iFogStor: an IoT Data Placement Strategy for Fog Infrastructure

被引:92
作者
Naas, Mohammed Islam [1 ]
Parvedy, Philippe Raipin [1 ]
Boukhobza, Jalil [2 ]
Lemarchand, Laurent [2 ]
机构
[1] Orange, Rennes, France
[2] Univ Bretagne Occidentale, Lab STICC, UMR 6285, F-29200 Brest, France
来源
2017 IEEE 1ST INTERNATIONAL CONFERENCE ON FOG AND EDGE COMPUTING (ICFEC) | 2017年
关键词
Internet of Things; Fog; Data Placement; Storage; Optimization; Generalized Assignment Problem;
D O I
10.1109/ICFEC.2017.15
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Internet of Things (IoT) will be one of the driving application for digital data generation in the next years as more than 50 billions of objects will be connected by 2020. IoT data can be processed and used by different devices spread all over the network. The traditional way of centralizing data processing in the Cloud can hardly scale because it cannot satisfy many of the latency critical IoT applications. In addition, it generates a too high network traffic when the number of objects and services increase. Fog infrastructure provides a beginning of an answer to such an issue. In this paper, we present a data placement strategy for Fog infrastructures called iFogStor. The objective of iFogStor is to take profit of the heterogeneity and location of Fog nodes to reduce the overall latency of storing and retrieving data in a Fog. We formulated the data placement problem as a Generalized Assignment Problem (GAP) and proposed two ways to solve it: 1) an exact solution using integer programming and 2) a heuristic one based on geographical zoning to reduce the solving time. Both solutions proved very good performance as they reduced the latency by more than 86% as compared to a Cloud based solution and by 60% as compared to a naive Fog solution. Using geographical zoning heuristic can allow solving problems with large number of Fog nodes efficiently and in a couple of seconds making iFogStor feasible in runtime and scalable.
引用
收藏
页码:97 / 104
页数:8
相关论文
共 18 条
  • [1] Aazam M, 2015, 2015 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATION WORKSHOPS (PERCOM WORKSHOPS), P518, DOI 10.1109/PERCOMW.2015.7134091
  • [2] [Anonymous], 2016, ARXIV
  • [3] [Anonymous], 2016, CLOUDSTR20162 U MELB
  • [4] Balachandar S. R., 2009, INT J MATH STAT SCI, V1
  • [5] Bonomi F, 2012, P 1 ED MCC WORKSH MO, P13, DOI [DOI 10.1145/2342509.2342513, 10.1145/2342509.2342513]
  • [6] An Architecture to Support the Collection of Big Data in the Internet of Things
    Cecchinel, Cyril
    Jimenez, Matthieu
    Mosser, Sebastien
    Riveill, Michel
    [J]. 2014 IEEE WORLD CONGRESS ON SERVICES (SERVICES), 2014, : 442 - 449
  • [7] Building a Big Data Platform for Smart Cities: Experience and Lessons from Santander
    Cheng, Bin
    Longo, Salvatore
    Cirillo, Flavio
    Bauer, Martin
    Kovacs, Ernoe
    [J]. 2015 IEEE INTERNATIONAL CONGRESS ON BIG DATA - BIGDATA CONGRESS 2015, 2015, : 592 - 599
  • [8] Meye P, 2014, 2014 INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING & SIMULATION (HPCS), P260, DOI 10.1109/HPCSim.2014.6903694
  • [9] Noronha A., 2014, Attaining IoT Value: How To Move from Connecting Things to Capturing Insights
  • [10] Saharan K.P., 2015, INT J COMPUTER APPL, V122