F, Tang G, Li Y, Cai Z, Zhang X, Zhou T (2019) A survey on edge computing systems and tools. Proc IEEE 107(8):1537–1562
Marotta A, Avallone S (2015) A Simulated Annealing Based Approach for Power Efficient Virtual Machines Consolidation. In: Proceedings of 8th international conference on cloud computing (CLOUD), IEEE
Mladenović N, Hansen P (1997) Variable neighborhood search. Comput Oper Res 24(11):1097–1100
Moura J, Hutchison D (2020) Fog computing systems: state of the art, research issues and future trends, with a focus on resilience. J Netw Comput Appl 169:102784
Queiroz TAd, Mundim LR (2020) Multiobjective pseudo-variable neighborhood descent for a bicriteria parallel machine scheduling problem with setup time. Int Trans Oper Res 27(3):1478–1500
Santos LFM, Iwayama RS, Cavalcanti LB, Turi LM, de Souza Morais FE, Mormilho G, Cunha CB (2019) A variable neighborhood search algorithm for the bin packing problem with compatible categories. Expert Syst Appl 124:209–225
Shanthamallu US, Spanias A, Tepedelenlioglu C, Stanley M (2017) A brief survey of machine learning methods and their sensor and IoT applications. In: 2017 8th international conference on information, intelligence, systems applications (IISA)
Silva RAC, Fonseca NLS (2019) On the location of fog nodes in fog-cloud infrastructures. Sensors 19(11):2445
Tang B, Chen Z, Hefferman G, Wei T, He H, Yang Q (2015) A hierarchical distributed fog computing architecture for big data analysis in smart cities. In: Proceedings of the ASE BigData & socialInformatics 2015, ACM, New York, ASE BD&SI ’15, pp 28:1–28:6
Wang T, Liang Y, Jia W, Arif M, Liu A, **e M (2019) Coupling resource management based on fog computing in smart city systems. J Netw Comput Appl 135:11–19
Wen Z, Yang R, Garraghan P, Lin T, Xu J, Rovatsos M (2017) Fog orchestration for internet of things services. IEEE Int Comput 21(2):16–24
Wolff RW (1982) Poisson arrivals see time averages. Oper Res 30(2):223–231
Xu Z, Cai Y (2018) Variable neighborhood search for consistent vehicle routing problem. Expert Syst Appl 113:66–76
Yamanaka N, Yamamoto G, Okamoto S, Muranaka T, Fumagalli A (2019) Autonomous driving vehicle controlling network using dynamic migrated edge computer function. In: 2019 21st international conference on transparent optical networks (ICTON), Angers
Yi S, Li C, Li Q (2015) A survey of fog computing: Concepts, applications and issues. In: Proceedings of the 2015 workshop on mobile big data, ACM, New York, Mobidata ’15, pp 37–42
Yousefpour A, Ishigaki G, Jue JP (2017) Fog computing: Towards minimizing delay in the internet of things. In: 2017 IEEE international conference on edge computing (EDGE), pp 17–24
Yusoh ZIM, Tang M (2010) A penalty-based genetic algorithm for the composite SaaS placement problem in the cloud. In: IEEE congress on evolutionary computation, pp 1–8
Zahmatkesh H, Al-Turjman F (2020) Fog computing for sustainable smart cities in the IoT era: Caching techniques and enabling technologies - an overview. Sustainable Cities Soc 59:102139
Zezulka F, Marcon P, Vesely I, Sajdl O (2016) Industry 4.0—an introduction in the phenomenon. IFAC-PapersOnLine 49(25):8–12; 14th IFAC conference on programmable devices and embedded systems PDES 2016
Zhang C (2020) Design and application of fog computing and internet of things service platform for smart city. Future Gener Comput Syst 112:630–640
Zheng P, Wang H, Sang Z (2018) Smart manufacturing systems for industry 4.0: Conceptual framework, scenarios, and future perspectives. Front Mech Eng 13:137–150
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
de Queiroz, T.A., Canali, C., Iori, M., Lancellotti, R. (2022). An Optimization View to the Design of Edge Computing Infrastructures for IoT Applications. In: Misra, S., Kumar Tyagi, A., Piuri, V., Garg, L. (eds) Artificial Intelligence for Cloud and Edge Computing. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-80821-1_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-80821-1_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80820-4
Online ISBN: 978-3-030-80821-1
eBook Packages: Computer ScienceComputer Science (R0)