Abstract
A decentralized optimization policy for service placement in fog computing is presented. The optimization is addressed to place most popular services as closer to the users as possible. The experimental validation is done in the iFogSim simulator and by comparing our algorithm with the simulator’s built-in policy. The simulation is characterized by modeling a microservice-based application for different experiment sizes. Results showed that our decentralized algorithm places most popular services closer to users, improving network usage and service latency of the most requested applications, at the expense of a latency increment for the less requested services and a greater number of service migrations.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Notes
The devices of the iFogSim are related with a list of children identificators and just one father, as it can be seen in lines 59 and 68 of class https://github.com/Cloudslab/iFogSim/blob/master/src/org/fog/entities/FogDevice.java.
Obtained from the analysis of the source code available in https://github.com/harshitgupta1337/fogsim.
References
Arkian HR, Diyanat A, Pourkhalili A (2017) Mist: Fog-based data analytics scheme with cost-efficient resource provisioning for iot crowdsensing applications. J Netw Comput Appl 82(Supplement C):152–165. https://doi.org/10.1016/j.jnca.2017.01.012. http://www.sciencedirect.com/science/article/pii/S1084804517300188
Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54(15):2787–2805
Balalaie A, Heydarnoori A, Jamshidi P (2016) Microservices architecture enables devops: migration to a cloud-native architecture. IEEE Softw 33(3):42–52. https://doi.org/10.1109/MS.2016.64
Barcelo M, Correa A, Llorca J, Tulino AM, Vicario JL, Morell A (2016) Iot-cloud service optimization in next generation smart environments. IEEE J Sel Areas Commun 34(12):4077–4090. https://doi.org/10.1109/JSAC.2016.2621398
Billet B, Issarny V (2014) From task graphs to concrete actions: a new task mapping algorithm for the future internet of things. In: 2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems, pp 470–478. https://doi.org/10.1109/MASS.2014.20
Bittencourt LF, Diaz-Montes J, Buyya R, Rana OF, Parashar M (2017) Mobility-aware application scheduling in fog computing. IEEE Cloud Comput. 4(2):26–35. https://doi.org/10.1109/MCC.2017.27
Borst S, Gupta V, Walid A (2010) Distributed caching algorithms for content distribution networks. In: 2010 Proceedings IEEE INFOCOM, pp 1–9. https://doi.org/10.1109/INFCOM.2010.5461964
Botta A, de Donato W, Persico V, Pescape A (2016) Integration of cloud computing and internet of things: a survey. Future Gener Comput Syst 56((Supplement C)):684–700
Brogi A, Forti S (2017) Qos-aware deployment of iot applications through the fog. IEEE Internet Things J 4(5):1185–1192. https://doi.org/10.1109/JIOT.2017.2701408
Brogi A, Forti S, Ibrahim A (2017) How to best deploy your fog applications, probably. In: 2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC), pp 105–114. https://doi.org/10.1109/ICFEC.2017.8
Cavalcante E, Pereira J, Alves MP, Maia P, Moura R, Batista T, Delicato FC, Pires PF (2016) On the interplay of internet of things and cloud computing: a systematic mapping study. Comput Commun 89-90(Supplement C):17–33. https://doi.org/10.1016/j.comcom.2016.03.012. http://www.sciencedirect.com/science/article/pii/S0140366416300706 (internet of Things: Research challenges and Solutions)
Chiang M, Zhang T (2016) Fog and iot: an overview of research opportunities. IEEE Internet Things J 3(6):854–864. https://doi.org/10.1109/JIOT.2016.2584538
Colistra G, Pilloni V, Atzori L (2014) The problem of task allocation in the internet of things and the consensus-based approach. Comput Netw 73(Supplement C):98–111. https://doi.org/10.1016/j.comnet.2014.07.011. http://www.sciencedirect.com/science/article/pii/S1389128614002655
Darwish A, Hassanien AE (2017) Cyber physical systems design, methodology, and integration: the current status and future outlook. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-017-0575-4
Darwish A, Hassanien AE, Elhoseny M, Sangaiah AK, Muhammad K (2017) The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-017-0659-1
Dastjerdi AV, Buyya R (2016) Fog computing: helping the internet of things realize its potential. Computer 49(8):112–116. https://doi.org/10.1109/MC.2016.245
Deng R, Lu R, Lai C, Luan TH (2015) Towards power consumption-delay tradeoff by workload allocation in cloud-fog computing. In: 2015 IEEE International Conference on Communications (ICC), pp 3909–3914. https://doi.org/10.1109/ICC.2015.7248934
Diaz M, Martin C, Rubio B (2016) State-of-the-art, challenges, and open issues in the integration of internet of things and cloud computing. J Netw Comput Appl 67(Supplement C):99 – 117. https://doi.org/10.1016/j.jnca.2016.01.010. http://www.sciencedirect.com/science/article/pii/S108480451600028X
Do CT, Tran NH, Pham C, Alam MGR, Son JH, Hong CS (2015) A proximal algorithm for joint resource allocation and minimizing carbon footprint in geo-distributed fog computing. In: 2015 International Conference on Information Networking (ICOIN), pp 324–329. https://doi.org/10.1109/ICOIN.2015.7057905
Farris I, Militano L, Nitti M, Atzori L, Iera A (2015) Federated edge-assisted mobile clouds for service provisioning in heterogeneous iot environments. In: 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), pp 591–596. https://doi.org/10.1109/WF-IoT.2015.7389120
Gu L, Zeng D, Guo S, Barnawi A, Xiang Y (2017) Cost efficient resource management in fog computing supported medical cyber-physical system. IEEE Trans Emerg Topics Comput 5(1):108–119. https://doi.org/10.1109/TETC.2015.2508382
Guerrero C, Lera I, Juiz C (2013) Performance improvement of web caching in web 2.0 via knowledge discovery. J Syst Softw 86(12):2970–2980. https://doi.org/10.1016/j.jss.2013.04.060. http://www.sciencedirect.com/science/article/pii/S0164121213001209
Guerrero C, Lera I, Juiz C (2017) Genetic algorithm for multi-objective optimization of container allocation in cloud architecture. J Grid Comput. https://doi.org/10.1007/s10723-017-9419-x.
Gupta H, Vahid Dastjerdi A, Ghosh SK, Buyya R (2017) ifogsim: a toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Softw Pract Exper 47(9):1275–1296. https://doi.org/10.1002/spe.2509.
Huang Z, Lin KJ, Yu SY, Hsu JY (2014a) Co-locating services in iot systems to minimize the communication energy cost. J Innov Digit Ecosyst 1(1):47–57. https://doi.org/10.1016/j.jides.2015.02.005. http://www.sciencedirect.com/science/article/pii/S2352664515000061
Huang Z, Lin KJ, Yu SY, Hsu JY (2014b) Building energy efficient internet of things by co-locating services to minimize communication. In: Proceedings of the 6th International Conference on Management of Emergent Digital EcoSystems, MEDES ’14, vol 18. ACM, New York, pp 101–108. https://doi.org/10.1145/2668260.2668270
Ko IY, Ko HG, Molina AJ, Kwon JH (2016) Soiot: Toward a user-centric iot-based service framework. ACM Trans Internet Technol 16(2):8. https://doi.org/10.1145/2835492
Krylovskiy A, Jahn M, Patti E (2015) Designing a smart city internet of things platform with microservice architecture. In: 2015 3rd International Conference on Future Internet of Things and Cloud, pp 25–30. https://doi.org/10.1109/FiCloud.2015.55
Li S, Xu LD, Zhao S (2015) The internet of things: a survey. Inf Syst Front 17(2):243–259
Mahmud R, Kotagiri R, Buyya R (2018) Fog computing: a taxonomy, survey and future directions. Springer, Singapore, pp 103–130
Munro I (1971) Efficient determination of the transitive closure of a directed graph. Inf Process Lett 1(2):56–58. https://doi.org/10.1016/0020-0190(71)90006-8. http://www.sciencedirect.com/science/article/pii/0020019071900068
Ni L, Zhang J, Jiang C, Yan C, Yu K (2017) Resource allocation strategy in fog computing based on priced timed petri nets. IEEE Internet Things J 4(5):1216–1228. https://doi.org/10.1109/JIOT.2017.2709814
Saurez E, Hong K, Lillethun D, Ramachandran U, Ottenwälder B (2016) Incremental deployment and migration of geo-distributed situation awareness applications in the fog. In: Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems, DEBS ’16. ACM, New York, pp 258–269. https://doi.org/10.1145/2933267.2933317. http://doi.acm.org/10.1145/2933267.2933317
Skarlat O, Nardelli M, Schulte S, Borkowski M, Leitner P (2017a) Optimized IoT service placement in the fog. Serv Oriented Comput Appl. https://doi.org/10.1007/s11761-017-0219-8
Skarlat O, Nardelli M, Schulte S, Dustdar S (2017b) Towards qos-aware fog service placement. In: 2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC), pp 89–96. https://doi.org/10.1109/ICFEC.2017.12
Souza VBC, Ramrez W, Masip-Bruin X, Marn-Tordera E, Ren G, Tashakor G (2016) Handling service allocation in combined fog-cloud scenarios. In: 2016 IEEE International Conference on Communications (ICC), pp 1–5. https://doi.org/10.1109/ICC.2016.7511465
Taneja M, Davy A (2017) Resource aware placement of IoT application modules in fog-cloud computing paradigm. In: 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), pp 1222–1228. https://doi.org/10.23919/INM.2017.7987464
Urgaonkar R, Wang S, He T, Zafer M, Chan K, Leung KK (2015a) Dynamic service migration and workload scheduling in edge-clouds. Perform Eval 91(Supplement C):205–228. https://doi.org/10.1016/j.peva.2015.06.013. http://www.sciencedirect.com/science/article/pii/S0166531615000619 (special Issue: Performance 2015)
Urgaonkar R, Wang S, He T, Zafer M, Chan K, Leung KK (2015b) Dynamic service migration and workload scheduling in edge-clouds. Perform Eval 91(C):205–228. https://doi.org/10.1016/j.peva.2015.06.013
Vakali A, Pallis G (2003) Content delivery networks: status and trends. IEEE Int Comput 7(6):68–74. https://doi.org/10.1109/MIC.2003.1250586
Varghese B, Buyya R (2017) Next generation cloud computing: new trends and research directions. Future Gener Comput Syst. https://doi.org/10.1016/j.future.2017.09.020. http://www.sciencedirect.com/science/article/pii/S0167739X17302224
Velasquez K, Abreu DP, Curado M, Monteiro E (2017) Service placement for latency reduction in the internet of things. Ann Telecommun 72(1):105–115. https://doi.org/10.1007/s12243-016-0524-9
Venticinque S, Amato A (2018) A methodology for deployment of iot application in fog. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-018-0785-4
Vogler M, Schleicher JM, Inzinger C, Dustdar S (2016) A scalable framework for provisioning large-scale iot deployments. ACM Trans Internet Technol 16(2):11. https://doi.org/10.1145/2850416. http://doi.acm.org/10.1145/2850416
Wang S, Urgaonkar R, Chan K, He T, Zafer M, Leung KK (2015) Dynamic service placement for mobile micro-clouds with predicted future costs. In: 2015 IEEE International Conference on Communications (ICC), pp 5504–5510. https://doi.org/10.1109/ICC.2015.7249199
Wang S, Zafer M, Leung KK (2017) Online placement of multi-component applications in edge computing environments. IEEE Access 5:2514–2533. https://doi.org/10.1109/ACCESS.2017.2665971
Weaveworks, ContainerSolutions (2016) Socks shop—a microservices demo application. https://microservices-demo.github.io/
Wen Z, Yang R, Garraghan P, Lin T, Xu J, Rovatsos M (2017) Fog orchestration for internet of things services. IEEE Internet Comput 21(2):16–24. https://doi.org/10.1109/MIC.2017.36
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):1440–1452. https://doi.org/10.1109/TC.2015.2435781
Zeng D, Gu L, Guo S, Cheng Z, Yu S (2016) Joint optimization of task scheduling and image placement in fog computing supported software-defined embedded system. IEEE Trans Comput 65(12):3702–3712. https://doi.org/10.1109/TC.2016.2536019
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This research was supported by the Spanish Government (Agencia Estatal de Investigación) and the European Commission (Fondo Europeo de Desarrollo Regional) through Grant Number TIN2017-88547-P (MINECO/AEI/FEDER, UE).
Rights and permissions
About this article
Cite this article
Guerrero, C., Lera, I. & Juiz, C. A lightweight decentralized service placement policy for performance optimization in fog computing. J Ambient Intell Human Comput 10, 2435–2452 (2019). https://doi.org/10.1007/s12652-018-0914-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-018-0914-0