A Survey of Optimization Algorithms for Fog Computing Service Placement

Fog computing provides Quality Of Service to latency sensitive applications in bandwidth constrained WAN networks. Fog environment consist of a set of IoT devices, fog nodes, and cloud node that gathers the sensed data, application requests from the user and decided to place the application modules in the suitable node. Fog framework uses the optimization algorithms to distribute the IoT application modules, based on functional requirements. This survey explores various optimization techniques to the application module placement, which includes Exact methods, Heuristic methods, Hybrid methods, and Hyper heuristic techniques on fog networks. The proposed work analyses and examine different optimization criteria, and outline the research challenges in the service placement.

[1]  Hamid Reza Arkian,et al.  MIST: Fog-based data analytics scheme with cost-efficient resource provisioning for IoT crowdsensing applications , 2017, J. Netw. Comput. Appl..

[2]  Kin K. Leung,et al.  Online Placement of Multi-Component Applications in Edge Computing Environments , 2016, IEEE Access.

[3]  Haibo He,et al.  A Hierarchical Distributed Fog Computing Architecture for Big Data Analysis in Smart Cities , 2015, ASE BD&SI.

[4]  Enrique Saurez,et al.  Incremental deployment and migration of geo-distributed situation awareness applications in the fog , 2016, DEBS.

[5]  Marília Curado,et al.  Service placement for latency reduction in the internet of things , 2016, Annals of Telecommunications.

[6]  Vineet Kansal,et al.  Comparison of Heuristic techniques:A case of TSP , 2020, 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence).

[7]  Rajkumar Buyya,et al.  Fog Computing: Helping the Internet of Things Realize Its Potential , 2016, Computer.

[8]  Song Guo,et al.  Joint Optimization of Task Scheduling and Image Placement in Fog Computing Supported Software-Defined Embedded System , 2016, IEEE Transactions on Computers.

[9]  Himdweep Walia,et al.  A Decision Tree Based Supervised Program Interpretation Technique for Gurmukhi Language , 2019 .

[10]  Rajkumar Buyya,et al.  Modelling and Simulation of Fog and Edge Computing Environments using iFogSim Toolkit , 2018, ArXiv.

[11]  Jaime Llorca,et al.  IoT-Cloud Service Optimization in Next Generation Smart Environments , 2016, IEEE Journal on Selected Areas in Communications.

[12]  Carlos Juiz,et al.  A lightweight decentralized service placement policy for performance optimization in fog computing , 2018, Journal of Ambient Intelligence and Humanized Computing.

[13]  Antonio Brogi,et al.  QoS-Aware Deployment of IoT Applications Through the Fog , 2017, IEEE Internet of Things Journal.

[14]  Raja Lavanya,et al.  Fog Computing and Its Role in the Internet of Things , 2019, Advances in Computer and Electrical Engineering.

[15]  Xavier Masip-Bruin,et al.  Handling service allocation in combined Fog-cloud scenarios , 2016, 2016 IEEE International Conference on Communications (ICC).

[16]  Philipp Leitner,et al.  Resource Provisioning for IoT Services in the Fog , 2016, 2016 IEEE 9th International Conference on Service-Oriented Computing and Applications (SOCA).

[17]  Vineet Kansal,et al.  Optimization in Wireless Sensor Network Using Soft Computing , 2020 .

[18]  Amit Agarwal,et al.  Secured Sharing of Data in Cloud via Dual Authentication, Dynamic Unidirectional PRE, and CPABE , 2020, Int. J. Inf. Secur. Priv..

[19]  Schahram Dustdar,et al.  Towards QoS-Aware Fog Service Placement , 2017, 2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC).

[20]  Luis Rodero-Merino,et al.  Finding your Way in the Fog: Towards a Comprehensive Definition of Fog Computing , 2014, CCRV.

[21]  Alan Davy,et al.  Resource aware placement of IoT application modules in Fog-Cloud Computing Paradigm , 2017, 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM).

[22]  Zhenyu Wen,et al.  Fog Orchestration for Internet of Things Services , 2017, IEEE Internet Computing.

[23]  Philipp Leitner,et al.  Optimized IoT service placement in the fog , 2017, Service Oriented Computing and Applications.

[24]  Xu Han,et al.  Cost Aware Service Placement and Load Dispatching in Mobile Cloud Systems , 2016, IEEE Transactions on Computers.

[25]  Jie Wang,et al.  Distributed Analytics and Edge Intelligence: Pervasive Health Monitoring at the Era of Fog Computing , 2015, Mobidata@MobiHoc.

[26]  Vineet Kansal,et al.  Load Distribution Challenges with Virtual Computing , 2020 .

[27]  Rajkumar Buyya,et al.  iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments , 2016, Softw. Pract. Exp..

[28]  BuyyaRajkumar,et al.  Latency-Aware Application Module Management for Fog Computing Environments , 2018 .

[29]  Juan Felipe Botero,et al.  GRECO: A Distributed Genetic Algorithm for Reliable Application Placement in Hybrid Clouds , 2016, 2016 5th IEEE International Conference on Cloud Networking (Cloudnet).

[30]  Vineet Kansal,et al.  A benchmarking framework using nonlinear manifold detection techniques for software defect prediction , 2020, Int. J. Comput. Sci. Eng..

[31]  Kurt Rothermel,et al.  MigCEP: operator migration for mobility driven distributed complex event processing , 2013, DEBS.

[32]  Vineet Kansal,et al.  Evaluating the Impact of Sampling-Based Nonlinear Manifold Detection Model on Software Defect Prediction Problem , 2020 .