Abstract
Over the last couple of years, the Internet of Things (IoT) has been one of the popular technologies along with the emergence of 5G technologies that facilitate new interactions between things and humans to enhance the quality of life. With the rapid development of IoT applications, connected devices are generating extraordinary volume and unmatched variety of data that to be processed at the centralized cloud data center. The ever-increasing demand for computation resources in the centralized cloud data center system inevitably affects the Quality of Service (QoS). The concept of fog computing is based on moving the computational load into the edge of the network, which is a middle layer that has been introduced that consists of multiple heterogeneous fog devices to process the IoT application. Undoubtedly, the processing of data at the fog layer reduces the response time and bandwidth cost while fulfilling the Quality of Services (QoS). Due to the heterogeneity and dynamicity properties of IoT applications, the proper application placement is a key to enhance the overall system performance. To fully utilize the capabilities of distributed fog computing architecture, a large-scale (IoT) application can be decomposed into dependent and independent services and to deploy those services in an orderly way into the available virtualized fog node while satisfying the constraints and Service-Level Agreement (SLA) may increase the efficiency and performance of the proposed model. In this work, we study the application placement problem which is a well-known NP-complete problem in the fog computing environment. We investigate different deterministic and non-deterministic approach proposed by authors for optimal placement of services based on single and multiple objectives. We propose a genetic-algorithm-based meta-heuristic technique to solve multi-objective service placement and compared with random-based application placement. Evaluation results show that our proposal outperforms random-based placement policy.
Supported by National Institute of Technology, Rourkela, Odisha, India.
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References
Mohan, N., Kangasharju, J.: Edge-fog cloud: A distributed cloud for internet of things computations, 2016 Cloudification of the Internet of Things. CIoT 2016 (2017)
Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the Internet of Things. In: Proceedings 1st Ed. MCC Workshop Mobile Cloud Computing, pp. 13–16 (2012)
Deng, R., Lu, R., Lai, C., Luan, T.H., Liang, H.: Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. IEEE Internet Things J. 3, 1171–1181 (2016)
Bitam, S., Zeadally, S., Mellouk, A.: Fog computing job scheduling optimization based on bees swarm. Enterprise Information Systems, pp. 1–25 (2017)
Azizi, S., Khosroabadi, F., Shojafar, M.: A priority-based service placement policy for fog-cloud computing systems. Comput. Methods Differen. Equ. 7(4) (Special Issue), pp. 521–534 (2019)
Toor, A., ul Islam, S., Ahmed, G., Jabbar, S., Khalid, S., Sharif, A.M.: Energy efficient edge-of-things. EURASIP J. Wirel. Commun. Netw. 8 (2019)
Gupta, H., Vahid Dastjerdi, A., Ghosh, S.K., Buyya, R.: ifogsim: a toolkit for modeling and simulation of resource management techniques in the internet of things, edge and fog computing environments. Softw. Pract. Exp. 47(9), 1275–1296 (2017)
Yousefpour, A., Ishigaki, G., Jue, J.P., Fog computing: towards minimizing delay in the internet of things. In: IEEE international conference on edge computing (EDGE). IEEE 2017, pp. 17–24 (2017)
Taneja, M., Davy, A.: Resource aware placement of iot application modules in fog-cloud computing paradigm. In: IFIP/IEEE Symposium on Integrated Network and Service Management (IM). IEEE 2017, pp. 1222–1228 (2017)
Skarlat, O., Nardelli, M., Schulte, S., Borkowski, M., Leitner, P.: Optimized Iot service placement in the fog. SOCA 11(4), 427–443 (2017)
Mahmud, Redowan, Ramamohanarao, Kotagiri, Buyya, Rajkumar: Latency-aware application module management for fog computing environments. ACM Trans. Internet Technol. (TOIT) 19(1), 1–21 (2018)
Mahmud, R., Ramamohanarao, K., Buyya, R.: Edge affinity-based management of applications in fog computing environments. In: Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing, pp. 61–70. ACM (2019)
Mahmud, R., Srirama, S.N., Ramamohanarao, K., Buyya, R.: Quality of experience (qoe)-aware placement of applications in fog computing environments. J. Parallel Distrib. Comput. 132, 190–203 (2019)
Mahmud, R., et al.: Quality of experience (QoE)-aware placement of applications in fog computing environments. J. Parallel Distrib. Comput. 132, 190–203 (2019)
Taneja, M., Davy, A.: Resource aware placement of IoT application modules in fog-cloud computing paradigm. In: 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). IEEE (2017)
Minh, Q.T., et al.: Toward service placement on Fog computing landscape. In: 2017 4th NAFOSTED Conference on Information and Computer Science. IEEE (2017)
Abrol, P., Guupta, S., Singh, S.: Nature-Inspired Metaheuristics in Cloud: A Review, pp. 13–34. Singapore, ICT Systems and Sustainability. Springer (2020)
Mseddi, A., et al.: Joint container placement and task provisioning in dynamic fog computing. IEEE Internet Things J. 6(6), 10028–10040 (2019)
Mishra, S.K., et al.: Sustainable service allocation using a metaheuristic technique in a fog server for industrial applications. IEEE Trans. Ind. Inform. 14(10), 4497–4506 (2018)
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Apat, H.K., Bhaisare, K., Sahoo, B., Maiti, P. (2021). A Nature-Inspired-Based Multi-objective Service Placement in Fog Computing Environment. In: Udgata, S.K., Sethi, S., Srirama, S.N. (eds) Intelligent Systems. Lecture Notes in Networks and Systems, vol 185. Springer, Singapore. https://doi.org/10.1007/978-981-33-6081-5_26
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