The Journal of Supercomputing

, Volume 73, Issue 12, pp 5239–5260 | Cite as

FLAPS: bandwidth and delay-efficient distributed data searching in Fog-supported P2P content delivery networks

  • Mohammad Shojafar
  • Zahra Pooranian
  • Paola G. Vinueza Naranjo
  • Enzo Baccarelli


Due to the growing interest for multimedia contents by mobile users, designing bandwidth and delay-efficient distributed algorithms for data searching over wireless (possibly, mobile) “ad hoc” Peer-to-Peer (P2P) content Delivery Networks (CDNs) is a topic of current interest. This is mainly due to the limited computing-plus-communication resources featuring state-of-the-art wireless P2P CDNs. In principle, an effective means to cope with this limitation is to empower traditional P2P CDNs by distributed Fog nodes. Motivated by this consideration, the goal of this paper is twofold. First, we propose and describe the main building blocks of a hybrid (e.g., mixed infrastructure and “ad hoc”) Fog-supported P2P architecture for wireless content delivery, namely, the Fog-Caching P2P architecture. It exploits the topological (possibly, time varying) information locally available at the serving Fog nodes, in order to speed up the data searching operations performed by the served peers. Second, we propose a bandwidth and delay-efficient, distributed and adaptive probabilistic search algorithm, that relies on the learning automata paradigm, e.g., the Fog-supported Learning Automata Adaptive Probabilistic Search (FLAPS) algorithm. The main feature of the FLAPS algorithm is the exploitation of the local topology information provided by the serving Fog nodes and the current status of the collaborating peers, in order to run a suitably distributed reinforcement algorithm for the adaptive discovery of peer-to-peer and peer-to-fog minimum-hop routes. The performance of the proposed FLAPS algorithm is numerically evaluated in terms of Success Rate, Hit-per-Query, Message-per-Query, Response Delay and Message Duplication Factor over a number of randomly generated benchmark CDN topologies. Furthermore, in order to corroborate the actual effectiveness of the FLAPS algorithm, extensive performance comparisons are carried out with some state-of-the-art searching algorithms, namely the Adaptive Probabilistic Search, Improved Adaptive Probabilistic Search and the Random Walk algorithms.


Fog computing Content delivery networks (CDNs) Fog-Caching P2P (FCP2P) Adaptive probabilistic data search (APS) Learning automata (LA) Reinforced Q-learning TCP/IP overlay networks 



This work has been supported by the project “GAUCHO—A Green Adaptive Fog Computing and Networking Architecture” founded by Progetti di Ricerca di Rilevante Interesse Nazionale (PRIN) Bando 2015, and by the project “V-FOG: Vehicular Fog for energy-efficient QoS mining and dissemination of multimedia Big Data streams” founded by Sapienza University of Rome Bando 2016.


  1. 1.
    CISCO (2016) Cisco visual networking index: global mobile data traffic forecast updated, 2015–2020. White paper.
  2. 2.
    Wang X, Chen M, Taleb T, Ksentini A, Leung V (2014) Cache in the air: exploiting content caching and delivery techniques for 5G systems. IEEE Commun Mag 52(2):131–139CrossRefGoogle Scholar
  3. 3.
    Bastug E, Bennis M, Debbah M (2014) Living on the edge: the role of proactive caching in 5G wireless networks. IEEE Commun Mag 52(8):82–89CrossRefGoogle Scholar
  4. 4.
    Li Y, Sun L, Wang W (2014) Exploring device-to-device communication for mobile cloud computing. In: Communications (ICC), 2014 IEEE International Conference on. IEEE, pp 2239–2244Google Scholar
  5. 5.
    Tigelaar AS, Hiemstra D, Trieschnigg D (2012) Peer-to-peer information retrieval: an overview. ACM Trans Inf Syst (TOIS) 30(2):9CrossRefGoogle Scholar
  6. 6.
    Shojafar M, Abawajy JH, Delkhah Z, Ahmadi A, Pooranian Z, Abraham A (2015) An efficient and distributed file search in unstructured peer-to-peer networks. Peer-to-Peer Netw Appl 8(1):120–136CrossRefGoogle Scholar
  7. 7.
    Gkantsidis C, Mihail M, Saberi A (2004) Random walks in peer-to-peer networks. In: INFOCOM 2004. Twenty-third annual joint conference of the IEEE computer and communications societies, vol 1. IEEEGoogle Scholar
  8. 8.
    Li B, Li J, Huai J, Wo T, Li Q, Zhong L (2009) Enacloud: An energy-saving application live placement approach for cloud computing environments. In: Cloud computing, CLOUD’09. IEEE International Conference on. IEEE, pp 17–24Google Scholar
  9. 9.
    Gao Y, Guan H, Qi Z, Hou Y, Liu L (2013) A multi-objective ant colony system algorithm for virtual machine placement in cloud computing. J Comput Syst Sci 79(8):1230–1242CrossRefzbMATHMathSciNetGoogle Scholar
  10. 10.
    Lucas-Simarro JL, Moreno-Vozmediano R, Montero RS, Llorente IM (2013) Scheduling strategies for optimal service deployment across multiple clouds. Future Gener Comput Syst 29(6):1431–1441CrossRefGoogle Scholar
  11. 11.
    Yang L, Cao J, Liang G, Han X (2016) Cost aware service placement and load dispatching in mobile cloud systems. IEEE Trans Comput 65(5):1440–1452CrossRefzbMATHMathSciNetGoogle Scholar
  12. 12.
    Cui Y, Wu Y, Jiang D (2015) Analysis and optimization of caching and multicasting in large-scale cache-enabled information-centric networks. In: Global Communications Conference (GLOBECOM), 2015 IEEE. IEEE, pp 1–7Google Scholar
  13. 13.
    Gnutella forum (2017).
  14. 14.
    Merugu S, Srinivasan S, Zegura E (2003) Adding structure to unstructured peer-to-peer networks: the role of overlay topology. In: Group communications and charges. Technology and business models. Springer, pp 83–94Google Scholar
  15. 15.
    Chawathe Y, Ratnasamy S, Breslau L, Lanham N, Shenker S (2003) Making gnutella-like p2p systems scalable. In: Proceedings of the 2003 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications. ACM, pp 407–418Google Scholar
  16. 16.
    Chowdhury F, Kolberg M (2013) Performance evaluation of structured peer-to-peer overlays for use on mobile networks. In: Developments in eSystems Engineering (DeSE), 2013 Sixth International Conference on. IEEE, pp 57–62Google Scholar
  17. 17.
    Maymounkov P, Mazieres D (2002) Kademlia: A peer-to-peer information system based on the xor metric. In: International Workshop on Peer-to-Peer Systems. Springer, pp 53–65Google Scholar
  18. 18.
    Thampi SM, Sekaran KC (2007) Autonomous data replication using q-learning for unstructured p2p networks. In: Sixth IEEE International Symposium on Network Computing and Applications (NCA). IEEE, pp 311–317Google Scholar
  19. 19.
    Yang B, Garcia-Molina H (2002) Improving search in peer-to-peer networks. In: Distributed Computing Systems, 2002. Proceedings. 22nd International Conference on. IEEE, pp 5–14Google Scholar
  20. 20.
    Di Caro G, Dorigo M (1998) AntNet: distributed stigmergetic control for communications networks. J Artif Intell Res 9:317–365zbMATHGoogle Scholar
  21. 21.
    Michlmayr E (2006) Ant algorithms for search in unstructured peer-to-peer networks. In: 22nd International Conference on Data Engineering Workshops (ICDEW’06). IEEEGoogle Scholar
  22. 22.
    Shojafar M, Cordeschi N, Baccarelli E (2016) Energy-efficient adaptive resource management for real-time vehicular cloud services. IEEE Trans Cloud Comput PP(99):1–14Google Scholar
  23. 23.
    Dabbagh M, Hamdaoui B, Guizani M, Rayes A (2016) An energy-efficient VM prediction and migration framework for overcommitted clouds. IEEE Trans Cloud Comput PP(99):1CrossRefGoogle Scholar
  24. 24.
    Rhea SC, Kubiatowicz J (2002) Probabilistic location and routing. In: INFOCOM, Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, vol. 3. IEEE, pp 1248–1257Google Scholar
  25. 25.
    Hayajna T, Kadoch M (2017) Analysis and enhancements of hello based link failure detection in wireless mesh networks. Telecommun Syst. doi: 10.1007/s11235-017-0293-4
  26. 26.
    Cordeschi N, Amendola D, Shojafar M, Baccarelli E (2015) Distributed and adaptive resource management in cloud-assisted cognitive radio vehicular networks with hard reliability guarantees. Veh Commun 2(1):1–12CrossRefGoogle Scholar
  27. 27.
    Naranjo PGV, Shojafar M, Mostafaei H, Pooranian Z, Baccarelli E (2017) P-SEP: a prolong stable election routing algorithm for energy-limited heterogeneous fog-supported wireless sensor networks. J Supercomput 73(2):733–755Google Scholar
  28. 28.
    Mustafa M, Papatriantafilou M, Schiller EM, Tohidi A, Tsigas P (2012) Autonomous tdma alignment for vanets. In: Vehicular Technology Conference (VTC Fall), 2012 IEEE. IEEE, pp 1–5Google Scholar
  29. 29.
    Tsoumakos D, Roussopoulos N (2003) Adaptive probabilistic search for peer-to-peer networks. In: Peer-to-Peer Computing, P2P 2003. Proceedings. Third International Conference on. IEEE, pp 102–109Google Scholar
  30. 30.
    Peersim: A peer-to-peer simulator (2016)
  31. 31.
    Marti S, Ganesan P, Garcia-Molina H (2004) DHT routing using social links. In: International Workshop on Peer-to-Peer Systems. Springer, pp 100–111Google Scholar
  32. 32.
    Ripeanu M, Foster I (2002) Mapping the gnutella network: Macroscopic properties of large-scale peer-to-peer systems. In: International Workshop on Peer-to-Peer Systems. Springer, pp 85–93Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Information Engineering, Electronic and TelecommunicationSapienza University of RomeRomeItaly
  2. 2.Center of National Consortium Inter-universities in Telecommunication (CNIT)University of Rome – Tor VergataRomeItaly
  3. 3.Department of MathematicUniversity of PadovaPadovaItaly

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