Optimization policy for file replica placement in fog domains

Fog computing architectures distribute computational and storage resources along the continuum from the cloud to things. Therefore, the execution of services or the storage of files can be closer to the users. The main objectives of fog computing domains are to reduce the user latency and the network usage. Availability is also an issue in fog architectures because the topology of the network does not guarantee redundant links between devices. Consequently, the definition of placement polices is a key challenge. We propose a placement policy for data replication to increase data availability that contrasts with other storage policies that only consider a single replica of the files. The system is modeled with complex weighted networks and topological features, such as centrality indices. Graph partition algorithms are evaluated to select the fog devices that store data replicas. Our approach is compared with two other placement policies: one that stores only one replica and FogStore, which also stores file replicas but uses a greedy approach (the shortest path). We analyze 22 experiments with simulations. The results show that our approach obtains the shortest latency times, mainly for writing operations, a smaller network usage increase, and a similar file availability to FogStore.

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

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

[3]  R. Manimegalai,et al.  Dynamic replica placement and selection strategies in data grids - A comprehensive survey , 2014, J. Parallel Distributed Comput..

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

[5]  Mohsen Guizani,et al.  Emerging Trends, Issues, and Challenges in Big Data and Its Implementation toward Future Smart Cities , 2017, IEEE Commun. Mag..

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

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

[8]  HuangXinyi,et al.  An overview of Fog computing and its security issues , 2016 .

[9]  Ivan Stojmenovic,et al.  An overview of Fog computing and its security issues , 2016, Concurr. Comput. Pract. Exp..

[10]  Michel Riveill,et al.  An Architecture to Support the Collection of Big Data in the Internet of Things , 2014, 2014 IEEE World Congress on Services.

[11]  Jafari NavimipourNima,et al.  A comprehensive review of the data replication techniques in the cloud environments , 2016 .

[12]  Laurence T. Yang,et al.  Emerging Trends, Issues, and Challenges in Big Data and Its Implementation toward Future Smart Cities , 2018, IEEE Commun. Mag..

[13]  Sonja Filiposka,et al.  Community-based VM placement framework , 2015, The Journal of Supercomputing.

[14]  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).

[15]  Tarek Hamrouni,et al.  A survey of dynamic replication and replica selection strategies based on data mining techniques in data grids , 2016, Eng. Appl. Artif. Intell..

[16]  M. Newman Analysis of weighted networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

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

[18]  Shantanu Dutt New faster Kernighan-Lin-type graph-partitioning algorithms , 1993, ICCAD.

[19]  Nima Jafari Navimipour,et al.  A comprehensive review of the data replication techniques in the cloud environments: Major trends and future directions , 2016, J. Netw. Comput. Appl..

[20]  Abdella Battou,et al.  Formal definition of edge computing: An emphasis on mobile cloud and IoT composition , 2018, 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC).

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

[22]  Jiang Zhu,et al.  Fog Computing: A Platform for Internet of Things and Analytics , 2014, Big Data and Internet of Things.

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

[24]  Carlos Juiz,et al.  Multi-Objective Optimization for Virtual Machine Allocation and Replica Placement in Virtualized Hadoop , 2018, IEEE Transactions on Parallel and Distributed Systems.

[25]  Ruben Mayer,et al.  FogStore: Toward a distributed data store for Fog computing , 2017, 2017 IEEE Fog World Congress (FWC).

[26]  Ciprian Dobre,et al.  Big Data and Internet of Things: A Roadmap for Smart Environments , 2014, Big Data and Internet of Things.

[27]  Roger Wattenhofer,et al.  Competitive Hill-Climbing Strategies for Replica Placement in a Distributed File System , 2001, DISC.

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

[29]  Nirwan Ansari,et al.  Cost Aware cloudlet Placement for big data processing at the edge , 2017, 2017 IEEE International Conference on Communications (ICC).

[30]  Ronny Hans,et al.  Cost-optimized redundant data storage in the cloud , 2017, Service Oriented Computing and Applications.

[31]  Yaser Jararweh,et al.  Recent advances in fog and mobile edge computing , 2018, Trans. Emerg. Telecommun. Technol..

[32]  Qing Yang,et al.  Fog Data: Enhancing Telehealth Big Data Through Fog Computing , 2015, ASE BD&SI.

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

[34]  Henk J. Scholten,et al.  Spatial Dimensions of Big Data: Application of Geographical Concepts and Spatial Technology to the Internet of Things , 2014, Big Data and Internet of Things.

[35]  Carlos Juiz,et al.  Comparing centrality indices for network usage optimization of data placement policies in fog devices , 2018, 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC).

[36]  Kin K. Leung,et al.  Dynamic Service Placement for Mobile Micro-Clouds with Predicted Future Costs , 2015, IEEE Transactions on Parallel and Distributed Systems.

[37]  U. Brandes A faster algorithm for betweenness centrality , 2001 .

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

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

[40]  Ioannis D. Moscholios,et al.  Towards Distributed Data Management in Fog Computing , 2018, Wirel. Commun. Mob. Comput..

[41]  Xuejie Zhang,et al.  A load balancing mechanism based on ant colony and complex network theory in open cloud computing federation , 2010, 2010 The 2nd International Conference on Industrial Mechatronics and Automation.

[42]  Laurence T. Yang,et al.  Distributed Multi-Representative Re-Fusion Approach for Heterogeneous Sensing Data Collection , 2017, ACM Trans. Embed. Comput. Syst..

[43]  Jemal H. Abawajy,et al.  Placement of File Replicas in Data Grid Environments , 2004, International Conference on Computational Science.

[44]  Brian W. Kernighan,et al.  An efficient heuristic procedure for partitioning graphs , 1970, Bell Syst. Tech. J..

[45]  Zhangjie Fu,et al.  Heterogeneous cloudlet deployment and user‐cloudlet association toward cost effective fog computing , 2017, Concurr. Comput. Pract. Exp..

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

[47]  Sonja Filiposka,et al.  Community-based allocation and migration strategies for fog computing , 2018, 2018 IEEE Wireless Communications and Networking Conference (WCNC).

[48]  Bin Cheng,et al.  Building a Big Data Platform for Smart Cities: Experience and Lessons from Santander , 2015, 2015 IEEE International Congress on Big Data.

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

[50]  Carlos Juiz,et al.  On the Influence of Fog Colonies Partitioning in Fog Application Makespan , 2018, 2018 IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud).

[51]  Roger Wattenhofer,et al.  Optimizing file availability in a secure serverless distributed file system , 2001, Proceedings 20th IEEE Symposium on Reliable Distributed Systems.

[52]  Umakishore Ramachandran,et al.  FogStore: A Geo-Distributed Key-Value Store Guaranteeing Low Latency for Strongly Consistent Access , 2018, DEBS.

[53]  Sherali Zeadally,et al.  Performance analysis of data intensive cloud systems based on data management and replication: a survey , 2016, Distributed and Parallel Databases.

[54]  Rahim Tafazolli,et al.  In-network caching of Internet-of-Things data , 2014, 2014 IEEE International Conference on Communications (ICC).

[55]  Elhadj Benkhelifa,et al.  A systematic literature review of data governance and cloud data governance , 2018, Personal and Ubiquitous Computing.

[56]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[57]  Stefan Richter,et al.  Centrality Indices , 2004, Network Analysis.