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Bi-objective optimization of application placement in fog computing environments

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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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Google Scholar 

  • Brogi A, Forti S (2017) Qos-aware deployment of iot applications through the fog. IEEE Internet Things J 4(5):1185–1192

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Chapter  Google Scholar 

  • Buyya R, Srirama SN (2019) Modeling and simulation of fog and edge computing environments using iFogSim toolkit. Wiley, Hoboken, pp 433–465

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Cheng MY, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112

    Article  Google Scholar 

  • 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

    MATH  Google Scholar 

  • Dadmehr Rahbari MN (2019) Low-latency and energy-efficient scheduling in fog-based iot applications. Turkish J Electr Eng Comput Sci 27:1406–1427

    Article  Google Scholar 

  • 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

    MATH  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Mann ZA (2020) Secure software placement and configuration. Futur Gener Comput Syst 110:243–253

    Article  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Vangala A, Das AK, Kumar N, Alazab M (2020) Smart secure sensing for iot-based agriculture: blockchain perspective. IEEE Sens J 1:1–1

    Google Scholar 

  • Velasquez K, Abreu DP, Curado M, Monteiro E (2017) Service placement for latency reduction in the internet of things. Ann Telecommun 72:105–115

    Article  Google Scholar 

  • Wang S, Zafer M, Leung KK (2017) Online placement of multi-component applications in edge computing environments. IEEE Access 5:2514–2533

    Article  Google Scholar 

  • 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

    Article  MathSciNet  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

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Correspondence to Mutaz A. B. Al-Tarawneh.

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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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