Skip to main content

Declarative Application Management in the Fog

A Bacteria-Inspired Decentralised Approach

Abstract

Orchestrating next-gen applications over heterogeneous resources along the Cloud-IoT continuum calls for new strategies and tools to enable scalable and application-specific managements. Inspired by the self-organisation capabilities of bacteria colonies, we propose a declarative, fully decentralised application management solution, targeting pervasive opportunistic Cloud-IoT infrastructures. We present a customisable declarative implementation of the approach and validate its scalability through simulation over motivating scenarios, also considering end-user’s mobility and the possibility to enforce application-specific management policies for different (classes of) applications.

References

  1. 1.

    Habibi, P., Farhoudi, M., Kazemian, S., Khorsandi, S., Leon-Garcia, A.: Fog computing: a comprehensive architectural survey. IEEE Access 8, 69105–69133 (2020)

    Article  Google Scholar 

  2. 2.

    Villari, M., Fazio, M., Dustdar, S., Rana, O., Jha, D.N., Ranjan, R.: Osmosis: The osmotic computing platform for microelements in the cloud, edge, and internet of things. Computer 52(8), 14–26 (2019)

    Article  Google Scholar 

  3. 3.

    Pham, Q.-V., Fang, F., Ha, V.N., Piran, M.J., Le, M., Le, L.B., Hwang, W.-J., Ding, Z.: A survey of multi-access edge computing in 5g and beyond: fundamentals, technology integration, and state-of-the-art. IEEE Access 8, 116974–117017 (2020)

    Article  Google Scholar 

  4. 4.

    Filali, A., Abouaomar, A., Cherkaoui, S., Kobbane, A., Guizani, M.: Multi-access edge computing: A survey. IEEE Access (2020)

  5. 5.

    Brogi, A., Forti, S., Guerrero, C., Lera, I.: How to place your apps in the fog: state of the art and open challenges. Softw. Pract. Exp. 50(5), 719–740 (2020)

    Article  Google Scholar 

  6. 6.

    Mahmud, R., Ramamohanarao, K., Buyya, R.: Application management in fog computing environments: a taxonomy, review and future directions. ACM Comput. Surv., 53(4) (2020)

  7. 7.

    Vaquero, L.M., Cuadrado, F., Elkhatib, Y., Bernal-Bernabe, J., Srirama, S.N., Zhani, M.F.: Research challenges in nextgen service orchestration. Future Gener. Comput. Syst. 90, 20–38 (2019)

    Article  Google Scholar 

  8. 8.

    Ghobaei-Arani, M., Souri, A., Rahmanian, A.A.: Resource management approaches in fog computing: a comprehensive review. J. Grid Comput. 18(1), 1–42 (2020). https://doi.org/10.1007/s10723-019-09491-1

    Article  Google Scholar 

  9. 9.

    Forti, S., Brogi, A.: Continuous reasoning for managing next-gen distributed applications. In: ICLP Technical Communications 2020, ser. EPTCS, vol. 325, pp. 164–177 (2020)

  10. 10.

    Guerrero, C., Lera, I., Juiz, C.: Migration-aware genetic optimization for mapreduce scheduling and replica placement in hadoop. J. Grid Comput. 16(2), 265–284 (2018)

    Article  Google Scholar 

  11. 11.

    Fernando, N., Loke, S.W., Avazpour, I., Chen, F., Abkenar, A.B., Ibrahim, A.: Opportunistic fog for IoT: challenges and opportunities. IEEE Internet Things J. 6(5), 8897–8910 (2019)

    Article  Google Scholar 

  12. 12.

    Casadei, R., Fortino, G., Pianini, D., Russo, W., Savaglio, C., Viroli, M.: Modelling and simulation of opportunistic IoT services with aggregate computing. Future Gener. Comput. Syst. 91, 252–262 (2019)

    Article  Google Scholar 

  13. 13.

    Lera, I., Guerrero, C., Juiz, C.: Algoritmo descentralizado para la asignación de servicios en arquitecturas de fog computing basado en un proceso expansivo de migración de instancias in Jornadas SARTECO (2019)

  14. 14.

    Dazzi, P., Mordacchini, M.: Scalable decentralized indexing and querying of multi-streams in the fog. J. Grid Comput. 18(3), 395–418 (2020). https://doi.org/10.1007/s10723-020-09521-3

    Article  Google Scholar 

  15. 15.

    Brogi, A., Forti, S., Guerrero, C., Lera, I.: Towards declarative decentralised application management in the fog. In: GAUSS. In press (2020)

  16. 16.

    Lera, I., Guerrero, C., Juiz, C.: YAFS: A simulator for IoT scenarios in Fog computing. IEEE Access 7, 91745–91758 (2019)

    Article  Google Scholar 

  17. 17.

    Bracciale, L., Bonola, M., Loreti, P., Bianchi, G., Amici, R., Rabuffi, A.: CRAWDAD dataset roma/taxi (v. 2014-07-17). https://crawdad.org/roma/taxi/20140717/taxicabs (2014)

  18. 18.

    Urry, L.A., Cain, M.L., Wasserman, S., Minorsky, P., Jane, R.: Campbell Biology, 11th edn. Pearson, London (2017)

    Google Scholar 

  19. 19.

    Bayles, K.W.: Bacterial programmed cell death: making sense of a paradox. Nature Rev. Microbiol. 12(1), 63–69 (2014)

    Article  Google Scholar 

  20. 20.

    Brogi, A., Forti, S., Gaglianese, M.: Measuring the fog, gently. In: ICSOC, pp. 523–538 (2019)

  21. 21.

    Forti, S., Gaglianese, M., Brogi, A.: Lightweight self-organising distributed monitoring of Fog infrastructures. Future Gener. Comput. Syst. 114, 605–618 (2021). (In press)

    Article  Google Scholar 

  22. 22.

    Taherizadeh, S., Jones, A.C., Taylor, I., Zhao, Z., Stankovski, V.: Monitoring self-adaptive applications within edge computing frameworks: a state-of-the-art review. J. Syst. Softw. 136, 19–38 (2018)

    Article  Google Scholar 

  23. 23.

    Guerrero, C., Lera, I., Juiz, C.: Evaluation and efficiency comparison of evolutionary algorithms for service placement optimization in fog architectures. Future Gener. Comput. Syst. 97, 131–144 (2019)

    Article  Google Scholar 

  24. 24.

    Drabent, W.: The prolog debugger and declarative programming. In: International Symposium on Logic-Based Program Synthesis and Transformation, pp. 193–208. Springer (2019)

  25. 25.

    Brogi, A., Forti, S., Guerrero, C., Lera, I.: Meet genetic algorithms in monte carlo: optimised placement of multi-service applications in the fog. In: 2019 IEEE International Conference on Edge Computing (EDGE). IEEE, pp. 13–17 (2019)

  26. 26.

    Pietri, I., Sakellariou, R.: Mapping virtual machines onto physical machines in cloud computing: a survey. ACM Comput. Surv. 49(3), 1–30 (2016)

    Article  Google Scholar 

  27. 27.

    Tomarchio, O., Calcaterra, D., Modica, G.D.: Cloud resource orchestration in the multi-cloud landscape: a systematic review of existing frameworks. J. Cloud Comput. 9, 49 (2020). https://doi.org/10.1186/s13677-020-00194-7

    Article  Google Scholar 

  28. 28.

    Kadioglu, S., Colena, M., Sebbah, S.: Heterogeneous resource allocation in Cloud Management. In: NCA, pp. 35–38 (2016)

  29. 29.

    Yin, Q., Schüpbach, A., Cappos, J., Baumann, A., Roscoe, T.: Rhizoma: a runtime for self-deploying, self-managing overlays. In: Middleware 2009, pp. 184–204 (2009)

  30. 30.

    Carlini, E., Coppola, M., Dazzi, P., Mordacchini, M.: A Passarella, Self-optimising decentralised service placement in heterogeneous cloud federation. In: SASO, pp. 110–119 (2016)

  31. 31.

    Sathiaseelan, A., Selimi, M., Molina, C., Lertsinsrubtavee, A., Navarro, L., Freitag, F., Ramos, F., Baig, R.: Towards decentralised resilient community clouds. In: MECC, pp. 1–6 (2017)

  32. 32.

    Ferrer, A.J., Marquès, J.M., Jorba, J.: Towards the decentralised cloud: survey on approaches and challenges for mobile, ad hoc, and edge computing. ACM Comput. Surv. 51(6), 1–36 (2019)

    Article  Google Scholar 

  33. 33.

    Xiang, Z., Deng, S., Taheri, J., Zomaya, A.Y.: Dynamical service deployment and replacement in resource-constrained edges. Mob. Netw. Appl. 25(2), 674–689 (2020). https://doi.org/10.1007/s11036-019-01449-7

    Article  Google Scholar 

  34. 34.

    Colistra, G., Pilloni, V., Atzori, L.: The problem of task allocation in the internet of things and the consensus-based approach. Comput. Networks 73, 98–111 (2014)

    Article  Google Scholar 

  35. 35.

    Mennes, R., Spinnewyn, B., Latré, S., F. Botero, J.: GRECO: A distributed genetic algorithm for reliable application placement in hybrid clouds. In: CloudNet, pp. 14–20 (2016)

  36. 36.

    Guerrero, C., Lera, I., Juiz, C.: A lightweight decentralized service placement policy for performance optimization in fog computing. J. Ambient Intell. Humaniz. Comput. 10(6), 2435–2452 (2019)

    Article  Google Scholar 

  37. 37.

    Herrera, J., Moltó, G.: Toward bio-inspired auto-scaling algorithms: An elasticity approach for container orchestration platforms. IEEE Access 8, 52139–52150 (2020)

    Article  Google Scholar 

  38. 38.

    Rossi, F., Cardellini, V., Lo Presti, F., Nardelli, M.: Geo-distributed efficient deployment of containers with kubernetes. Comput. Commun. 159, 161–174 (2020)

    Article  Google Scholar 

  39. 39.

    Hinrichs, T.L., Gude, N.S., Casado, M., Mitchell, J.C., Shenker, S.: Practical declarative network management. In: WREN, pp. 1–10 (2009)

  40. 40.

    Herden, S., Zwanziger, A., Robinson, P: Declarative application deployment and change management. In: CNSM, pp. 126–133 (2010)

  41. 41.

    Forti, S., Paganelli, F., Brogi, A.: Probabilistic QoS-aware placement of VNF chains at the Edge. Theory Pract. Log. Program. In press (2021)

  42. 42.

    Forti, S., Ferrari, G., Brogi, A.: Secure Cloud-Edge deployments, with trust. Future Gener. Comput. Syst. 102, 775–788 (2020)

    Article  Google Scholar 

  43. 43.

    Harzenetter, L., Breitenbücher, U., Leymann, F., Saatkamp, K., Weder, B., Wurster, M: Automated generation of management workflows for applications based on deployment models. In: EDOC, pp. 216–225 (2019)

  44. 44.

    Casadei, R., Viroli, M: Coordinating computation at the edge: a decentralized, self-organizing, spatial approach. In: FMEC 2019, pp. 60–67 (2019), https://doi.org/10.1109/FMEC.2019.8795355

  45. 45.

    Pianini, D., Casadei, R., Viroli, M., Natali, A.: Partitioned integration and coordination via the self-organising coordination regions pattern. Future Gener. Comput. Syst. 114, 44–68 (2021)

    Article  Google Scholar 

  46. 46.

    Viroli, M., Beal, J., Damiani, F., Audrito, G., Casadei, R., Pianini, D.: From field-based coordination to aggregate computing. In: COORDINATION 2018, vol. 10852, pp. 252–279. Springer (2018)

  47. 47.

    Margariti, S.V., Dimakopoulos, V.V., Tsoumanis, G.: Modeling and simulation tools for fog computing–a comprehensive survey from a cost perspective. Future Internet 12(5), 89 (2020)

    Article  Google Scholar 

  48. 48.

    Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: iFogSim: A toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Soft. Pract. Exp. 47(9), 1275–1296 (2017)

    Article  Google Scholar 

  49. 49.

    Sonmez, C., Ozgovde, A., Ersoy, C.: EdgeCloudSim: An environment for performance evaluation of edge computing systems. Trans. Emerg. Telecommun. Technol. 29, e3493 (2018)

    Article  Google Scholar 

  50. 50.

    Forti, S., Pagiaro, A., Brogi, A.: Simulating FogDirector application management. Simul. Model. Pract. Theory 101(102021), 1–18 (2020)

    Google Scholar 

  51. 51.

    Kurdi, H.A., Aldaood, M.F., Al-Megren, S., Aloboud, E., Aldawood, A.S., Youcef-Toumi, K.: Adaptive task allocation for multi-uav systems based on bacteria foraging behaviour. Appl. Soft Comput. 83 (2019)

  52. 52.

    Ahsan, M.M., Gupta, K.D., Nag, A.K., Pouydal, S., Kouzani, A.Z., Mahmud, M.P.: Applications and evaluations of bio-inspired approaches in cloud security: a review. IEEE Access (2020)

  53. 53.

    Chang, Y.-C., Cai, W.-X., Jhuang, J.-W.: Bacteria-inspired communication mechanism based on software-defined network. In: WOCC, pp. 1–3 (2018)

  54. 54.

    Chao, H., Cho, H., Shih, T.K., Chen, C.: Bacteria-inspired network for 5g mobile communication. IEEE Netw. 33(4), 138–145 (2019)

    Article  Google Scholar 

Download references

Acknowledgements

This work has been partly supported by project “Lightweight Self-adaptive Cloud-IoT Monitoring across Fed4FIRE+ Testbed” (LiSCIo) funded by Fed4Fire+, by project “Continuous QoS-compliant Management of Software Applications over the Cloud-IoT Continuum” (CONTWARE) funded by the Conference of Italian University Rectors and by the Spanish Government (AEI) and the EU Commission (Fondo Europeo de Desarrollo Regional) through grant number TIN2017-88547-P (MINECO / AEI / FEDER, UE).

Funding

Open access funding provided by Università di Pisa within the CRUI-CARE Agreement.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Stefano Forti.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Author names are ordered alphabetically. All authors have contributed equally to this study.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Brogi, A., Forti, S., Guerrero, C. et al. Declarative Application Management in the Fog. J Grid Computing 19, 45 (2021). https://doi.org/10.1007/s10723-021-09582-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10723-021-09582-y

Keywords

  • Fog computing
  • User mobility
  • Declarative programming
  • Bio-inspired solution
  • Cloud-IoT continuum