Distributed Heuristic Algorithm for Migration and Replication of Self-organized Services in Future Networks
DOI:
https://doi.org/10.21123/bsj.2022.6338Keywords:
Communication Cost, Heuristic, Migration, Replication, Service PlacementAbstract
Nowadays, the mobile communication networks have become a consistent part of our everyday life by transforming huge amount of data through communicating devices, that leads to new challenges. According to the Cisco Networking Index, more than 29.3 billion networked devices will be connected to the network during the year 2023. It is obvious that the existing infrastructures in current networks will not be able to support all the generated data due to the bandwidth limits, processing and transmission overhead. To cope with these issues, future mobile communication networks must achieve high requirements to reduce the amount of transferred data, decrease latency and computation costs. One of the essential challenging tasks in this subject area is the optimal self-organized service placement. In this paper a heuristic-based algorithm for service placement in future networks was presented. This algorithm achieves the ideal placement of services replicas by monitoring the load within the server and its neighborhood, choosing the node that contributes with the highest received load, and finally replicating or migrating the service to it based on specific criteria, so that the distance of requests coming from clients becomes as small as possible because of placing services within nearby locations. It was proved that our proposed algorithm achieves an improved performance by meeting the services within a shorter time, a smaller bandwidth, and thus a lower communication cost. It was compared with the traditional client-server approach and the random placement algorithm. Experimental results showed that the heuristic algorithm outperforms other approaches and meets the optimal performance with different network sizes and varying load scenarios.
Received 19/5/2021
Accepted 13/10/2021
Published Online First 20/5/2022
References
Ramiro J, Hamied K, editors. Self-organizing networks, self-planning, self-optimization, and self-healing for GSM, UMTS and LTE. USA: Wiley; 2012. 318 p.
Barnett Th, Jain Sh, Andra U, Khurana T. Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2017–2022. APJC Cisco Knowledge Network (CKN).2019 Feb: 33p.
Hachem J, Karamchandani N, Diggavi S. Content caching and delivery over heterogeneous wireless networks. Proc IEEE INFOCOM Online. 2015Aug24, 756-764, DOI: 10.1109/INFOCOM.2015 May.7218445 .
Poularakis K, Llorca J, Tulino A. M, Taylor I, Tassiulas L. Joint Service Placement and Request Routing in Multi-Cell Mobile Edge Computing Networks. Proc IEEE INFOCOM Online. 2019 June17, 10-18, DOI: 10.1109/INFOCOM.2019.8737385.
Gupta A, Jha R K. A Survey of 5G Network: Architecture and Emerging Technologies.IEEE Access.2015Jul.28; 3:1206-1232, doi: 10.1109/ACCESS.2015.2461602.
Chiang M, Zhang T. Fog and IoT: An overview of research opportunities. IEEE Internet Things J. 2016 Dec; 3(6): 854 – 864.
Yang L, Cao J, Liang G, Han, X. Cost aware Service Placement and Load Dispatching in Mobile Cloud Systems. IEEE Trans Comput. 2016 May 01; 65(5): 1440 - 1452, https://doi.org/10.1109/TC.2015.2435781
Abbas N, Zhang Y, Taherkordi A, Skeie T. Mobile Edge Computing: A Survey. IEEE Internet Things J. 2018 Feb; 5(1): 450-465, doi: 10.1109/JIOT.2017.2750180.
Mackenzie C M, Laskey K, McCabe F, Brown P F, Metz R B. Reference Model for Service Oriented Architecture 1.0. OASIS standard. 2006 Aug [cited 2018 Nov 19]; 31p. Available from:
http://docs.oasis-open.org/soa-rm/v1.0/
Xu J, Chen L, Zhou P. Joint Service Caching and Task Offloading for Mobile Edge Computing in Dense Networks. Proc IEEE Infocom Online.2018 Oct 11: 207-215, DOI: 10.1109/INFOCOM.2018.8485977.
Gadegaard S, L. Discrete Location Problems: Theory, algorithms, and extensions to multiple objectives. A PhD Dissertation. Denmark: Department of Economics and Business Economics, Aarhus University;2016.
Reese J. Solution Methods for the p-Median Problem: An Annotated Bibliography. Networks (N Y). 2006 August 01; 48(3):125–142p, DOI: https://doi.org/10.1002/net.20128
Corneújols G, Nemhauser G L, Wolsey L A. The Uncapacitated Facility Location Problem. In: Mirchandani B. P, Francis R. L, editors. Discrete Location Theory..USA:Wiley-Interscience;1990. 99th ed ,576: 1-8.
Laoutaris N, Smaragdakis G, Oikonomou K, Stavrakakis I, Bestavros A. Distributed Placement of Service Facilities in Large-Scale Networks. Proc IEEE Infocom Online. 2007 May: 2144-2152, DOI: 10.1109/INFCOM.2007.248.
Ali S, Mitschele-Thiel A, Diab A, Rasheed A. A Survey of Services Placement Mechanisms for Future Mobile Communication Networks. Association for Computing Machinery, Proceedings of the 8th International Conference on Frontiers of Information Technology, ACM-BCB 2010, FIT '10. 2010 Dec: 1-5, DOI: https://doi.org/10.1145 /1943628.1943667.
Gramoli V, Kermarrec A, Merrer E L, Neveux D. SONDe, a Self-Organizing Object Deployment Algorithm in Large-Scale Dynamic Systems. In: 2008 Seventh European Dependable Computing Conference. IEEE, 2008:157-166, DOI: 10.1109/EDCC-7.2008.17.
Poularakis K, Tassiulas L. Cooperation and information replication in wireless networks. Philos Trans A Math Phys Eng Sci [Internet]. 2016 [cited 2020 Dec 02]; 374(2062): 14p. Available from: https://doi.org/10.1098/rsta.2015.0123
Sahoo J, Salahuddin M A, Glitho R, Elbiaze H, Ajib W. A Survey on Replica Server Placement Algorithms for Content Delivery Networks. IEEE Commun Surv. 2017; 19(2). 1002-1026, DOI: 10.1109/COMST.2016.2626384.
Skarlat O, Nardelli M, Schulte S, Borkowski M, Leitner P. Optimized IoT service placement in the fog. Springer London: Serv Oriented Comput. Appl.11(4). 2017Oct.04, 427–443. DOI: https://doi.org/10.1007/s11761-017-0219-8
Albu-Salih, A T, Hosseini Seno S A. Optimal UAV Deployment for Data Collection in Deadline-based IoT Applications. Baghdad Sci J. 2018Dec 9; 15(4): 0484.
Selimi M, Cerdà-Alabern L, Freitag F, Veiga L, Sathiaseelan A, Crowcroft J. A Lightweight Service Placement Approach for Community Network Micro-Clouds. Springer Netherlands: J Grid Comput. 2019 March28; 17(1): 169–189, DOI: https://doi.org/10.1007/ s10723-018-9437-3
Mueller-Bady R, Kappes M, Medina-Bulo I, Palomo-lozano F. An evolutionary hybrid search heuristic for monitor placement in communication networks. Springer Netherlands: J Heuristics. 2019May02; 25(6): 861-899. DOI: https://doi.org/10.1007/ s10732-019-09414-z
Donassolo B, Fajjari I, Legrand A, Mertikopoulos, P. Fog Based Framework for IoT Service Provisioning. 16th IEEE Annu. Consum. Commun. Netw. Conf. 2019 Feb28: 1-6, DOI: 10.1109/CCNC.2019.8651835.
Diestel R. Graph Theory (Graduate Texts in Mathematics, 173), 5th ed. Berlin, Heidelberg: Springer. Extremal Graph Theory. 2017. Chap7: 173-207.
Ali F, Jawad R. Using Evolving Algorithms to Cryptanalysis Nonlinear Cryptosystems. Baghdad Sci J. 2020June06; 17(2): 1-7, DOI: http://dx.doi.org/10.21123/bsj.2020.17.2(SI).0682
Bonabeau E, Dorigo M, Theraulaz G. Swarm Intelligence: From Natural to Artificial Systems. New York, NY: Oxford University Press, Santa Fe Institute Studies in the Sciences of Complexity; 1999. Chap 2, Ant Foraging Behavior, Combinatorial Optimization, and Routing in Communications Networks, 37P.
Carneiro G. NS-3: Network simulator 3. In: UTM Lab Meeting April. 2010: 4-5.
Downloads
Published
Issue
Section
License
Copyright (c) 2022 Baghdad Science Journal
This work is licensed under a Creative Commons Attribution 4.0 International License.