The Impact of Human Mobility on Edge Data Center Deployment in Urban Environments

Multi-access Edge Computing (MEC) brings storage and computational capabilities at the edge of the network into so-called Edge Data Centers (EDCs) to better low-latency applications. To this end, effective placement of EDCs in urban environments is key for proper load balance and to minimize outages. In this paper, we specifically tackle this problem. To fully understand how the computational demand of EDCs varies, it is fundamental to analyze the complex dynamics of cities. Our work takes into account the mobility of citizens and their spatial patterns to estimate the optimal placement of MEC EDCs in urban environments in order to minimize outages. To this end, we propose and compare two heuristics. In particular, we present the mobility-aware deployment algorithm (MDA) that outperforms approaches that do not consider citizens mobility. Simulations are conducted in Luxembourg City by extending the CrowdSenSim simulator and show that efficient EDCs placement significantly reduces outages.

[1]  Max Mühlhäuser,et al.  A Multi-Cloudlet Infrastructure for Future Smart Cities: An Empirical Study , 2018, EdgeSys@MobiSys.

[2]  Paolo Giaccone,et al.  High-Precision Design of Pedestrian Mobility for Smart City Simulators , 2018, 2018 IEEE International Conference on Communications (ICC).

[3]  Dario Sabella,et al.  Flexible MEC service consumption through edge host zoning in 5G networks , 2019, 2019 IEEE Wireless Communications and Networking Conference Workshop (WCNCW).

[4]  Leonard Kleinrock,et al.  Queueing Systems: Volume I-Theory , 1975 .

[5]  Mahadev Satyanarayanan,et al.  The Emergence of Edge Computing , 2017, Computer.

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

[7]  Aleksandra Checko,et al.  A Survey of the Functional Splits Proposed for 5G Mobile Crosshaul Networks , 2019, IEEE Communications Surveys & Tutorials.

[8]  Peng Liu,et al.  ParaDrop: Enabling Lightweight Multi-tenancy at the Network’s Extreme Edge , 2016, 2016 IEEE/ACM Symposium on Edge Computing (SEC).

[9]  Tarik Taleb,et al.  On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration , 2017, IEEE Communications Surveys & Tutorials.

[10]  Heiko Ludwig,et al.  Zenith: Utility-Aware Resource Allocation for Edge Computing , 2017, 2017 IEEE International Conference on Edge Computing (EDGE).

[11]  Dmitrii Chemodanov,et al.  Data-Driven Edge Computing Resource Scheduling for Protest Crowds Incident Management , 2018, 2018 IEEE 17th International Symposium on Network Computing and Applications (NCA).

[12]  Antonio Corradi,et al.  The participact mobile crowd sensing living lab: The testbed for smart cities , 2014, IEEE Communications Magazine.

[13]  Stefano Giordano,et al.  CrowdSenSim: a Simulation Platform for Mobile Crowdsensing in Realistic Urban Environments , 2017, IEEE Access.

[14]  Marco Fiore,et al.  Not All Apps Are Created Equal: Analysis of Spatiotemporal Heterogeneity in Nationwide Mobile Service Usage , 2017, CoNEXT.

[15]  Yuren Zhou,et al.  Understanding Urban Human Mobility through Crowdsensed Data , 2018, IEEE Communications Magazine.

[16]  Jörg Widmer,et al.  OpenLEON: An End-to-End Emulator from the Edge Data Center to the Mobile Users , 2018, WiNTECH@MOBICOM.

[17]  Weifa Liang,et al.  Optimal Cloudlet Placement and User to Cloudlet Allocation in Wireless Metropolitan Area Networks , 2017, IEEE Transactions on Cloud Computing.

[18]  Dzmitry Kliazovich,et al.  A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities , 2019, IEEE Communications Surveys & Tutorials.

[19]  Stefano Chessa,et al.  Human-Enabled Edge Computing: Exploiting the Crowd as a Dynamic Extension of Mobile Edge Computing , 2018, IEEE Communications Magazine.

[20]  Cheng Li,et al.  Delay Outage Probability of Multi-relay Selection for Mobile Relay Edge Computing System , 2019, 2019 IEEE/CIC International Conference on Communications in China (ICCC).

[21]  Archan Misra,et al.  Understanding the Interdependency of Land Use and Mobility for Urban Planning , 2018, UbiComp/ISWC Adjunct.

[22]  Jörg Ott,et al.  Consolidate IoT Edge Computing with Lightweight Virtualization , 2018, IEEE Network.

[23]  Paolo Giaccone,et al.  Profiling Performance of Application Partitioning for Wearable Devices in Mobile Cloud and Fog Computing , 2019, IEEE Access.