Skip to main content

Advertisement

Springer Nature Link
Log in

JarvSis: a distributed scheduler for IoT applications

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

JarvSis is a distributed scheduler capable to automate the execution of multiple heterogeneous tasks on IoT and Robotics applications by means of a modular and adaptable software architecture. JarvSis is designed to accept pluggable modules that make it adaptable to any devices, from simple sensors to complex robots, that, in turn, expose remote interfaces, i.e. Web-API, MQTT or ROS message bus. Through JarvSis, the developer can easily configure and deploy hierarchies of control tasks running in the Cloud and in the Fog in order to interact and control IoT devices or robots that operate in the ground. Control tasks are organized in a hierarchical network on which Fog resources represent a bridge between the computational resources hosted in the Cloud, and IoT devices or robots operating in the “ground”. In such a network, the highest layer provides control and coordination, and is typically hosted in the Cloud, while the last layer is distributed in the Fog. The advantages provided by JarvSis are discussed by a detailed example in the robotic domain.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Notes

  1. e.g., a micro-controller with the needed firmware.

  2. The list may not be exhaustive.

  3. Here the period is intended to be not critical, thus it is treated in a best-effort way.

  4. The needed configurations can be easily generated for each sub-area in order to represent a specific mission that must be performed by a specific robot.

  5. https://github.com/h2r/java_rosbridge

References

  1. http://obsidianscheduler.com/

  2. http://quartz-scheduler.org

  3. Introducing json. www.json.org

  4. Introduction to platform lsf. http://www.ibm.com/support/knowledgecenter/SSETD4_9.1.3/lsf_foundations/lsf_introduction_to.html

  5. Mosquitto. An open source mqtt v3.1/v3.1.1 broker. https://mosquitto.org/

  6. Openlava. Open source workload management. www.openlava.org

  7. Paho java client library. https://eclipse.org/paho/clients/java/

  8. Bic suite. http://independit.com/bicsuite/ (2016)

  9. Schedulix —“open source enterprise job scheduling”. http://www.schedulix.org (2016)

  10. Baccelli, E., Hahm, O., Gunes, M., Wahlisch, M., Schmidt, TC.: Riot OS: towards an OS for the internet of things. In: Computer Communications Workshops (INFOCOM WKSHPS), 2013 IEEE Conference, pp. 79–80. IEEE (2013)

  11. Banks, A., Gupta, R.: Mqtt version 3.1.1. http://docs.oasis-open.org/mqtt/mqtt/v3.1.1/os/mqtt-v3.1.1-os.html (2014)

  12. De Benedetti, M., D’Urso, F., Messina, F., Pappalardo, G., Santoro, C.: Self-organising UAVs for wide area fault-tolerant aerial monitoring. In: Proceedings of the 16th Workshop “From Objects to Agents”, pp. 135–141. Naples, Italy, 17–19 June 2015 (2015)

  13. Bonomi, F., Milito, R., Natarajan, P., Zhu, J.: Fog computing: a platform for internet of things and analytics. In: Big Data and Internet of Things: A Roadmap for Smart Environments, pp. 169–186. Springer, Berlin (2014)

  14. Botta, A., de Donato, W., Persico, V., Pescapé, A.: Integration of cloud computing and internet of things: a survey. Future Gener. Comput. Syst. 56, 684–700 (2016)

    Article  Google Scholar 

  15. Boyinbode, O., Le, H., Takizawa, M.: A survey on clustering algorithms for wireless sensor networks. Int. J. Space-Based Situat. Comput. 1(2–3), 130–136 (2011)

    Article  Google Scholar 

  16. Comi, A., Fotia, L., Messina, F., Pappalardo, G., Rosaci, D., Sarné, G.M.: An evolutionary approach for cloud learning agents in multi-cloud distributed contexts. In: Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), 2015 IEEE 24th International Conference, pp. 99–104. IEEE (2015)

  17. Adaptive computing and green computing. Torque resource manager. http://www.adaptivecomputing.com (2016)

  18. De Benedetti, M., D’Urso, F., Messina, F., Pappalardo, G., Santoro, C.: UAV-based aerial monitoring: a performance evaluation of a self-organising flocking algorithm. In: P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), 2015 10th International Conference, pp. 248–255. IEEE (2015)

  19. De Benedetti, M., D’Urso, F., Messina, F., Pappalardo, G., Santoro, C.: UAV-based aerial monitoring: a performance evaluation of a self-organising flocking algorithm. In: P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), 2015 10th International Conference, pp. 248–255. IEEE (2015)

  20. Eswaran, S.P., Bapat, J.: Service centric markov based spectrum sharing for internet of things (iot). In: Region 10 Symposium (TENSYMP), 2015 IEEE, pp. 9–12. IEEE (2015)

  21. Fletcher, P.: Introduction to signalir. https://www.asp.net/signalr/overview/getting-started/introduction-to-signalr (2014)

  22. Fortino, G., Di Fatta, G., Pathan, M., Vasilakos, A.V.: Cloud-assisted body area networks: state-of-the-art and future challenges. Wirel. Netw. 20(7), 1925–1938 (2014)

    Article  Google Scholar 

  23. Fortino, G., Garro, A., Russo, W.: Achieving mobile agent systems interoperability through software layering. Inf. & Softw. Technol. 50(4), 322–341 (2008)

    Article  Google Scholar 

  24. Fortino, G., Guerrieri, A., Russo, W., Savaglio, C.: Integration of agent-based and cloud computing for the smart objects-oriented iot. In: Proceedings of the IEEE 18th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2014, pp. 493–498. Hsinchu, Taiwan, 21-23 May 2014 (2014)

  25. Fortino, G., Parisi, D., Pirrone, V., Di Fatta, G.: Bodycloud: a saas approach for community body sensor networks. Future Generation Computer Systems, 35, 62–79 (2014). Fortino, G., Pathan, M (Guest eds) Special section: Integration of Cloud Computing and Body Sensor Networks

  26. Gravina, R., Ma, C., Pace, P., Aloi, G., Russo,W., Li, W., Fortino,G.: Cloud-based activity-aaservice cyber—physical framework for human activity monitoring in mobility. Future Gener Comput Syst (2016)

  27. Liu, C.L., Layland, J.W.: Scheduling algorithms for multiprogramming in a hard-real-time environment. J. ACM 20(1), 46–61 (1973)

    Article  MathSciNet  MATH  Google Scholar 

  28. Messina, F., Pappalardo, G., Rosaci, D., Sarné, GM.: An agent based architecture for vm software tracking in cloud federations. In: Complex, Intelligent and Software Intensive Systems (CISIS), 2014 Eighth International Conference, pp. 463–468. IEEE (2014)

  29. Messina, F., Pappalardo, G., Rosaci, D., Sarné, GM: A trust-based, multi-agent architecture supporting inter-cloud vm migration in iaas federations. In: International Conference on Internet and Distributed Computing Systems, pp. 74–83. Springer (2014)

  30. Messina, F., Pappalardo, G., Santoro, C.: Decentralised resource finding and allocation in cloud federations. In: Intelligent Networking and Collaborative Systems (INCoS), 2014 International Conference, pp. 26–33. IEEE (2014)

  31. Introducing Microsoft. Net. Microsoft press, Redmond (2002)

  32. Puzar, M., Plagemann, T.: Data sharing in mobile ad-hoc networks—a study of replication and performance in the midas data space. Int. J. Space-Based Situat. Comput. 1(2–3), 137–150 (2011)

    Article  Google Scholar 

  33. Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: Ros: an open-source robot operating system. In: ICRA workshop on open source software, vol. 3, p. 5. Kobe (2009)

  34. Reuther, A., Byun, C., Arcand, W., Bestor, D., Bergeron, B, Hubbell, M., Jones, M., Michaleas, P., Prout, A., Rosa, A., et al.: Scheduler technologies in support of high performance data analysis. arXiv preprint arXiv:1607.06544, 2016

  35. Senobary, Saeed, Naghibzadeh, Mahmoud: Semi-partitioned scheduling for fixed-priority real-time tasks based on intelligent rate monotonic algorithm. Int. J. Grid Util. Comput. 6(3–4), 184–191 (2015)

    Article  Google Scholar 

  36. OpenPBS Team. A batching queuing system. Software Project, Altair Grid Technologies, LLC, www.openpbs.org

  37. Vaquero, L.M., Merino, L.R.: Finding your way in the fog: towards a comprehensive definition of fog computing. ACM SIGCOMM Comput. Commun. Rev. 44(5), 27–32 (2014)

    Article  Google Scholar 

  38. Wei, X., Li, W.-X., Ran, C., Pi, C-C., Ma, Y.-J., Sheng, Y.-X.: Architecture and scheduling method of cloud video surveillance system based on iot. In: International Conference on Algorithms and Architectures for Parallel Processing, pp. 551–560. Springer (2015)

  39. Wen, Yean-Fu, Chang, Chih-Lung: Load balancing consideration of both transmission and process responding time for multi-task assignment. Int. J. Space-Based Situat. Comput. 4(2), 100–113 (2014)

    Article  MathSciNet  Google Scholar 

  40. Xia, F., Yang, L.T., Wang, L., Vinel, A.: Internet of things. Int. J. Commun. Syst. 25(9), 1101 (2012)

    Article  Google Scholar 

  41. Yuriyama, Madoka, Kushida, Takayuki: Integrated cloud computing environment with it resources and sensor devices. Int. J. Space-Based Situat. Comput. 1(2–3), 163–173 (2011)

    Article  Google Scholar 

  42. Zhao, Bo, Wenjie, Hu, Zheng, Qiang, Cao, Guohong: Energy-aware web browsing on smartphones. IEEE Trans. Parallel Distrib. Syst. 26(3), 761–774 (2015)

    Article  Google Scholar 

Download references

Acknowledgements

This work is partially supported by projects PRISMA PON04a2 A/F and CLARA funded by the Italian MIUR.

Author information

Authors and Affiliations

  1. Department of Mathematics and Informatics, University of Catania, Viale A. Doria, 6, 95126, Catania, Italy

    M. De Benedetti, F. Messina, G. Pappalardo & C. Santoro

Authors
  1. M. De Benedetti
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. F. Messina
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. G. Pappalardo
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. C. Santoro
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to F. Messina.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

De Benedetti, M., Messina, F., Pappalardo, G. et al. JarvSis: a distributed scheduler for IoT applications. Cluster Comput 20, 1775–1790 (2017). https://doi.org/10.1007/s10586-017-0836-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-0836-1

Keywords