Abstract
Fog computing has been recently introduced to complement the cloud computing paradigm and offer application services at the edge of the network. The heterogeneity of fog computational nodes makes application placement in fog infrastructures a challenging task that requires proper management in order to satisfy application requirements. This paper proposes a bi-objective application placement algorithm for fog computing environments. The proposed algorithm seeks to optimally place application modules on the underlying fog devices considering applications criticality levels and security requirements. The placement problem has been formulated as a bi-objective knapsack problem and solved using the non-dominated sorting genetic algorithm II (NSGA-II). It has been implemented using a specialized fog computing simulation tool and compared against existing placement algorithms. Simulation results demonstrate the ability of the proposed algorithm to optimize application placement in fog computing environments in terms of application performance, power efficiency and security satisfaction rates.
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References
Adhikari M, Srirama SN, Amgoth T (2020) Application offloading strategy for hierarchical fog environment through swarm optimization. IEEE Internet Things J 7(5):4317–4328
Afrin M, Jin J, Rahman A, Tian YC, Kulkarni A (2019) Multi-objective resource allocation for edge cloud based robotic workflow in smart factory. Futur Gener Comput Syst 97:119–130
Al-Tarawneh M (2020) Bi-objective application placement implementation in the ifogsim simulator. https://github.com/mutazaltarawneh/iFogSim-NSGA/tree/master
Arkian HR, Diyanat A, Pourkhalili A (2017) Mist: fog-based data analytics scheme with cost-efficient resource provisioning for iot crowdsensing applications. J Netw Comput Appl 82:152–165
Auluck N, Rana O, Nepal S, Jones A, Singh A (2019) Scheduling real time security aware tasks in fog networks. IEEE Trans Serv Comput 1:1–1
Bittencourt LF, Lopes MM, Petri I, Rana OF (2015) Towards virtual machine migration in fog computing. In: 2015 10th international conference on P2P, parallel, grid, cloud and internet computing (3PGCIC), pp 1–8
Bonomi F, Milito R, Natarajan P, Zhu J (2014) Fog computing: a platform for internet of things and analytics. Springer, Cham, pp 169–186
Brogi A, Forti S (2017) Qos-aware deployment of iot applications through the fog. IEEE Internet Things J 4(5):1185–1192
Brogi A, Forti S, Guerrero C, Lera I (2019a) How to place your apps in the fog: State of the art and open challenges. Pract Exp, Software, pp 1–22
Brogi A, Forti S, Ibrahim A (2019b) Optimising qos-assurance, resource usage and cost of fog application deployments. In: Muñoz VM, Ferguson D, Helfert M, Pahl C (eds) Cloud Comput Serv Sci. Springer, Cham, pp 168–189
Buyya R, Srirama SN (2019) Modeling and simulation of fog and edge computing environments using iFogSim toolkit. Wiley, Hoboken, pp 433–465
Capota EA, Stangaciu CS, Micea MV, Curiac DI (2019) Towards mixed criticality task scheduling in cyber physical systems: challenges and perspectives. J Syst Softw 156:204–216
Cheng MY, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112
CISCO (2019) Internet of things connected means informed. https://www.cisco.com/c/en_in/index.html?country-redirect=true
Cormen TH, Stein C, Rivest RL, Leiserson CE (2009) Introd Algorithms, 3rd edn. MIT Press, Cambridge
Dadmehr Rahbari MN (2019) Low-latency and energy-efficient scheduling in fog-based iot applications. Turkish J Electr Eng Comput Sci 27:1406–1427
Dalvand FM, Zamanifar K (2019) Multi-objective service provisioning in fog: a trade-off between delay and cost using goal programming. In: 2019 27th Iranian conference on electrical engineering (ICEE), pp 2050–2056
Deb K, Kalyanmoy D (2001) Multi-objective optimization using evolutionary algorithms. Wiley, New York
Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans Evolut Comput 6(2):182–197
Dudhe PV, Kadam NV, Hushangabade RM, Deshmukh MS, (2017) Internet of things (iot): an overview, and its applications. (2017) International conference on energy. communication, data analytics and soft computing (ICECDS), pp 2650–2653
Forti S, Ferrari GL, Brogi A (2020) Secure cloud-edge deployments, with trust. Futur Gener Comput Syst 102:775–788
Giang NK, Blackstock M, Lea R, Leung VCM (2015) Developing iot applications in the fog: a distributed dataflow approach. In: 2015 5th international conference on the internet of things (IOT), pp 155–162
Gu L, Zeng D, Guo S, Barnawi A, Xiang Y (2017) Cost efficient resource management in fog computing supported medical cyber-physical system. IEEE Trans Emerg Top Comput 5(1):108–119
Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of things (iot): a vision, architectural elements, and future directions. Futur Gener Comput Syst 29(7):1645–1660
Guerrero C, Lera I, Juiz C (2019) A lightweight decentralized service placement policy for performance optimization in fog computing. J Ambient Intell Hum Comput 10(6):2435–2452
Gupta H, Vahid Dastjerdi A, Ghosh SK, Buyya R (2017) ifogsim: a toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Softw Pract Exp 47(9):1275–1296
Hong H, Tsai P, Hsu C (2016) Dynamic module deployment in a fog computing platform. In: 2016 18th Asia-Pacific network operations and management symposium (APNOMS), pp 1–6
Hughes A, Awad A (2019) Quantifying performance determinism in virtualized mixed-criticality systems. In: 2019 IEEE 22nd international symposium on real-time distributed computing (ISORC), pp 181–184
Kavitha D, Ravikumar S (2020) Designing an iot based autonomous vehicle meant for detecting speed bumps and lanes on roads. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-020-02419-8
Lera I, Guerrero C, Juiz C (2019) Availability-aware service placement policy in fog computing based on graph partitions. IEEE Internet Things J 6(2):3641–3651
Mahmud R, Ramamohanarao K, Buyya R (2018) Latency-aware application module management for fog computing environments. ACM Trans Internet Technol 19(1):9:1–9:21
Mahmud R, Ramamohanarao K, Buyya R (2019a) Edge affinity-based management of applications in fog computing environments. In: Proceedings of the 12th IEEE/ACM international conference on utility and cloud computing, association for computing machinery, New York, NY, USA, p 61–70
Mahmud R, Srirama SN, Ramamohanarao K, Buyya R (2019b) Quality of experience (qoe)-aware placement of applications in fog computing environments. J Parallel Distrib Comput 132:190–203
Mahmud R, Toosi AN, Rao K, Buyya R (2019c) Context-aware placement of industry 4.0 applications in fog computing environments. IEEE Trans Ind Inf. pp 1–1
Mahmud R, Srirama SN, Ramamohanarao K, Buyya R (2020) Profit-aware application placement for integrated fog-cloud computing environments. J Parallel Distrib Comput 135:177–190
Mann ZA (2020) Secure software placement and configuration. Futur Gener Comput Syst 110:243–253
Mann ZA, Metzger A, Prade J, Seidl R (2020) Optimized application deployment using fog and cloud computing environments. In: Felderer M, Hasselbring W, Rabiser R, Jung R (eds) Software Engineering 2020. Gesellschaft für Informatik e.V, Bonn, pp 117–119
Matnei Filho RA, Vergilio SR (2016) A multi-objective test data generation approach for mutation testing of feature models. J Softw Eng Res Dev 4(1):4
Nardelli M, Cardellini V, Grassi V, Presti FL (2019) Efficient operator placement for distributed data stream processing applications. IEEE Trans Parallel Distrib Syst 30(8):1753–1767
Ni J, Zhang K, Lin X, Shen XS (2018) Securing fog computing for internet of things applications: challenges and solutions. IEEE Commun Surv Tutor 20(1):601–628
Rahbari D, Nickray M (2017) Scheduling of fog networks with optimized knapsack by symbiotic organisms search. In: 2017 21st conference of open innovations association (FRUCT), pp 278–283
Saleem K, Bajwa IS, Sarwar N, Anwar W, Ashraf A (2020) Iot healthcare: design of smart and cost-effective sleep quality monitoring system. J Sens 2020:8882378
Shakdher A, Agrawal S, Yang B (2019) Security vulnerabilities in consumer iot applications. In: 2019 IEEE 5th Intl conference on big data security on cloud (BigDataSecurity), IEEE intl conference on high performance and smart computing, (HPSC) and IEEE intl conference on intelligent data and security (IDS), pp 1–6
Skarlat O, Nardelli M, Schulte S, Dustdar S (2017) Towards qos-aware fog service placement. In: 2017 IEEE 1st international conference on fog and edge computing (ICFEC), pp 89–96
Souza VB, Masip-Bruin X, Marin-Tordera E, Ramirez W, Sanchez S (2016a) Towards distributed service allocation in fog-to-cloud (f2c) scenarios. In: 2016 IEEE global communications conference (GLOBECOM), pp 1–6
Souza VBC, Ramírez W, Masip-Bruin X, Marín-Tordera E, Ren G, Tashakor G (2016b) Handling service allocation in combined fog-cloud scenarios. In: 2016 IEEE international conference on communications (ICC), pp 1–5
Stojmenovic I, Wen S, Huang X, Luan H (2016) An overview of fog computing and its security issues. Concurr Comput Pract Exper 28(10):2991–3005
Suter M, Eidenbenz R, Pignolet Y, Singla A (2019) Fog application allocation for automation systems. In: 2019 IEEE international conference on fog computing (ICFC), pp 97–106
Tan K, Lee T, Khor E (2002) Evolutionary algorithms for multi-objective optimization: performance assessments and comparisons. Artif Intell Rev 17(4):251–290
Taneja M, Davy A (2017) Resource aware placement of iot application modules in fog-cloud computing paradigm. In: 2017 IFIP/IEEE symposium on integrated network and service management (IM), pp 1222–1228
Tuli S, Mahmud R, Tuli S, Buyya R (2019) Fogbus: a blockchain-based lightweight framework for edge and fog computing. J Syst Softw 154:22–36
Vangala A, Das AK, Kumar N, Alazab M (2020) Smart secure sensing for iot-based agriculture: blockchain perspective. IEEE Sens J 1:1–1
Velasquez K, Abreu DP, Curado M, Monteiro E (2017) Service placement for latency reduction in the internet of things. Ann Telecommun 72:105–115
Wang S, Zafer M, Leung KK (2017) Online placement of multi-component applications in edge computing environments. IEEE Access 5:2514–2533
Yang L, Cao J, Liang G, Han X (2016) Cost aware service placement and load dispatching in mobile cloud systems. IEEE Trans Comput 65(5):1440–1452
Yu W, Liang F, He X, Hatcher WG, Lu C, Lin J, Yang X (2018) A survey on the edge computing for the internet of things. IEEE Access 6:6900–6919
Zafari F, Li J, Leung KK, Towsley D, Swami A (2018) A game-theoretic approach to multi-objective resource sharing and allocation in mobile edge. In: Proceedings of the 2018 on technologies for the wireless edge workshop, association for computing machinery, New York, NY, USA, p 9–13
Zakarya M, Gillam L, Ali H, Rahman I, Salah K, Khan R, Rana O, Buyya R (2020) epcaware: a game-based, energy, performance and cost efficient resource management technique for multi-access edge computing. IEEE Trans Serv Comput 1:1–1
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Al-Tarawneh, M.A.B. Bi-objective optimization of application placement in fog computing environments. J Ambient Intell Human Comput 13, 445–468 (2022). https://doi.org/10.1007/s12652-021-02910-w
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DOI: https://doi.org/10.1007/s12652-021-02910-w