{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T07:26:51Z","timestamp":1790234811524,"version":"4.1.0"},"reference-count":29,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,9,21]],"date-time":"2019-09-21T00:00:00Z","timestamp":1569024000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Fog computing is becoming popular as a solution to support applications based on geographically distributed sensors that produce huge volumes of data to be processed and filtered with response time constraints. In this scenario, typical of a smart city environment, the traditional cloud paradigm with few powerful data centers located far away from the sources of data becomes inadequate. The fog computing paradigm, which provides a distributed infrastructure of nodes placed close to the data sources, represents a better solution to perform filtering, aggregation, and preprocessing of incoming data streams reducing the experienced latency and increasing the overall scalability. However, many issues still exist regarding the efficient management of a fog computing architecture, such as the distribution of data streams coming from sensors over the fog nodes to minimize the experienced latency. The contribution of this paper is two-fold. First, we present an optimization model for the problem of mapping data streams over fog nodes, considering not only the current load of the fog nodes, but also the communication latency between sensors and fog nodes. Second, to address the complexity of the problem, we present a scalable heuristic based on genetic algorithms. We carried out a set of experiments based on a realistic smart city scenario: the results show how the performance of the proposed heuristic is comparable with the one achieved through the solution of the optimization problem. Then, we carried out a comparison among different genetic evolution strategies and operators that identify the uniform crossover as the best option. Finally, we perform a wide sensitivity analysis to show the stability of the heuristic performance with respect to its main parameters.<\/jats:p>","DOI":"10.3390\/a12100201","type":"journal-article","created":{"date-parts":[[2019,9,23]],"date-time":"2019-09-23T03:26:32Z","timestamp":1569209192000},"page":"201","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":80,"title":["GASP: Genetic Algorithms for Service Placement in Fog Computing Systems"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8448-7693","authenticated-orcid":false,"given":"Claudia","family":"Canali","sequence":"first","affiliation":[{"name":"Department of Engineering \u201cEnzo Ferrari\u201d, University of Modena and Reggio Emilia, Via P. Vivarelli 10\/1, 41125 Modena, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9470-8784","authenticated-orcid":false,"given":"Riccardo","family":"Lancellotti","sequence":"additional","affiliation":[{"name":"Department of Engineering \u201cEnzo Ferrari\u201d, University of Modena and Reggio Emilia, Via P. Vivarelli 10\/1, 41125 Modena, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1016\/j.future.2017.02.017","article-title":"Secure intelligent traffic light control using fog computing","volume":"78","author":"Liu","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sasaki, K., Suzuki, N., Makido, S., and Nakao, A. (2016, January 20\u201323). Vehicle Control System Coordinated Between Cloud and Mobile Edge Computing. Proceedings of the 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), Tsukuba, Japan.","DOI":"10.1109\/SICE.2016.7749210"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yi, S., Li, C., and Li, Q. (2015, January 21). A Survey of Fog Computing: Concepts, Applications and Issues. Proceedings of the 2015 Workshop on Mobile Big Data, Hangzhou, China.","DOI":"10.1145\/2757384.2757397"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/MC.2016.245","article-title":"Fog Computing: Helping the Internet of Things Realize Its Potential","volume":"49","author":"Dastjerdi","year":"2016","journal-title":"Computer"},{"key":"ref_5","first-page":"1171","article-title":"Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption","volume":"3","author":"Deng","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yousefpour, A., Ishigaki, G., and Jue, J.P. (2017, January 25\u201330). Fog Computing: Towards Minimizing Delay in the Internet of Things. Proceedings of the 2017 IEEE International Conference on Edge Computing (EDGE), Honolulu, HI, USA.","DOI":"10.1109\/IEEE.EDGE.2017.12"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yusoh, Z.I.M., and Tang, M. (2010, January 18\u201323). A Penalty-Based Genetic Algorithm for the Composite SaaS Placement Problem in the Cloud. Proceedings of the IEEE Congress on Evolutionary Computation, Barcelona, Spain.","DOI":"10.1109\/CEC.2010.5586151"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Canali, C., and Lancellotti, R. (2019, January 2\u20134). A Fog Computing Service Placement for Smart Cities based on Genetic Algorithms. Proceedings of the International Conference on Cloud Computing and Services Science (CLOSER 2019), Crete, Greece.","DOI":"10.5220\/0007699400810089"},{"key":"ref_9","unstructured":"Bigi, A., Veratti, G., Fabbi, S., Ziven, O., Po, L., and Ghermandi, G. (2019, January 3\u20136). Forecast of the Impact by Local Emissions at An Urban Micro Scale by the Combination of Lagrangian Modelling and Low Cost Sensing Technology: The TRAFAIR Project. Proceedings of the 19th International Conference on Harmionisation within Atmospheric Dispersion Modelling for Regulatory Purposes, Bruges, Belgium."},{"key":"ref_10","unstructured":"Shojafar, M., Canali, C., and Lancellotti, R. (2017, January 24\u201326). A Computation- and Network-Aware Energy Optimization Model for Virtual Machines Allocation. Proceedings of the International Conference on Cloud Computing and Services Science (CLOSER 2017), Porto, Portugal."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Shojafar, M., Canali, C., Lancellotti, R., and Abolfazli, S. (2016, January 23\u201325). An Energy-aware Scheduling Algorithm in DVFS-enabled Networked Data Centers. Proceedings of the 6th International Conference on Cloud Computing and Services Science, Rome, Italy.","DOI":"10.5220\/0005928903870397"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jnca.2018.03.002","article-title":"Optimization of live virtual machine migration in cloud computing: A survey and future directions","volume":"110","author":"Noshy","year":"2018","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.future.2016.02.016","article-title":"Energy-aware scheduling of virtual machines in heterogeneous cloud computing systems","volume":"74","author":"Duan","year":"2017","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_14","unstructured":"Ardagna, D., Ciavotta, M., Lancellotti, R., and Guerriero, M. (2018). A hierarchical receding horizon algorithm for QoS-driven control of Multi-IaaS applications. IEEE Trans. Cloud Comput."},{"key":"ref_15","unstructured":"(2019, July 10). Knitro Website. Available online: https:\/\/www.artelys.com\/solvers\/knitro\/."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1007\/s00607-016-0498-5","article-title":"Scalable and automatic virtual machines placement based on behavioral similarities","volume":"99","author":"Canali","year":"2017","journal-title":"Computing"},{"key":"ref_17","first-page":"137","article-title":"A survey of bio inspired optimization algorithms","volume":"2","author":"Binitha","year":"2012","journal-title":"Int. J. Soft Comput. Eng."},{"key":"ref_18","unstructured":"Back, T., Fogel, D., and Michalewicz, Z. (2002). Evolutionary Computation 1: Basic Algorithms and Operators, CRC Press."},{"key":"ref_19","unstructured":"(2019, September 19). DEAP: Distributed Evolutionary Algorithms in Pyton. Available online: https:\/\/deap.readthedocs.io."},{"key":"ref_20","unstructured":"Cicirello, V.A., and Smith, S.F. (2000, January 10\u201312). Modeling GA Performance for Control Parameter Optimization. Proceedings of the 2nd Annual Conference on Genetic and Evolutionary Computation, Las Vegas, NV, USA."},{"key":"ref_21","unstructured":"(2019, September 19). AMPL: Streamlined Modeling for Real Optimization. Available online: https:\/\/ampl.com\/."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Tang, B., Chen, Z., Hefferman, G., Wei, T., He, H., and Yang, Q. (2015, January 7\u20139). A Hierarchical Distributed Fog Computing Architecture for Big Data Analysis in Smart Cities. Proceedings of the ASE BigData & SocialInformatics 2015, Kaohsiung, Taiwan.","DOI":"10.1145\/2818869.2818898"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/MIC.2017.36","article-title":"Fog orchestration for internet of things services","volume":"21","author":"Wen","year":"2017","journal-title":"IEEE Internet Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.future.2019.05.015","article-title":"Improving fog computing performance via Fog-2-Fog collaboration","volume":"100","author":"Baker","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ferr\u00e1ndez-Pastor, F.J., Mora, H., Jimeno-Morenilla, A., and Volckaert, B. (2018). Deployment of IoT edge and fog computing technologies to develop smart building services. Sustainability, 10.","DOI":"10.3390\/su10113832"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Maamar, Z., Baker, T., Faci, N., Ugljanin, E., Khafajiy, M.A., and Bur\u00e9gio, V. (2019, January 8\u201312). Towards a Seamless Coordination of Cloud and Fog: Illustration through the Internet-of-Things. Proceedings of the 34th ACM\/SIGAPP Symposium on Applied Computing, Limassol, Cyprus.","DOI":"10.1145\/3297280.3297477"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Nair, B., and Saira Bhanu, S.M. (2019). Fog-Cloud Collaboration for Real-Time Streaming Applications: FCC for RTSAs. Handbook of Research on the IoT, Cloud Computing, and Wireless Network Optimization, IGI Global.","DOI":"10.4018\/978-1-5225-7335-7.ch007"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Cardellini, V., Grassi, V., Lo Presti, F., and Nardelli, M. (2016, January 20\u201324). Optimal Operator Placement for Distributed Stream Processing Applications. Proceedings of the 10th ACM International Conference on Distributed and Event-Based Systems, Irvine, CA, USA.","DOI":"10.1145\/2933267.2933312"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.1007\/s11227-016-1814-8","article-title":"QoS-aware service composition in cloud computing using data mining techniques and genetic algorithm","volume":"73","author":"Karimi","year":"2017","journal-title":"J. Supercomput."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/12\/10\/201\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:22:39Z","timestamp":1760188959000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/12\/10\/201"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,21]]},"references-count":29,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["a12100201"],"URL":"https:\/\/doi.org\/10.3390\/a12100201","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,21]]}}}