Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/2873587.2873599acmconferencesArticle/Chapter ViewAbstractPublication PageshotmobileConference Proceedingsconference-collections
research-article
Public Access

A First Look at Unstable Mobility Management in Cellular Networks

Published: 23 February 2016 Publication History

Abstract

Mobility management is a prominent feature in cellular networks. In this paper, we examine the (in)stability of mobility management. We disclose that handoff may never converge in some real-world cases. We focus on persistent handoff oscillations, rather than those transient ones caused by dynamic networking environment and user mobility (e.g., moving back and force between two base stations). Our study reveals that persistent handoff loops indeed exist in operational cellular networks. They not only violate their design goals, but also incur excessive signaling overhead and data performance degradation. To detect and validate instability in mobility management, we devise MMDIAG, an in-device diagnosis tool for cellular network operations. The core of MMDIAG is to build a handoff decision automata based on 3GPP standards, and detect possible loops by checking the structural property of stability. We first leverage device-network signaling exchanges to retrieve mobility management policies and configurations, and then feed them into MMDIAG, along with runtime measurements. MMDIAG further emulates various handoff scenarios and identifies possible violations (i.e., loops) caused by the used policies and configurations. Finally, we validate the identified problems through real measurements over operational networks. Our preliminary results with a top-tier US carrier demonstrate that, unstable mobility management indeed occurs in reality and hurts both carriers and users. The proposed methodology is effective to identify persistent instabilities and pinpoint their root causes in problematic configurations and policy conflicts.

References

[1]
C. Brunner, A. Garavaglia, M. Mittal, M. Narang, and J. V. Bautista. Inter-system Handover Parameter Optimization. In VTC Fall, 2006.
[2]
A. Lobinger, S. Stefanski, T. Jansen, and I. Balan. Coordinating Handover Parameter Optimization and Load Balancing in LTE Self-Optimizing Networks. In VTC Spring. IEEE, 2011.
[3]
3GPP. TS23.401: GPRS Enhancements for E-UTRAN Access, 2011.
[4]
3GPP. TS25.304: User Equipment (UE) Procedures in Idle Mode and Procedures for Cell Reselection in Connected Mode, 2012.
[5]
3GPP. TS36.304: E-UTRA; User Equipment Procedures in Idle Mode, 2015.
[6]
3GPP. TS23.009: Handover Procedures, 2011.
[7]
3GPP. TS23.272: Circuit Switched (CS) fallback in Evolved Packet System (EPS), 2012.
[8]
3GPP. TS 23.216: Single Radio Voice Call Continuity (SRVCC), 2011.
[9]
3GPP. TS25.367: Mobility procedures for Home Node B, 2014.
[10]
3GPP. TS23.261: IP flow mobility and seamless Wireless Local Area Network (WLAN) offload; Stage 2, 2014.
[11]
3GPP. TS32.500: Self-Organizing Networks (SON); Concepts and requirements, 2014.
[12]
G.-H. Tu, Y. Li, C. Peng, C.-Y. Li, H. Wang, and S. Lu. Control-Plane Protocol Interactions in Cellular Networks. In SIGCOMM, 2014.
[13]
G. Tu, C. Peng, H. Wang, C. Li, and S. Lu. How Voice Calls Affect Data in Operational LTE Networks. In MobiCom, Oct. 2013.
[14]
Mobileinsight project. http://metro.cs.ucla.edu/mobile_insight.
[15]
QUALCOMM eXtensible Diagnostic Monitor. http://www.qualcomm.com/media/documents/tags/qxdm.
[16]
Mediatek. Xcal-mobile. http://www.accuver.com.
[17]
3GPP. TS36.331: E-UTRA; Radio Resource Control (RRC), 2012.
[18]
3GPP. TS24.008: Mobile Radio Interface Layer 3, 2012.
[19]
3GPP. TS24.301: Non-Access-Stratum (NAS) for EPS;, Jun. 2013.
[20]
G.-H. Tu, Y. Li, C. Peng, C.-Y. Li, and S. Lu. Detecting problematic control-plane protocol interactions in mobile networks. IEEE Transactions on Networking (TON), pages 1--14, March 2015.
[21]
M. Liu, Z. Li, X. Guo, and E. Dutkiewicz. Performance Analysis and Optimization of Handoff Algorithms in Heterogeneous Wireless Networks. IEEE Transactions on Mobile Computing, 7(7):846--857, July 2008.
[22]
A. Balasubramanian, R. Mahajan, and A. Venkataramani. Augmenting mobile 3g using wifi. In ACM MobiSys, June 2010.
[23]
W. Dong, S. Rallapalli, R. Jana, L. Qiu, K. Ramakrishnan, L. Razoumov, Y. Zhang, and T. W. Cho. ideal: Incentivized dynamic cellular offloading via auctions. TON, 22(4):1271--1284, 2014.
[24]
F. P. Tso, J. Teng, W. Jia, and D. Xuan. Mobility: A Double-Edged Sword for HSPA Networks: A Large-Scale Test on Hong Kong Mobile HSPA Networks. IEEE Transactions on Parallel and Distributed Systems, 23(10):1895--1907, 2012.
[25]
N. Balasubramanian, A. Balasubramanian, and A. Venkataramani. Energy consumption in mobile phones: A measurement study and implications for network applications. In IMC, 2009.
[26]
U. Javed, D. Han, R. Caceres, J. Pang, S. Seshan, and A. Varshavsky. Predicting handoffs in 3g networks. In MobiHeld, 2011.
[27]
T. G. Griffin and G. Wilfong. An Analysis of BGP Convergence Properties. In ACM SIGCOMM, 1999.
[28]
V. Pappas, Z. Xu, S. Lu, D. Massey, A. Terzis, and L. Zhang. Impact of Configuration Errors on DNS Robustness. In SIGCOMM, 2004.
[29]
B. Aggarwal, R. Bhagwan, T. Das, S. Eswaran, V. N. Padmanabhan, and G. M. Voelker. NetPrints: Diagnosing Home Network Misconfigurations Using Shared Knowledge. In NSDI, 2009.
[30]
P. Sun, R. Mahajan, J. Rexford, L. Yuan, M. Zhang, and A. Arefin. A Network-State Management Service. In ACM SIGCOMM, 2014.

Cited By

View all
  • (2023)Octopus: Exploiting the Edge Intelligence for Accessible 5G Mobile Performance EnhancementIEEE/ACM Transactions on Networking10.1109/TNET.2022.322436931:6(2454-2469)Online publication date: Dec-2023
  • (2022)Vivisecting mobility management in 5G cellular networksProceedings of the ACM SIGCOMM 2022 Conference10.1145/3544216.3544217(86-100)Online publication date: 22-Aug-2022
  • (2022)Measurement-Based Optimization of Cell Selection in NB-IoT NetworksACM Transactions on Sensor Networks10.1145/354401718:4(1-19)Online publication date: 29-Nov-2022
  • Show More Cited By

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences
HotMobile '16: Proceedings of the 17th International Workshop on Mobile Computing Systems and Applications
February 2016
120 pages
ISBN:9781450341455
DOI:10.1145/2873587
  • General Chair:
  • David Chu,
  • Program Chair:
  • Prabal Dutta
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 February 2016

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. cellular networks
  2. instability
  3. mobility management
  4. persistent loop

Qualifiers

  • Research-article

Funding Sources

Conference

HotMobile '16
Sponsor:

Acceptance Rates

HotMobile '16 Paper Acceptance Rate 18 of 55 submissions, 33%;
Overall Acceptance Rate 96 of 345 submissions, 28%

Upcoming Conference

HOTMOBILE '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)115
  • Downloads (Last 6 weeks)16
Reflects downloads up to 13 Nov 2024

Other Metrics

Citations

Cited By

View all
  • (2023)Octopus: Exploiting the Edge Intelligence for Accessible 5G Mobile Performance EnhancementIEEE/ACM Transactions on Networking10.1109/TNET.2022.322436931:6(2454-2469)Online publication date: Dec-2023
  • (2022)Vivisecting mobility management in 5G cellular networksProceedings of the ACM SIGCOMM 2022 Conference10.1145/3544216.3544217(86-100)Online publication date: 22-Aug-2022
  • (2022)Measurement-Based Optimization of Cell Selection in NB-IoT NetworksACM Transactions on Sensor Networks10.1145/354401718:4(1-19)Online publication date: 29-Nov-2022
  • (2021)A nationwide study on cellular reliabilityProceedings of the 2021 ACM SIGCOMM 2021 Conference10.1145/3452296.3472908(597-609)Online publication date: 9-Aug-2021
  • (2021)Insecurity of operational cellular IoT serviceProceedings of the 27th Annual International Conference on Mobile Computing and Networking10.1145/3447993.3483239(437-450)Online publication date: 25-Oct-2021
  • (2021)A First Look at Energy Consumption of NB-IoT in the Wild: Tools and Large-Scale MeasurementIEEE/ACM Transactions on Networking10.1109/TNET.2021.309665629:6(2616-2631)Online publication date: Dec-2021
  • (2020)Understanding power consumption of NB-IoT in the wildProceedings of the 26th Annual International Conference on Mobile Computing and Networking10.1145/3372224.3419212(1-13)Online publication date: 16-Apr-2020
  • (2020)Measuring and Optimizing Cell Selection of NB-IoT Network2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)10.1109/MASS50613.2020.00061(446-454)Online publication date: Dec-2020
  • (2019)Leveraging Context-Triggered Measurements to Characterize LTE Handover PerformancePassive and Active Measurement10.1007/978-3-030-15986-3_1(3-17)Online publication date: 13-Mar-2019
  • (2018)Mobility Support in Cellular NetworksProceedings of the Internet Measurement Conference 201810.1145/3278532.3278546(147-160)Online publication date: 31-Oct-2018
  • Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Get Access

Login options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media