[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Design and energy-efficient resource management of virtualized networked Fog architectures for the real-time support of IoT applications

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

With the incoming 5G access networks, it is forecasted that Fog computing (FC) and Internet of Things (IoT) will converge onto the Fog-of-IoT paradigm. Since the FC paradigm spreads, by design, networking and computing resources over the wireless access network, it would enable the support of computing-intensive and delay-sensitive streaming applications under the energy-limited wireless IoT realm. Motivated by this consideration, the goal of this paper is threefold. First, it provides a motivating study the main “killer” application areas envisioned for the considered Fog-of-IoT paradigm. Second, it presents the design of a CoNtainer-based virtualized networked computing architecture. The proposed architecture operates at the Middleware layer and exploits the native capability of the Container Engines, so as to allow the dynamic real-time scaling of the available computing-plus-networking virtualized resources. Third, the paper presents a low-complexity penalty-aware bin packing-type heuristic for the dynamic management of the resulting virtualized computing-plus-networking resources. The proposed heuristic pursues the joint minimization of the networking-plus-computing energy by adaptively scaling up/down the processing speeds of the virtual processors and transport throughputs of the instantiated TCP/IP virtual connections, while guaranteeing hard (i.e., deterministic) upper bounds on the per-task computing-plus-networking delays. Finally, the actual energy performance-versus-implementation complexity trade-off of the proposed resource manager is numerically tested under both wireless static and mobile Fog-of-IoT scenarios and comparisons against the corresponding performances of some state-of-the-art benchmark resource managers and device-to-device edge computing platforms are also carried out.

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
£29.99 /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 includes VAT (United Kingdom)

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

Similar content being viewed by others

Notes

  1. https://www.gaucho.unifi.it/.

References

  1. Borgia E (2014) The internet of things vision: key features, applications and open issues. Comput Commun 54:1–31

    Article  Google Scholar 

  2. Ouaddah A, Mousannif H, Elkalam AA, Ouahman AA (2017) Access control in the internet of things: big challenges and new opportunities. Comput Netw 112:237–262

    Article  Google Scholar 

  3. Bonomi F, Milito R, Zhu J, Addepalli S (2012) Fog computing and its role in the internet of things. In: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing. ACM, pp 13–16

  4. Baccarelli E, Biagi M (2004) Power-allocation policy and optimized design of multiple-antenna systems with imperfect channel estimation. IEEE Trans Veh Technol 53(1):136–145

    Article  Google Scholar 

  5. Baccarelli E, Biagi M, Pelizzoni C, Cordeschi N (2008) Optimal MIMO UWB-IR transceiver for Nakagami-fading and Poisson-arrivals. JCM 3(1):27–40

    Article  Google Scholar 

  6. Shojafar M, Cordeschi N, Amendola D, Baccarelli E (2015) Energy-saving adaptive computing and traffic engineering for real-time-service data centers. In: 2015 IEEE International Conference on Communication Workshop (ICCW 2015), London, UK, pp 1800–1806

  7. Gupta H, Dastjerdi AV, Ghosh SK, Buyya R (2016) iFogSim: a toolkit for modeling and simulation of resource management techniques in internet of things, edge and fog computing environments. arXiv preprint arXiv:1606.02007

  8. Beloglazov A, Abawajy J, Buyya R (2012) Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener Comput Syst 28(5):755–768

    Article  Google Scholar 

  9. Lovász G, Niedermeier F, de Meer H (2013) Performance tradeoffs of energy-aware virtual machine consolidation. Clust Comput 16(3):481–496

    Article  Google Scholar 

  10. Chang H, Kodialam M, Kompella RR, Lakshman T, Lee M, Mukherjee S (2011) Scheduling in mapreduce-like systems for fast completion time. In: 2011 Proceedings IEEE INFOCOM. IEEE, pp 3074–3082

  11. Guazzone M, Anglano C, Canonico M (2011) Energy-efficient resource management for cloud computing infrastructures. In: Proceedings of the IEEE Third International Conference on Cloud Computing Technology and Science (CloudSim 2011), Athens, Greece, pp 1–11

  12. Verma A, Cherkasova L, Kumar VS, Campbell RH (2012) Deadline-based workload management for mapreduce environments: pieces of the performance puzzle. In: Network Operations and Management Symposium (NOMS), 2012 IEEE. IEEE, pp 900–905

  13. Lim N, Majumdar S, Ashwood-Smith P (2014) A constraint programming-based resource management technique for processing mapreduce jobs with SLAs on clouds. In: 43rd International Conference on Parallel Processing (ICPP 2014). IEEE, pp 411–421

  14. Wirtz T, Ge R (2011) Improving mapreduce energy efficiency for computation intensive workloads. In: 2011 International Green Computing Conference and Workshops (IGCC). IEEE, pp 1–8

  15. Kim KH, Buyya R, Kim J (2007) Power aware scheduling of bag-of-tasks applications with deadline constraints on DVS-enabled clusters. In: Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2007), vol 7, pp 541–548

  16. Beloglazov A, Buyya R (2010) Energy efficient resource management in virtualized cloud data centers. In: Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing. IEEE Computer Society, pp 826–831

  17. Cardosa M, Singh A, Pucha H, Chandra A (2012) Exploiting spatio-temporal tradeoffs for energy-aware mapreduce in the cloud. IEEE Trans Comput 61(12):1737–1751

    Article  MathSciNet  MATH  Google Scholar 

  18. Çavdar D, Chen LY, Alagöz F (2014) Green mapreduce for heterogeneous data centers. In: 2014 IEEE Global Communications Conference (GLOBECOM). IEEE, pp 1120–1126

  19. Chiang Y-J, Ouyang Y-C, Hsu C-HR (2015) An efficient green control algorithm in cloud computing for cost optimization. IEEE Trans Cloud Comput 3(2):145–155

    Article  Google Scholar 

  20. Baccarelli E, Cusani R (1996) Recursive Kalman-type optimal estimation and detection of hidden Markov chains. Sig Process 51(1):55–64

    Article  MATH  Google Scholar 

  21. Neumeyer L, Robbins B, Nair A, Kesari A (2010) S4: distributed stream computing platform. In: 2010 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, pp 170–177

  22. Zaharia M, Das T, Li H, Shenker S, Stoica I (2012) Discretized streams: an efficient and fault-tolerant model for stream processing on large clusters. In: Proceedings of the 4th USENIX Conference on Hot Topics in Cloud Computing (HotCloud), vol 12, p 10

  23. Qian Z, He Y, Su C, Wu Z, Zhu H, Zhang T, Zhou L, Yu Y, Zhang Z (2013) Timestream: reliable stream computation in the cloud. In: Proceedings of the 8th ACM European Conference on Computer Systems. ACM, pp 1–14

  24. Kumbhare AG, Simmhan Y, Prasanna VK (2014) Plasticc: predictive look-ahead scheduling for continuous dataflows on clouds. In: 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2014). IEEE, pp 344–353

  25. Bonomi F (2011) Connected vehicles, the internet of things, and fog computing. In: Proceedings of the Eighth ACM International Workshop on Vehicular Internetworking, Las Vegas, pp 1–5

  26. Kai K, Cong W, Tao L (2016) Fog computing for vehicular ad-hoc networks: paradigms, scenarios, and issues. J China Univ Posts Telecommun 23(2):56–96

    Article  Google Scholar 

  27. Shojafar M, Cordeschi N, Baccarelli E (2016) Energy-efficient adaptive resource management for real-time vehicular cloud services. IEEE Trans Cloud Comput. https://doi.org/10.1109/TCC.2016.2551747

    Google Scholar 

  28. Baccarelli E, Vinueza Naranjo PG, Scarpiniti M, Shojafar M, Abawajy JH, Abawajy JH (2017) Fog of everything: energy-efficient networked computing architectures, research challenges, and a case study. IEEE Access 5:9882–9910

    Article  Google Scholar 

  29. Peralta G, Iglesias-Urkia M, Barcelo M, Gomez R, Moran A, Bilbao J (2017) Fog computing based efficient IoT scheme for the industry 4.0. In: Proceedings of the 2017 International Workshop of Electronics, Control, Measurement, Signals and Their Application to Mechatronics (ECMSM 2017), Donostia-San Sebastian, Spain, pp 24–26

  30. Tao F, Zuo Y, Da Xu L, Zhang L (2014) IoT-based intelligent perception and access of manufacturing resource toward cloud manufacturing. IEEE Trans Ind Inform 10(2):1547–1557

    Article  Google Scholar 

  31. Ma Y, Wang X, Zhou X, Gao Z, Wu Y, Yin J, Xu X (2016) An overview of energy internet. In: 2016 Chinese Control and Decision Conference (CCDC). IEEE, pp 6212–6215

  32. Baccarelli E, Cordeschi N, Mei A, Panella M, Shojafar M, Stefa J (2016) Energy-efficient dynamic traffic offloading and reconfiguration of networked data centers for big data stream mobile computing: review, challenges, and a case study. IEEE Netw 30(2):54–61

    Article  Google Scholar 

  33. Yovanof GS, Hazapis GN (2009) An architectural framework and enabling wireless technologies for digital cities and intelligent urban environments. Wirel Pers Commun 49(3):445–463

    Article  Google Scholar 

  34. Baccarelli E, Cordeschi N, Polli V (2013) Optimal self-adaptive QoS resource management in interference-affected multicast wireless networks. IEEE/ACM Trans Netw (TON) 21(6):1750–1759

    Article  Google Scholar 

  35. Portnoy M (2012) Virtualization essentials. Wiley, New York

    Google Scholar 

  36. Soltesz S, Pötzl H, Fiuczynski ME, Bavier A, Peterson L (2007) Container-based operating system virtualization: a scalable, high-performance alternative to hypervisors. In: ACM SIGOPS Operating Systems Review, vol 41. ACM, pp 275–287

  37. Kwak J, Kim Y, Lee J, Chong S (2015) DREAM: dynamic resource and task allocation for energy minimization in mobile cloud systems. IEEE J Sel Areas Commun 33(12):2510–2523

    Article  Google Scholar 

  38. McCarthy D, Malone P, Hange J, Doyle K, Robson E, Conway D, Ivanov S, Radziwonowicz L, Kleinfeld R, Michalareas T, Kastrinogiannis T, Stasinos N, Lampathaki F (2015) Personal cloudlets: implementing a user-centric datastore with privacy aware access control for cloud-based data platforms. In: Proceedings of the First International Workshop on TEchnical and LEgal Aspects of Data pRIvacy and SEcurity (TELERISE), Florence, Italy, pp 38–43

  39. Huang X, Xiang Y, Bertino E, Zhou J, Xu L (2014) Robust multi-factor authentication for fragile communications. IEEE Trans Dependable Secure Comput 11(6):568–581

    Article  Google Scholar 

  40. Stojmenovic I, Wen S, Huang X, Luan H (2016) An overview of fog computing and its security issues. Concurr Comput Pract Exp 28(10):2991–3005

    Article  Google Scholar 

  41. Dsouza C, Ahn G-J, Taguinod M (2014) Policy-driven security management for fog computing: preliminary framework and a case study. In: 2014 IEEE 15th International Conference on Information Reuse and Integration (IRI), Redwood City, CA, USA, pp 16–23

  42. Abdo J, Demerjian J, Chaouchi H, Atechian T, Bassil C (2015) Privacy using mobile cloud computing. In: 2015 Fifth International Conference on Digital Information and Communication Technology and its Applications (DICTAP). Lebanese University, Beirut, Lebanon, pp 178–182

  43. Wang C, Ren K, Wang J (2016) Secure optimization computation outsourcing in cloud computing: a case study of linear programming. IEEE Trans Comput 65(1):216–229

    Article  MathSciNet  MATH  Google Scholar 

  44. Perez R, Sailer R, Van Doorn L (2006) vTPM: virtualizing the trusted platform module. In: Proceedings of 15th Conference on USENIX Security Symposium, pp 305–320

  45. Hong K, Lillethun D, Ramachandran U, Ottenwälder B, Koldehofe B (2013) Mobile fog: a programming model for large-scale applications on the internet of things. In: Proceedings of the Second ACM SIGCOMM Workshop on Mobile Cloud Computing, Hong Kong, China, pp 15–20

  46. Kansal A, Zhao F, Liu J, Kothari N, Bhattacharya AA (2010) Virtual machine power metering and provisioning. In: Proceedings of the 1st ACM Symposium on Cloud Computing. ACM, pp 39–50

  47. Urgaonkar B, Pacifici G, Shenoy P, Spreitzer M, Tantawi A (2007) Analytic modeling of multitier internet applications. ACM Trans Web (TWEB) 1(1):2

    Article  Google Scholar 

  48. Gulati A, Merchant A, Varman PJ (2010) mClock: handling throughput variability for hypervisor IO scheduling. In: Proceedings of the 9th USENIX Conference on Operating Systems Design and Implementation. USENIX Association, pp 437–450

  49. Guo C, Lu G, Wang HJ, Yang S, Kong C, Sun P, Wu W, Zhang Y (2010) SecondNet: a data center network virtualization architecture with bandwidth guarantees. In: Proceedings of the 6th International Conference on Emerging Networking Experiments and Technologies (CoNEXT). ACM, p 15

  50. Iyengar SS, Brooks RR (2012) Distributed sensor networks: sensor networking and applications. CRC Press, Boca Raton

    Book  MATH  Google Scholar 

  51. Da Costa F (2013) Rethinking the Internet of Things: a scalable approach to connecting everything. Apress, New York

    Google Scholar 

  52. Zhou Z, Liu F, Xu Y, Zou R, Xu H, Lui JCS, Jin H (2013) Carbon-aware load balancing for geo-distributed cloud services. In: 2013 IEEE 21st International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems. IEEE, pp 232–241

  53. Abe Y, Geambasu R, Joshi K, Lagar-Cavilla HA, Satyanarayanan M (2013) vTube: efficient streaming of virtual appliances over last-mile networks. In: Proceedings of the ACM 4th Annual Symposium on Cloud Computing, Santa Clara, CA, USA, p 16, 1–3 Oct 2013

  54. Baccarelli E, Cusani R, Galli S (1998) A novel adaptive receiver with enhanced channel tracking capability for TDMA-based mobile radio communications. IEEE J Sel Areas Commun 16(9):1630–1639

    Article  Google Scholar 

  55. Taleb T, Ksentini A (2016) Follow me cloud: interworking federated clouds and distributed mobile networks. IEEE Netw 27(5):12–19

    Article  Google Scholar 

  56. Gandotra P, Jha RK, Jain S (2017) A survey on device-to-device (D2D) communication: architecture and security issues. J Netw Comput Appl 78:9–29

    Article  Google Scholar 

  57. Byers CC, Wetterwald P (2015) Ubiquity symposium: the Internet of Things: fog computing: distributing data and intelligence for resiliency and scale necessary for IoT. Ubiquity 11:1–12

    Article  Google Scholar 

  58. Kaur R, Mahajan M (2015) Fault tolerance in cloud computing. Int J Sci Technol Manag (IJSTM) 6(1):1–4

    Google Scholar 

Download references

Acknowledgements

This work has been developed under the umbrella of the PRIN2015 project with Grant No. 2015YPXH4W_004: “A green adaptive FC and networking architecture (GAUChO),” funded by the Italian MIUR. Also, it has also been partially supported by the projects “Vehicular Fog energy-efficient QoS mining and dissemination of multimedia Big Data streams (V-Fog and V-Fog2),” funded by Sapienza University of Rome in Italy. The authors would like to thank all anonymous reviewers for their precious and helpful comments and suggestions.

Author information

Authors and Affiliations

  1. Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, Rome, Italy

    Paola G. Vinueza Naranjo, Enzo Baccarelli & Michele Scarpiniti

Authors
  1. Paola G. Vinueza Naranjo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Enzo Baccarelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michele Scarpiniti
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paola G. Vinueza Naranjo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vinueza Naranjo, P.G., Baccarelli, E. & Scarpiniti, M. Design and energy-efficient resource management of virtualized networked Fog architectures for the real-time support of IoT applications. J Supercomput 74, 2470–2507 (2018). https://doi.org/10.1007/s11227-018-2274-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-018-2274-0

Keywords

Search

Navigation