Evaluation and efficiency comparison of evolutionary algorithms for service placement optimization in fog architectures

Abstract This study compares three evolutionary algorithms for the problem of fog service placement: weighted sum genetic algorithm (WSGA), non-dominated sorting genetic algorithm II (NSGA-II), and multiobjective evolutionary algorithm based on decomposition (MOEA/D). A model for the problem domain (fog architecture and fog applications) and for the optimization (objective functions and solutions) is presented. Our main concerns are related to optimize the network latency, the service spread and the use of the resources. The algorithms are evaluated with a random Barabasi–Albert network topology with 100 devices and with two experiment sizes of 100 and 200 application services. The results showed that NSGA-II obtained the highest optimizations of the objectives and the highest diversity of the solution space. On the contrary, MOEA/D was better to reduce the execution times. The WSGA algorithm did not show any benefit with regard to the other two algorithms.

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

[2]  Carlos Juiz,et al.  Resource optimization of container orchestration: a case study in multi-cloud microservices-based applications , 2018, The Journal of Supercomputing.

[3]  Antonio Brogi,et al.  How to Best Deploy Your Fog Applications, Probably , 2017, 2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC).

[4]  Carlos Juiz,et al.  Migration-Aware Genetic Optimization for MapReduce Scheduling and Replica Placement in Hadoop , 2018, Journal of Grid Computing.

[5]  Zdenek Becvar,et al.  Mobile Edge Computing: A Survey on Architecture and Computation Offloading , 2017, IEEE Communications Surveys & Tutorials.

[6]  Igor Cavrak,et al.  Architecture of an interoperable IoT platform based on microservices , 2016, 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).

[7]  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..

[8]  Radu Prodan,et al.  Multi-objective Middleware for Distributed VMI Repositories in Federated Cloud Environment , 2016, Scalable Comput. Pract. Exp..

[9]  Marco Jahn,et al.  Designing a Smart City Internet of Things Platform with Microservice Architecture , 2015, 2015 3rd International Conference on Future Internet of Things and Cloud.

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

[11]  Rongxing Lu,et al.  Towards power consumption-delay tradeoff by workload allocation in cloud-fog computing , 2015, 2015 IEEE International Conference on Communications (ICC).

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

[13]  Antonio Iera,et al.  Federated edge-assisted mobile clouds for service provisioning in heterogeneous IoT environments , 2015, 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT).

[14]  Long Sun,et al.  An open IoT framework based on microservices architecture , 2017, China Communications.

[15]  Valérie Issarny,et al.  From Task Graphs to Concrete Actions: A New Task Mapping Algorithm for the Future Internet of Things , 2014, 2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems.

[16]  Roch H. Glitho,et al.  A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges , 2017, IEEE Communications Surveys & Tutorials.

[17]  Arnaud Legrand,et al.  Fog Based Framework for IoT Service Provisioning , 2019, 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC).

[18]  Shiqiang Wang,et al.  Dynamic service placement for mobile micro-clouds with predicted future costs , 2015, ICC.

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

[20]  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..

[21]  Rajkumar Buyya,et al.  Mobility-Aware Application Scheduling in Fog Computing , 2017, IEEE Cloud Computing.

[22]  Qingfu Zhang,et al.  Comparison between MOEA/D and NSGA-II on the Multi-Objective Travelling Salesman Problem , 2009 .

[23]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[24]  Weisong Shi,et al.  Edge Computing: Vision and Challenges , 2016, IEEE Internet of Things Journal.

[25]  Hisao Ishibuchi,et al.  Evolutionary many-objective optimization by NSGA-II and MOEA/D with large populations , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[26]  Stefan Schulte,et al.  A Framework for Optimization, Service Placement, and Runtime Operation in the Fog , 2018, 2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC).

[27]  Lalit M. Patnaik,et al.  Adaptive probabilities of crossover and mutation in genetic algorithms , 1994, IEEE Trans. Syst. Man Cybern..

[28]  Marco Laumanns,et al.  Performance assessment of multiobjective optimizers: an analysis and review , 2003, IEEE Trans. Evol. Comput..

[29]  Schahram Dustdar,et al.  A Scalable Framework for Provisioning Large-Scale IoT Deployments , 2016, ACM Trans. Internet Techn..

[30]  Ruben Mayer,et al.  EmuFog: Extensible and scalable emulation of large-scale fog computing infrastructures , 2017, 2017 IEEE Fog World Congress (FWC).

[31]  Chungang Yan,et al.  Resource Allocation Strategy in Fog Computing Based on Priced Timed Petri Nets , 2017, IEEE Internet of Things Journal.

[32]  Shapour Azarm,et al.  Metrics for Quality Assessment of a Multiobjective Design Optimization Solution Set , 2001 .

[33]  Nour Ali,et al.  A Systematic Mapping Study in Microservice Architecture , 2016, 2016 IEEE 9th International Conference on Service-Oriented Computing and Applications (SOCA).

[34]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[35]  Bharat K. Bhargava,et al.  A Survey of Computation Offloading for Mobile Systems , 2012, Mobile Networks and Applications.

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

[37]  Carlos Juiz,et al.  Genetic Algorithm for Multi-Objective Optimization of Container Allocation in Cloud Architecture , 2017, Journal of Grid Computing.

[38]  Victor C. M. Leung,et al.  Developing IoT applications in the Fog: A Distributed Dataflow approach , 2015, 2015 5th International Conference on the Internet of Things (IOT).

[39]  Jun Zhang,et al.  Cloud Computing Resource Scheduling and a Survey of Its Evolutionary Approaches , 2015, ACM Comput. Surv..

[40]  Amol C. Adamuthe,et al.  Multiobjective Virtual Machine Placement in Cloud Environment , 2013, 2013 International Conference on Cloud & Ubiquitous Computing & Emerging Technologies.

[41]  Kin K. Leung,et al.  Dynamic service migration and workload scheduling in edge-clouds , 2015, Perform. Evaluation.

[42]  Schahram Dustdar,et al.  Efficient and Scalable IoT Service Delivery on Cloud , 2013, 2013 IEEE Sixth International Conference on Cloud Computing.

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

[44]  Jane Yung-jen Hsu,et al.  Co-locating services in IoT systems to minimize the communication energy cost , 2014, J. Innov. Digit. Ecosyst..

[45]  Rajkumar Buyya,et al.  Fog Computing: A Taxonomy, Survey and Future Directions , 2016, Internet of Everything.

[46]  Carlos Juiz,et al.  Availability-Aware Service Placement Policy in Fog Computing Based on Graph Partitions , 2019, IEEE Internet of Things Journal.

[47]  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).

[48]  John K. Zao,et al.  Augmented Brain Computer Interaction Based on Fog Computing and Linked Data , 2014, 2014 International Conference on Intelligent Environments.

[49]  Kalyanmoy Deb,et al.  A Comparative Analysis of Selection Schemes Used in Genetic Algorithms , 1990, FOGA.

[50]  Pascal Bouvry,et al.  A Survey of Evolutionary Computation for Resource Management of Processing in Cloud Computing [Review Article] , 2015, IEEE Computational Intelligence Magazine.

[51]  Wilhelm Hasselbring,et al.  Search-based genetic optimization for deployment and reconfiguration of software in the cloud , 2013, 2013 35th International Conference on Software Engineering (ICSE).

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

[53]  Pooyan Jamshidi,et al.  Microservices Architecture Enables DevOps: Migration to a Cloud-Native Architecture , 2016, IEEE Software.

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

[55]  Yong Xiang,et al.  Cost Efficient Resource Management in Fog Computing Supported Medical Cyber-Physical System , 2017, IEEE Transactions on Emerging Topics in Computing.

[56]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[57]  Antonio Iera,et al.  Evaluating Performance of Containerized IoT Services for Clustered Devices at the Network Edge , 2017, IEEE Internet of Things Journal.

[58]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[59]  Chen-Khong Tham,et al.  Latency aware mobile task assignment and load balancing for edge cloudlets , 2017, 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops).

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

[61]  Marília Curado,et al.  Fog orchestration for the Internet of Everything: state-of-the-art and research challenges , 2018, J. Internet Serv. Appl..

[62]  Qingfu Zhang,et al.  Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II , 2009, IEEE Transactions on Evolutionary Computation.

[63]  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.

[64]  Luigi Atzori,et al.  The problem of task allocation in the Internet of Things and the consensus-based approach , 2014, Comput. Networks.

[65]  Mitsuo Gen,et al.  Genetic algorithms and engineering optimization , 1999 .

[66]  David Lillethun,et al.  Mobile fog: a programming model for large-scale applications on the internet of things , 2013, MCC '13.