MEC clusters great again!

Transcription

1 MEC clusters great again! Geo-partitioning of MEC resources Mathieu Bouet, Vania Conan Thales Communications & Security, France

2 ETSI MEC (Mobile Multi-access Edge Computing) Launched in Sept Key challenges: Convergence between IT and Telecom (virtualization) Elasticity and flexibility Deploying various services and caching content at the mobile network edge Reduced latency and core traffic Allowing software applications to tap into local content and real-time information about local-access network conditions Efficient resource management Source: ETSI MEC 2

3 High level view of a MEC deployment Applications: Live video analysis Privacy filter Personal assistant (productivity, sport ) Remote medicine Leverage virtualization at the edge N-level hierarchy 3

4 Context and motivation Spatiotemporal variations Mobile communications are generally spatially distributed according to the population density and activity, which vary in time Activity patterns The mobile traffic in the business areas differ from the mobile traffic in the transport, residential and entertainment areas [10, 14, 17] Such properties will be amplified with the realization of the IoT and 5G visions [7] 4

5 Objective: Dimension MEC areas (or clusters) A MEC partitioning must have the following properties MEC servers, as any compute, storage and network node, have a maximum capacity (e.g. in terms of CPU, storage resources, application hosting capabilities etc.) MEC server loads should be balanced both spatially and temporally to improve user experience The traffic between the MEC servers and the core should be minimized, in particular by consolidating applications at the MEC server level, such that the global latency is reduced A MEC cluster should be geo-consistent (connected) to rationalize the deployment 5

6 Related work Partitioning and MEC server placement Qazi [11] showed that the number and the locations of MEC servers have a direct impact on the QoE (imbalance loads and high latencies) and on the operational cost Ceselli et al. [19] have proposed an ILP for the joint problem of base stations allocation to MEC servers and routing to reduce infrastructure cost. The clusters are not geo-consistent, meaning that the base stations associated to a MEC server can be completely scattered in space. The computation does not scale. And us! 6 [11] Z. Ayyub Qazi, P. Krishna, V. Sekar, V. Gopalakrishnan, K. Joshi, and S. Das KLEIN: A Minimally Disruptive Design for an Elastic Cellular Core. In Proceedings of ACM Symposium on SDN Research (SOSR). [19] A. Ceselli, M. Premoli, and S. Secci Mobile Edge Cloud Network Design Optimization. IEEE/ACM Transactions on Networking 99 (2017), 1 14.

7 Our geo-partitioning algorithm for MEC resources MEC clustering algorithm inspired from the Louvain method (detection of communities in graphs) Aggregates local interactions (communications) up to a max. cluster load communication MEC cluster? 7

8 Algorithm: Initialization comm. 8

9 Algorithm: 1 st pass 9 The two grid cells that have the highest interaction are merged if it respects the max cluster capacity

10 Algorithm: 2 nd pass Until no pair of nodes can be merged because of max. server capacity 10 The two grid cells that have the highest interaction are merged if it respects the max cluster capacity

11 Dataset (1/2) In 2014, Telecom Italia organized the Telecom Italia Big Data Challenge ( Several types of Call Details Record (CDR) datasets were produced to measure the interaction intensity between different locations The dataset we used in this study: quantify the interactions within Milan (i.e., Milan to Milan) over November 2013 temporally aggregated every 10 min and spatially aggregated in a grid (next slide) (at most) 34% of population's data is collected, due to Telecom Italia's market share. Moreover there is no information about missed calls. Gianni Barlacchi et al., «A multi-source dataset of urban life in the city of Milan and the Province of Trentino, in Science Data

12 Dataset (2/2) Calls intensity City map of Milan Spatial discretization of Milan area (d=235m) Normalized mobile communication intensity (5pm-6pm, 11/04/2013) Call intensity: number proportional to the number of calls generated from one grid cell to one other grid cell 12

13 Evaluation - Varying day and day time (1/2) Number of MEC clusters Logically, as the maximum cluster capacity diminishes the number of clusters increases to serve traffic at the edge 13

14 Evaluation - Varying day and day time (1/2) Number of MEC clusters Logically, as the maximum cluster capacity diminishes the number of clusters increases to serve traffic at the edge Intra MEC cluster vs. total traffic (%) Traffic more localized on week-end 14 Core offloaded

15 Evaluation - Varying day and day time (2/2) 15 Clustering result For a maximum cluster capacity of 5% of the total communications, i.e. 8,500 communications (5pm-6pm, 11/04/2013). Th e numbers in the clusters correspond to their load. Well balanced server load MEC cluster loads The median is close to the maximum cluster capacity

16 Evaluation Through time (1/2) Traffic offloaded to the core (i.e. cluster saturation) < 3% Intra-core traffic ratio around 53% 16 Static clusters + dynamic demand => almost no server saturation!

17 Evaluation Through time (2/2) Normalized MEC cluster loads over a day (11/04/2015) with a partition done at 5pm and a maximum cluster capacity of 5% of the total communications 17 Well balanced server load through time

18 Conclusion We proposed a graph-based geo-partitioning algorithm for MEC resources The data-driven evaluation shows Core offloading (i.e. consolidation of the traffic at the edge) Well balanced server loads (even through time) 18 Future work Mathematical optimization model Group communications Combination with online application offloading and migration Experiments with SDN and NFV

19 References [1] Open Big Data. httšps://dandelion.eu/datamine/open-big-data/. (2014). Accessed: [2] G. Barlacchi, M De Nadai, R Larcher, and others A multi-source dataset of urban life in the city of Milan and the Province of Trentino. Scienti c Data 2, (2015). [3] V.D. Blondel, J.L. Guillaume, R. LambioŠe, and E.L.J.S. Mech Fast unfolding of communities in large networks. J. Stat. Mech (2008). Proceedings of ACM Symposium on SDN Research (SOSR). [12] R. Saunders, J. Cho, A. Banerjee, F. Rocha, and J. Van der Merwe P2P Offloading in Mobile Networks using SDN. In Proceedings of ACM Symposium on SDN Research (SOSR). [13] H. Tan, Z. Han, X.Y. Li, and F.C.M. Lau Online Job Dispatching and Scheduling in Edge-Clouds. In In Proceedings of IEEE International Conference on Computer Communications (INFOCOM) [4] C.-Y. Chang, K. Alexandris, N. Nikaein, K. Katsalis, and T. Spyropoulos MEC architectural implications for LTE/LTE-A networks. In Proceedings of the Workshop on Mobility in the Evolving Internet Architecture (MobiArch) [5] European Telecommunications Standards Institute (ETSI) Mobile-Edge Computing (MEC); Service Scenarios (GS MEC-IEG 004). [6] OpenFog Consortium Architecture Working Group OpenFog Architecture Overview (OPFWP ). [7] Y. C. Hu, M. Patel, D. Sabella, N. Sprecher, and V. Young Mobile Edge Computing A key technology towards 5G (ETSI White Paper No. 11). European Telecommunications Standards Institute (ETSI). [8] F. Manco, J. Martins, K. Yasukata, J. Mendes, S. Kuenzer, and F. Huici ŒThe Case for the Superƒuid Cloud. In In Proceedings of USENIX Workshop on Hot Topics in Cloud Computing (HotCloud). [9] Y. Mao, C. You, J. Zhang, K. Huang, and K. Ben Letaief Mobile Edge Computing: Survey and Research Outlook. CoRR abs/ (2017). hšttp//arxiv.org/abs/ [10] D. Naboulsi, M. Fiore, S. Ribot, and R. Stanica Large-scale Mobile Traffic Analysis: a Survey. IEEE Communications Surveys and Tutorials (2015). [11] Z. Ayyub Qazi, P. Krishna, V. Sekar, V. Gopalakrishnan, K. Joshi, and S. Das KLEIN: A Minimally Disruptive Design for an Elastic Cellular Core. In [14] N. Tastevin and M. Bouet Characterizing and modeling the distance of mobile calls: A metropolitan case study. In In Proceedings of IEEE 27th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). [15] L. Tong, Y. Li, and W. Gao A hierarchical edge cloud architecture for mobile computing. In In Proceedings of IEEE International Conference on Computer Communications (INFOCOM) [16] P.L. Ventre, C. Pisa, S. Salsano, G. Siracusano, F. Schmidt, P. Lungaroni, and N. Blefari-Melazzi Performance Evaluation and Tuning of Virtual Infrastructure Managers for (Micro) Virtual Network Functions. In In Proceedings of IEEE Conference on Network unction Virtualization and Software Defined Networks (NFV- SDN). [17] H. Wang, F. Xu, Y. Li, P. Zhang, and D. Jin Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment. In Proceedings of ACM Internet Measurement Conference (IMC). [18] Q. Xu, F. Qian, J. Huang, A. Gerber, Z. Wang, and Z. M. Mao Cellular data network infrastructure characterization and implication on mobile content placement. In Proceedings of ACM SIGMETRICS. [19] A. Ceselli, M. Premoli, and S. Secci Mobile Edge Cloud Network Design Optimization. IEEE/ACM Transactions on Networking 99 (2017), 1 14.