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Measuring the Fog, Gently

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Service-Oriented Computing (ICSOC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11895))

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Abstract

The availability of suitable monitoring tools and techniques will be crucial to orchestrate multi-service applications in a context- and QoS-aware manner over new Fog infrastructures. In this paper, we propose FogMon, a lightweight distributed prototype monitoring tool, which measures data about hardware resources (viz., CPU, RAM, HDD) at the available Fog nodes, end-to-end network QoS (viz., latency and bandwidth) between those nodes, and detects connected IoT devices. FogMon is organised into a peer-to-peer architecture and it shows a very limited footprint on both hardware and bandwidth. The usage of FogMon on a real testbed is presented.

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Notes

  1. 1.

    Available at: https://github.com/di-unipi-socc/FogMon.

  2. 2.

    We assume that Leaders act as superpeers [24] that are (possibly) deployed to faster, more powerful and more reliable nodes. The known Leader node acts as a registry of Leader identifiers (viz., IP addresses and ports) of all other Leaders, and we assume it to be deployed at a known location.

  3. 3.

    Active bandwidth measurements consist of sending as many bytes as possible over a certain end-to-end link and measuring the ratio between the employed time and the amount of transmitted data. Despite being very reliable, this approach tends to make unstable the connectivity between the considered nodes.

  4. 4.

    Currently, the prototype discovers IoT devices connected through serial ports, any other standard (e.g., Bluetooth, ZigBee) can be supported by extending the IoT package of FogMon.

  5. 5.

    IoT devices directly connected to a node are assumed to reach it with negligible latency and infinite bandwidth.

  6. 6.

    In real large-scale settings, it is up to the infrastructure manager to guarantee a sufficient number of Leaders is available in the Fog network monitored by FogMon. More precisely, we expect Leaders to be deployed either to Cloud nodes or to Fog nodes that naturally manage a subset of Followers (e.g., gateways, building servers, ISP switches).

  7. 7.

    The VMs on AWS feature 1 vCPU, 1 GB of RAM and 8 GB of storage, and run Amazon Linux 2, based on RedHat Enterprise Linux and CentOS. The VMs on Microsoft Azure feature 1 vCPU, 4 GB of RAM and 7 GB of storage, and run Debian 9.9. The VM on the university datacentre features 1 vCPU, 2 GB of RAM and 30 GB of storage, ad runs Ubuntu 18.04. RaspberryPi3 nodes feature a Cortex-A53 (ARMv8) 64-bit SoC 1.4 GHz processor, 1 GB of RAM and 16 GB of storage, and run Raspbian 4.14, but for node A which runs Fedora 28.

  8. 8.

    With reference to Table 1, reporting time was set to 30 s, latency time was set to 30 s and heartbeat time was set to 120 s.

References

  1. Cloud key marketing trends for 2017 and ideas for exceeding customer expectations, IBM Marketing (2017)

    Google Scholar 

  2. Amazon CloudWatch. https://aws.amazon.com/it/cloudwatch/

  3. bmon - portable bandwidth monitor. https://linux.die.net/man/1/bmon

  4. Hyperic’s system information gatherer (sigar). https://github.com/hyperic/sigar/wiki/overview

  5. Iperf. https://software.es.net/iperf/

  6. Libserialport. https://sigrok.org/wiki/Libserialport

  7. Zenoss (2014). http://www.zenoss.com/

  8. Barth, W.: Nagios: System and Network Monitoring. No Starch Press, San Francisco (2008)

    Google Scholar 

  9. Bonomi, F., Milito, R., Natarajan, P., Zhu, J.: Fog computing: a platform for internet of things and analytics. In: Bessis, N., Dobre, C. (eds.) Big Data and Internet of Things: A Roadmap for Smart Environments. SCI, vol. 546, pp. 169–186. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05029-4_7

    Chapter  Google Scholar 

  10. Brandón, Á., Pérez, M.S., Montes, J., Sanchez, A.: Fmone: a flexible monitoring solution at the edge. Wirel. Commun. Mob. Comput. 2018, 15 (2018)

    Article  Google Scholar 

  11. Brogi, A., Forti, S., Guerrero, C., Lera, I.: How to Place Your Apps in the Fog-State of the Art and Open Challenges. preprint arXiv:1901.05717 (2019)

  12. Brogi, A., Forti, S., Ibrahim, A.: How to best deploy your fog applications, probably. In: 2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC), pp. 105–114. IEEE (2017)

    Google Scholar 

  13. Brogi, A., Forti, S., Ibrahim, A., Rinaldi, L.: Bonsai in the Fog: an active learning lab with Fog computing. In: 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC), pp. 79–86. IEEE (2018)

    Google Scholar 

  14. Buyya, R., et al.: A manifesto for future generation cloud computing: research directions for the next decade. ACM Comput. Surv. (CSUR) 51(5), 105 (2018)

    Article  Google Scholar 

  15. CISCO: the internet of things: Extend the cloud to where the things are. Cisco White Paper (2015)

    Google Scholar 

  16. Clayman, S., Galis, A., Mamatas, L.: Monitoring virtual networks with lattice. In: 2010 IEEE/IFIP Network Operations and Management Symposium Workshops (NOMS Wksps), pp. 239–246. IEEE (2010)

    Google Scholar 

  17. Dastjerdi, A.V., Buyya, R.: Fog computing: helping the internet of things realize its potential. Computer 49(8), 112–116 (2016)

    Article  Google Scholar 

  18. Du, W., Liao, Y., Tao, N., Geurts, P., Fu, X., Leduc, G.: Rating network paths for locality-aware overlay construction and routing. IEEE/ACM Trans. Networking (TON) 23(5), 1661–1673 (2015)

    Article  Google Scholar 

  19. Forti, S., Ibrahim, A., Brogi, A.: Mimicking FogDirector application management. Software-Intensive Cyber-Phys. Syst. 34(2–3), 151–161 (2019)

    Article  Google Scholar 

  20. Goldoni, E., Rossi, G., Torelli, A.: Assolo, a new method for available bandwidth estimation. In: 2009 Fourth International Conference on Internet Monitoring and Protection, ICIMP 2009, pp. 130–136. IEEE (2009)

    Google Scholar 

  21. Hu, N.: Network monitoring and diagnosis based on available bandwidth measurement. Technical report, Carnegie-Mellon University, Pittsburgh, PA School of Computer Science (2006)

    Google Scholar 

  22. Issariyapat, C., Pongpaibool, P., Mongkolluksame, S., Meesublak, K.: Using Nagios as a groundwork for developing a better network monitoring system. In: 2012 Proceedings of PICMET 2012 Technology Management for Emerging Technologies (PICMET), pp. 2771–2777. IEEE (2012)

    Google Scholar 

  23. Jelasity, M.: Gossip. In: Di Marzo Serugendo, G., Gleizes, M.P., Karageorgos, A. (eds.) Self-organising Software. NCS, pp. 139–162. Springer, Berlin (2011). https://doi.org/10.1007/978-3-642-17348-6_7

    Chapter  Google Scholar 

  24. Jesi, G.P., Montresor, A., Babaoglu, O.: Proximity-aware superpeer overlay topologies. IEEE Trans. Netw. Serv. Manag. 4(2), 74–83 (2007)

    Article  Google Scholar 

  25. Liao, Y., Du, W., Geurts, P., Leduc, G.: DMFSGD: a decentralized matrix factorization algorithm for network distance prediction. IEEE/ACM Trans. Networking (TON) 21(5), 1511–1524 (2013)

    Article  Google Scholar 

  26. Marttinen, A., et al.: Estimating kpis in deployed heterogeneous networks. IEEE Commun. Mag. 54(10), 158–165 (2016)

    Article  Google Scholar 

  27. Massie, M.L., Chun, B.N., Culler, D.E.: The ganglia distributed monitoring system: design, implementation, and experience. Parallel Comput. 30(7), 817–840 (2004)

    Article  Google Scholar 

  28. Mongkolluksamee, S., Pongpaibool, P., Issariyapat, C.: Strengths and limitations of Nagios as a network monitoring solution. In: Proceedings of the 7th International Joint Conference on Computer Science and Software Engineering (JCSSE 2010), Bangkok, Thailand. pp. 96–101 (2010)

    Google Scholar 

  29. Montes, J., Sánchez, A., Memishi, B., Pérez, M.S., Antoniu, G.: GMonE: a complete approach to cloud monitoring. Future Gener. Comput. Syst. 29(8), 2026–2040 (2013)

    Article  Google Scholar 

  30. Noghabi, S.A., Kolb, J., Bodik, P., Cuervo, E.: Steel: Simplified development and deployment of edge-cloud applications. In: 10th \(\{\)USENIX\(\}\) Workshop on Hot Topics in Cloud Computing (HotCloud 18) (2018)

    Google Scholar 

  31. Povedano-Molina, J., Lopez-Vega, J.M., Lopez-Soler, J.M., Corradi, A., Foschini, L.: Dargos: a highly adaptable and scalable monitoring architecture for multi-tenant clouds. Future Gener. Comput. Syst. 29(8), 2041–2056 (2013)

    Article  Google Scholar 

  32. Rahimi, M.R., Ren, J., Liu, C.H., Vasilakos, A.V., Venkatasubramanian, N.: Mobile cloud computing: a survey, state of art and future directions. Mobile Networks Appl. 19(2), 133–143 (2014)

    Article  Google Scholar 

  33. Satyanarayanan, M., et al.: Edge analytics in the internet of things. IEEE Pervasive Comput. 14(2), 24–31 (2015)

    Article  Google Scholar 

  34. Simmonds, E., Harrington, J.: SCF/FEF Evaluation of Nagios and Zabbix Monitoring Systems, pp. 1–9 (2009). www.scopus.com, cited By: 2

  35. Skarlat, O., Bachmann, K., Schulte, S.: Fogframe: Iot service deployment and execution in the fog. KuVS-Fachgespräch Fog Comput. 1, 5–8 (2018)

    Google Scholar 

  36. Song, Y., Yau, S.S., Yu, R., Zhang, X., Xue, G.: An approach to QoS-based task distribution in edge computing networks for IoT applications. In: 2017 IEEE International Conference on Edge Computing (EDGE), pp. 32–39. IEEE (2017)

    Google Scholar 

  37. Souza, A., Cacho, N., Noor, A., Jayaraman, P.P., Romanovsky, A., Ranjan, R.: Osmotic monitoring of microservices between the edge and cloud. In: 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pp. 758–765 (2018)

    Google Scholar 

  38. Tader, P.: Server monitoring with zabbix. Linux J. 2010(195), 7 (2010)

    Google Scholar 

  39. 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 

  40. Vögler, M., Schleicher, J.M., Inzinger, C., Dustdar, S.: Diane-dynamic IoT application deployment. In: 2015 IEEE International Conference on Mobile Services, pp. 298–305. IEEE (2015)

    Google Scholar 

  41. Wöbker, C., Seitz, A., Mueller, H., Bruegge, B.: Fogernetes: deployment and management of fog computing applications. In: NOMS 2018–2018 IEEE/IFIP Network Operations and Management Symposium, pp. 1–7. IEEE (2018)

    Google Scholar 

  42. Xie, K., et al.: Sequential and adaptive sampling for matrix completion in network monitoring systems. In: 2015 IEEE Conference on Computer Communications (INFOCOM), pp. 2443–2451. IEEE (2015)

    Google Scholar 

  43. Yip, M.: Rapidjson-a fast json parser/generator for c++ with both sax/dom style api. THL A29. https://github.com/miloyip/rapidjson (2015)

  44. Yousefpour, A., et al.: All one needs to know about fog computing and related edge computing paradigms: a complete survey. J. Syst. Architect. 98, 289–330 (2019)

    Article  Google Scholar 

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Acknowledgements

This work has been partly supported by the project “DECLWARE: Declarative methodologies of application design and deployment” (PRA_2018_66), funded by University of Pisa, Italy, and by the project “GIÒ: a Fog computing testbed for research & education”, funded by the Department of Computer Science of the University of Pisa, Italy.

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Authors and Affiliations

  1. Department of Computer Science, University of Pisa, Pisa, Italy

    Antonio Brogi, Stefano Forti & Marco Gaglianese

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  1. Antonio Brogi
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  2. Stefano Forti
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  3. Marco Gaglianese
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Correspondence to Stefano Forti .

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Editors and Affiliations

  1. Laboratory for Analysis and Architecture, Toulouse, France

    Sami Yangui

  2. National Engineering School of Sfax, Sfax, Tunisia

    Ismael Bouassida Rodriguez

  3. Laboratory for Analysis and Architecture, Toulouse, France

    Khalil Drira

  4. RMIT University, Melbourne, VIC, Australia

    Zahir Tari

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Brogi, A., Forti, S., Gaglianese, M. (2019). Measuring the Fog, Gently. In: Yangui, S., Bouassida Rodriguez, I., Drira, K., Tari, Z. (eds) Service-Oriented Computing. ICSOC 2019. Lecture Notes in Computer Science(), vol 11895. Springer, Cham. https://doi.org/10.1007/978-3-030-33702-5_40

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  • DOI: https://doi.org/10.1007/978-3-030-33702-5_40

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