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Mitigating the Latency-Accuracy Trade-off in Mobile Data Analytics Systems

Published: 15 October 2018 Publication History

Abstract

An increasing amount of mobile analytics is performed on data that is procured in a real-time fashion to make real-time decisions. Such tasks include simple reporting on streams to sophisticated model building. However, the practicality of these analyses are impeded in several domains because they are faced with a fundamental trade-off between data collection latency and analysis accuracy. In this paper, we first study this trade-off in the context of a specific domain, Cellular Radio Access Networks (RAN). We find that the trade-off can be resolved using two broad, general techniques: intelligent data grouping and task formulations that leverage domain characteristics. Based on this, we present CellScope, a system that applies a domain specific formulation and application of Multi-task Learning (MTL) to RAN performance analysis. It uses three techniques: feature engineering to transform raw data into effective features, a PCA inspired similarity metric to group data from geographically nearby base stations sharing performance commonalities, and a hybrid online-offline model for efficient model updates. Our evaluation shows that CellScope's accuracy improvements over direct application of ML range from 2.5× to 4.4× while reducing the model update overhead by up to 4.8×. We have also used CellScope to analyze an LTE network of over 2 million subscribers, where it reduced troubleshooting efforts by several magnitudes. We then apply the underlying techniques in CellScope to another domain specific problem, mobile phone energy bug diagnosis, and show that the techniques are general.

References

[1]
3gpp. {n. d.}. Self-Organizing Networks SON Policy Network Resource Model (NRM) Integration Reference Point (IRP). http://www.3gpp.org/ ftp/Specs/archive/32_series/32.521/.
[2]
Bhavish Aggarwal, Ranjita Bhagwan, Tathagata Das, Siddharth Eswaran, Venkata N. Padmanabhan, and Geoffrey M. Voelker. 2009. NetPrints: diagnosing home network misconfigurations using shared knowledge. In Proceedings of the 6th USENIX symposium on Networked systems design and implementation (NSDI'09). USENIX Association, Berkeley, CA, USA, 349--364. http://dl.acm.org/citation.cfm?id= 1558977.1559001
[3]
Alcatel Lucent. 2013. 9900 Wireless Network Guardian. http://www. alcatel-lucent.com/products/9900-wireless-network-guardian.
[4]
Alcatel Lucent. 2014. 9959 Network Performance Optimizer. http://www.alcatel-lucent.com/products/ 9959-network-performance-optimizer.
[5]
Alcatel Lucent. 2014. Alcatel-Lucent Motive Big Network Analytics for service creation. http://resources.alcatel-lucent.com/?cid=170795.
[6]
Alcatel Lucent. 2014. Motive Big Network Analytics. http://www. alcatel-lucent.com/solutions/motive-big-network-analytics.
[7]
Paramvir Bahl, Ranveer Chandra, Albert Greenberg, Srikanth Kandula, David A. Maltz, and Ming Zhang. 2007. Towards Highly Reliable Enterprise Network Services via Inference of Multi-level Dependencies. In Proceedings of the 2007 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications (SIGCOMM '07). ACM, NewYork, NY, USA, 13--24.
[8]
Athula Balachandran, Vaneet Aggarwal, Emir Halepovic, Jeffrey Pang, Srinivasan Seshan, Shobha Venkataraman, and He Yan. 2014. Modeling Web Quality-of-experience on Cellular Networks. In Proceedings of the 20th Annual International Conference on Mobile Computing and Networking (MobiCom '14). ACM, New York, NY, USA, 213--224.
[9]
Raquel Barco, Volker Wille, Luis Díez, and Matías Toril. 2010. Learning of Model Parameters for Fault Diagnosis in Wireless Networks. Wirel. Netw. 16, 1 (Jan. 2010), 255--271.
[10]
Jonathan Baxter. 2000. A Model of Inductive Bias Learning. J. Artif. Int. Res. 12, 1 (March 2000), 149--198. http://dl.acm.org/citation.cfm? id=1622248.1622254
[11]
Richard Caruana. 1993. Multitask Learning: A Knowledge-Based Source of Inductive Bias. In Proceedings of the Tenth International Conference on Machine Learning. Morgan Kaufmann, 41--48.
[12]
Ira Cohen, Moises Goldszmidt, Terence Kelly, Julie Symons, and Jeffrey S. Chase. 2004. Correlating Instrumentation Data to System States: A Building Block for Automated Diagnosis and Control. In Proceedings of the 6th Conference on Symposium on Opearting Systems Design & Implementation - Volume 6 (OSDI'04). USENIX Association, Berkeley, CA, USA, 16--16. http://dl.acm.org/citation.cfm?id=1251254.1251270
[13]
Chuck Cranor, Theodore Johnson, Oliver Spataschek, and Vladislav Shkapenyuk. 2003. Gigascope: a stream database for network applications. In Proceedings of the 2003 ACM SIGMOD international conference on Management of data (SIGMOD '03). ACM, New York, NY, USA, 647--651.
[14]
Thomas G Dietterich. 2000. Ensemble methods in machine learning. In Multiple classifier systems. Springer, 1--15.
[15]
Ericsson. 2012. Ericsson RAN Analyzer Overview. http://www. optxview.com/Optimi_Ericsson/RANAnalyser.pdf.
[16]
Ericsson. 2014. Ericsson RAN Analyzer. http://www.ericsson.com/ ourportfolio/products/ran-analyzer.
[17]
Theodoros Evgeniou and Massimiliano Pontil. 2004. Regularized Multi-- task Learning. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '04). ACM, NewYork, NY, USA, 109--117.
[18]
Jerome H Friedman. 2001. Greedy function approximation: a gradient boosting machine. Annals of statistics (2001), 1189--1232.
[19]
João Gama, Indre Žliobaite, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia. 2014. A Survey on Concept Drift Adaptation. ACM Comput. Surv. 46, 4, Article 44 (March 2014), 37 pages.
[20]
Pinghua Gong, Jieping Ye, and Changshui Zhang. 2012. Robust Multitask Feature Learning. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '12). ACM, New York, NY, USA, 895--903.
[21]
Nikhil Handigol, Brandon Heller, Vimalkumar Jeyakumar, David Mazières, and Nick McKeown. 2014. I Know What Your Packet Did Last Hop: Using Packet Histories to Troubleshoot Networks. In Proceedings of the 11th USENIX Conference on Networked Systems Design and Implementation (NSDI'14). USENIX Association, Berkeley, CA, USA, 71--85. http://dl.acm.org/citation.cfm?id=2616448.2616456
[22]
Paul Harris, Chris Brunsdon, and Martin Charlton. 2011. Geographically weighted principal components analysis. International Journal of Geographical Information Science 25, 10 (2011), 1717--1736.
[23]
Chi-Yao Hong, MatthewCaesar, Nick Duffield, and JiaWang. 2012. Tiresias: Online Anomaly Detection for Hierarchical Operational Network Data. In Proceedings of the 2012 IEEE 32Nd International Conference on Distributed Computing Systems (ICDCS '12). IEEE Computer Society, Washington, DC, USA, 173--182.
[24]
Junxian Huang, Feng Qian, Yihua Guo, Yuanyuan Zhou, Qiang Xu, Z. Morley Mao, Subhabrata Sen, and Oliver Spatscheck. 2013. An In-depth Study of LTE: Effect of Network Protocol and Application Behavior on Performance. In Proceedings of the ACM SIGCOMM 2013 Conference on SIGCOMM (SIGCOMM '13). ACM, New York, NY, USA, 363--374.
[25]
Anand Iyer, Li Erran Li, and Ion Stoica. 2015. CellIQ : Real-Time Cellular Network Analytics at Scale. In 12th USENIX Symposium on Networked Systems Design and Implementation (NSDI 15). USENIX Association, Oakland, CA, 309--322. https://www.usenix.org/conference/nsdi15/ technical-sessions/presentation/iyer
[26]
Anand Padmanabha Iyer, Li Erran Li, and Ion Stoica. 2017. Automating Diagnosis of Cellular Radio Access Network Problems. In Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking (MobiCom '17). ACM, New York, NY, USA, 79--87.
[27]
Haiqing Jiang, Yaogong Wang, Kyunghan Lee, and Injong Rhee. 2012. Tackling Bufferbloat in 3G/4G Networks. In Proceedings of the 2012 ACM Conference on Internet Measurement Conference (IMC '12). ACM, NewYork, NY, USA, 329--342.
[28]
Srikanth Kandula, Ratul Mahajan, Patrick Verkaik, Sharad Agarwal, Jitendra Padhye, and Paramvir Bahl. 2009. Detailed diagnosis in enterprise networks. In Proceedings of the ACM SIGCOMM 2009 conference on Data communication (SIGCOMM '09). ACM, New York, NY, USA, 243--254.
[29]
Gunjan Khanna, Mike Yu Cheng, Padma Varadharajan, Saurabh Bagchi, Miguel P. Correia, and Paulo J. Veríssimo. 2007. Automated Rule- Based Diagnosis Through a Distributed Monitor System. IEEE Trans. Dependable Secur. Comput. 4, 4 (Oct. 2007), 266--279.
[30]
Seyoung Kim and Eric P. Xing. 2010. Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity. Intenational Conference on Machine Learning (ICML) (2010).
[31]
Tim Kraska, Ameet Talwalkar, John C. Duchi, Rean Griffith, Michael J. Franklin, and Michael I. Jordan. 2013. MLbase: A Distributed Machinelearning System. In CIDR. http://www.cidrdb.org/cidr2013/Papers/ CIDR13_Paper118.pdf
[32]
WJ Krzanowski. 1979. Between-groups comparison of principal components. J. Amer. Statist. Assoc. 74, 367 (1979), 703--707.
[33]
Anukool Lakhina, Mark Crovella, and Christophe Diot. 2004. Diagnosing Network-wide Traffic Anomalies. In Proceedings of the 2004 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications (SIGCOMM '04). ACM, New York, NY, USA, 219--230.
[34]
Yan Liu, Jing Zhang, M. Jiang, D. Raymer, and J. Strassner. 2008. A model-based approach to adding autonomic capabilities to network fault management system. In Network Operations and Management Symposium, 2008. NOMS 2008. IEEE. 859--862.
[35]
Adam J. Oliner, Anand P. Iyer, Ion Stoica, Eemil Lagerspetz, and Sasu Tarkoma. 2013. Carat: Collaborative Energy Diagnosis for Mobile Devices. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems (SenSys '13). ACM, New York, NY, USA, Article 10, 14 pages.
[36]
Yan Pan, Rongkai Xia, Jian Yin, and Ning Liu. 2015. A Divide-and- Conquer Method for Scalable Robust Multitask Learning. Neural Networks and Learning Systems, IEEE Transactions on 26, 12 (Dec 2015), 3163--3175.
[37]
K. Pearson. 1901. On lines and planes of closest fit to systems of points in space. Philos. Mag. 2, 6 (1901), 559--572.
[38]
Feng Qian, Zhaoguang Wang, Alexandre Gerber, Zhuoqing Mao, Subhabrata Sen, and Oliver Spatscheck. 2011. Profiling Resource Usage for Mobile Applications: A Cross-layer Approach. In Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services (MobiSys '11). ACM, New York, NY, USA, 321--334.
[39]
Sudarshan Rao. 2006. Operational Fault Detection in Cellular Wireless Base-stations. IEEE Trans. on Netw. and Serv. Manag. 3, 2 (April 2006), 1--11.
[40]
Sanae Rosen, Haokun Luo, Qi Alfred Chen, Z. Morley Mao, Jie Hui, Aaron Drake, and Kevin Lau. 2014. Discovering Fine-grained RRC State Dynamics and Performance Impacts in Cellular Networks. In Proceedings of the 20th Annual International Conference on Mobile Computing and Networking (MobiCom '14). ACM, New York, NY, USA, 177--188.
[41]
AhmedMSafwat and Hussein Mouftah. 2005. 4G network technologies for mobile telecommunications. Network, IEEE 19, 5 (2005), 3--4.
[42]
Stefania Sesia, Issam Toufik, and Matthew Baker. 2009. LTE: the UMTS long term evolution. Wiley Online Library.
[43]
Muhammad Zubair Shafiq, Jeffrey Erman, Lusheng Ji, Alex X. Liu, Jeffrey Pang, and Jia Wang. 2014. Understanding the Impact of Network Dynamics on Mobile Video User Engagement. In The 2014 ACM International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS '14). ACM, New York, NY, USA, 367--379.
[44]
Muhammad Zubair Shafiq, Lusheng Ji, Alex X. Liu, Jeffrey Pang, Shobha Venkataraman, and Jia Wang. 2013. A First Look at Cellular Network Performance During Crowded Events. In Proceedings of the ACM SIGMETRICS/International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS '13). ACM, New York, NY, USA, 17--28.
[45]
Shai Shalev-Shwartz and Ambuj Tewari. 2011. Stochastic Methods for L1-regularized Loss Minimization. J. Mach. Learn. Res. 12 (July 2011), 1865--1892. http://dl.acm.org/citation.cfm?id=1953048.2021059
[46]
Clint Smith. 2006. 3G wireless networks. McGraw-Hill, Inc.
[47]
Evan R. Sparks, Ameet Talwalkar, Virginia Smith, Jey Kottalam, Xinghao Pan, Joseph E. Gonzalez, Michael J. Franklin, Michael I. Jordan, and Tim Kraska. 2013. MLI: An API for Distributed Machine Learning. In 2013 IEEE 13th International Conference on Data Mining, Dallas, TX, USA, December 7--10, 2013, Hui Xiong, George Karypis, Bhavani M. Thuraisingham, Diane J. Cook, and Xindong Wu (Eds.). IEEE Computer Society, 1187--1192.
[48]
Technical Specification Group. {n. d.}. 3GPP Specifications. http: //www.3gpp.org/specifications.
[49]
Nawanol Theera-Ampornpunt, Saurabh Bagchi, Kaustubh R. Joshi, and Rajesh K. Panta. 2013. Using Big Data for More Dependability: A Cellular Network Tale. In Proceedings of the 9th Workshop on Hot Topics in Dependable Systems (HotDep '13). ACM, New York, NY, USA, Article 2, 5 pages.
[50]
Sebastian Thrun. 1996. Is Learning The n-th Thing Any Easier Than Learning The First?. In Advances in Neural Information Processing Systems. The MIT Press, 640--646.
[51]
Robert Tibshirani. 1994. Regression Shrinkage and Selection Via the Lasso. Journal of the Royal Statistical Society, Series B 58 (1994), 267-- 288.
[52]
Guan-Hua Tu, Yuanjie Li, Chunyi Peng, Chi-Yu Li, Hongyi Wang, and Songwu Lu. 2014. Control-plane Protocol Interactions in Cellular Networks. In Proceedings of the 2014 ACM Conference on SIGCOMM (SIGCOMM '14). ACM, New York, NY, USA, 223--234.
[53]
Helen J.Wang, John C. Platt, Yu Chen, Ruyun Zhang, and Yi-MinWang. 2004. Automatic Misconfiguration Troubleshooting with Peerpressure. In Proceedings of the 6th Conference on Symposium on Opearting Systems Design & Implementation - Volume 6 (OSDI'04). USENIX Association, Berkeley, CA, USA, 17--17. http://dl.acm.org/citation.cfm?id=1251254. 1251271
[54]
He Yan, A. Flavel, Zihui Ge, A. Gerber, D. Massey, C. Papadopoulos, H. Shah, and J. Yates. 2012. Argus: End-to-end service anomaly detection and localization from an ISP's point of view. In INFOCOM, 2012 Proceedings IEEE. 2756--2760.
[55]
Kiyoung Yang and Cyrus Shahabi. 2004. A PCA-based Similarity Measure for Multivariate Time Series. In Proceedings of the 2nd ACM International Workshop on Multimedia Databases (MMDB '04). ACM, New York, NY, USA, 65--74.
[56]
Matei Zaharia, Mosharaf Chowdhury, Tathagata Das, Ankur Dave, Justin Ma, Murphy McCauley, Michael J. Franklin, Scott Shenker, and Ion Stoica. 2012. Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation (NSDI'12). USENIX Association, Berkeley, CA, USA, 2--2. http: //dl.acm.org/citation.cfm?id=2228298.2228301
[57]
Matei Zaharia, Tathagata Das, Haoyuan Li, Timothy Hunter, Scott Shenker, and Ion Stoica. 2013. Discretized Streams: Fault-tolerant Streaming Computation at Scale. In Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles (SOSP '13). ACM, New York, NY, USA, 423--438.
[58]
Ce Zhang, Arun Kumar, and Christopher Ré. 2014. Materialization Optimizations for Feature Selection Workloads. In Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data (SIGMOD '14). ACM, New York, NY, USA, 265--276.
[59]
Alice X. Zheng, Jim Lloyd, and Eric Brewer. 2004. Failure Diagnosis Using Decision Trees. In Proceedings of the First International Conference on Autonomic Computing (ICAC '04). IEEE Computer Society, Washington, DC, USA, 36--43. http://dl.acm.org/citation.cfm?id=1078026. 1078407

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  1. Mitigating the Latency-Accuracy Trade-off in Mobile Data Analytics Systems

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      cover image ACM Conferences
      MobiCom '18: Proceedings of the 24th Annual International Conference on Mobile Computing and Networking
      October 2018
      884 pages
      ISBN:9781450359030
      DOI:10.1145/3241539
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      Published: 15 October 2018

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      Author Tags

      1. cellular networks
      2. data analytics
      3. mobile systems

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      MobiCom '18 Paper Acceptance Rate 42 of 187 submissions, 22%;
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