Data-Driven Techniques in Computing System Management

Modern forms of computing systems are becoming progressively more complex, with an increasing number of heterogeneous hardware and software components. As a result, it is quite challenging to manage these complex systems and meet the requirements in manageability, dependability, and performance that are demanded by enterprise customers. This survey presents a variety of data-driven techniques and applications with a focus on computing system management. In particular, the survey introduces intelligent methods for event generation that can transform diverse log data sources into structured events, reviews different types of event patterns and the corresponding event-mining techniques, and summarizes various event summarization methods and data-driven approaches for problem diagnosis in system management. We hope this survey will provide a good overview for data-driven techniques in computing system management.

[1]  Yehuda Koren,et al.  Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[2]  Philip S. Yu,et al.  Mining asynchronous periodic patterns in time series data , 2000, KDD '00.

[3]  Wei Peng,et al.  An Integrated Data-Driven Framework for Computing System Management , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[4]  Larisa Shwartz,et al.  Hierarchical multi-label classification over ticket data using contextual loss , 2014, 2014 IEEE Network Operations and Management Symposium (NOMS).

[5]  Daniela Rosu,et al.  Multi-dimensional Knowledge Integration for Efficient Incident Management in a Services Cloud , 2009, 2009 IEEE International Conference on Services Computing.

[6]  Yuriy Brun,et al.  Synoptic: Summarizing System Logs with Refinement , 2010, SLAML.

[7]  Joseph L. Hellerstein,et al.  Discovering Fully Dependent Patterns , 2002, SDM.

[8]  Qing Wang,et al.  Online Context-Aware Recommendation with Time Varying Multi-Armed Bandit , 2016, KDD.

[9]  Loren Terveen,et al.  Beyond Recommender Systems: Helping People Help Each Other , 2001 .

[10]  K. P. Unnikrishnan,et al.  Fast algorithms for frequent episode discovery in event sequences , 2004 .

[11]  Philippe O. A. Navaux,et al.  A Survey of the State-of-the-art in Event Processing , 2013 .

[12]  Cuntai Guan,et al.  Mutual information-based selection of optimal spatial-temporal patterns for single-trial EEG-based BCIs , 2012, Pattern Recognit..

[13]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[14]  Wei Peng,et al.  An integrated framework on mining logs files for computing system management , 2005, KDD '05.

[15]  Yixin Diao,et al.  A framework for predicting service delivery efforts using IT infrastructure-to-incident correlation , 2014, 2014 IEEE Network Operations and Management Symposium (NOMS).

[16]  Dirk Husemann,et al.  Automatic Classification of Change Requests for Improved IT Service Quality , 2011, 2011 Annual SRII Global Conference.

[17]  Mark A. Pitt,et al.  Advances in Minimum Description Length: Theory and Applications , 2005 .

[18]  Johannes Gehrke,et al.  Sequential PAttern mining using a bitmap representation , 2002, KDD.

[19]  Thomas J Owens.  Survey of Event Processing , 2007 .

[20]  John Stearley,et al.  Towards informatic analysis of syslogs , 2004, 2004 IEEE International Conference on Cluster Computing (IEEE Cat. No.04EX935).

[21]  Larisa Shwartz,et al.  An Integrated Framework for Mining Temporal Logs from Fluctuating Events , 2019, IEEE Transactions on Services Computing.

[22]  John F. Roddick,et al.  Sequential pattern mining -- approaches and algorithms , 2013, CSUR.

[23]  Navendu Jain,et al.  Juggling the Jigsaw: Towards Automated Problem Inference from Network Trouble Tickets , 2013, NSDI.

[24]  Michal Aharon,et al.  One Graph Is Worth a Thousand Logs: Uncovering Hidden Structures in Massive System Event Logs , 2009, ECML/PKDD.

[25]  Ricard Gavaldà,et al.  Mining frequent closed trees in evolving data streams , 2011, Intell. Data Anal..

[26]  Yehuda Koren,et al.  Collaborative filtering with temporal dynamics , 2009, KDD.

[27]  Heikki Mannila,et al.  Rule Discovery from Time Series , 1998, KDD.

[28]  P. S. Sastry,et al.  A fast algorithm for finding frequent episodes in event streams , 2007, KDD '07.

[29]  Joseph L. Hellerstein,et al.  Discovering actionable patterns in event data , 2002, IBM Syst. J..

[30]  Wei Peng,et al.  Mining logs files for data-driven system management , 2005, SKDD.

[31]  Jiawei Han,et al.  Mining top-k frequent closed patterns without minimum support , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[32]  Common Event Expression , 2008 .

[33]  Ramakrishnan Srikant,et al.  Mining quantitative association rules in large relational tables , 1996, SIGMOD '96.

[34]  Umeshwar Dayal,et al.  FreeSpan: frequent pattern-projected sequential pattern mining , 2000, KDD '00.

[35]  Juan Carlos Niebles,et al.  Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words , 2006, BMVC.

[36]  Kyuseok Shim,et al.  SPIRIT: Sequential Pattern Mining with Regular Expression Constraints , 1999, VLDB.

[37]  Zheng Liu,et al.  FLAP: An End-to-End Event Log Analysis Platform for System Management , 2017, KDD.

[38]  Ling Huang,et al.  Mining Console Logs for Large-Scale System Problem Detection , 2008, SysML.

[39]  Tao Li,et al.  Intelligent cloud capacity management , 2012, 2012 IEEE Network Operations and Management Symposium.

[40]  Jilles Vreeken,et al.  The long and the short of it: summarising event sequences with serial episodes , 2012, KDD.

[41]  P. Sammulal,et al.  Survey on Sequential Pattern Mining Algorithms , 2013 .

[42]  Sheldon M. Ross,et al.  Stochastic Processes , 2018, Gauge Integral Structures for Stochastic Calculus and Quantum Electrodynamics.

[43]  Joseph L. Hellerstein,et al.  Event relationship networks: a framework for action oriented analysis in event management , 2001, 2001 IEEE/IFIP International Symposium on Integrated Network Management Proceedings. Integrated Network Management VII. Integrated Management Strategies for the New Millennium (Cat. No.01EX470).

[44]  Jorma Rissanen,et al.  The Minimum Description Length Principle in Coding and Modeling , 1998, IEEE Trans. Inf. Theory.

[45]  Evimaria Terzi,et al.  Constructing comprehensive summaries of large event sequences , 2009, TKDD.

[46]  R. Durrett.  Probability: Theory and Examples , 1993 .

[47]  Qing Wang,et al.  Constructing the Knowledge Base for Cognitive IT Service Management , 2017, 2017 IEEE International Conference on Services Computing (SCC).

[48]  Junjie Yao,et al.  Temporal and Social Context Based Burst Detection from Folksonomies , 2010, AAAI.

[49]  Mariëlle Stoelinga,et al.  Fault tree analysis: A survey of the state-of-the-art in modeling, analysis and tools , 2014, Comput. Sci. Rev..

[50]  Liang Tang,et al.  An integrated framework for optimizing automatic monitoring systems in large IT infrastructures , 2013, KDD.

[51]  Tao Li,et al.  Natural event summarization , 2011, CIKM '11.

[52]  Tao Li,et al.  LogSig: generating system events from raw textual logs , 2011, CIKM '11.

[53]  Evangelos E. Milios,et al.  Clustering event logs using iterative partitioning , 2009, KDD.

[54]  Aomar Osmani,et al.  Mining Association Rules in Temporal Sequences , 2007, 2007 IEEE Symposium on Computational Intelligence and Data Mining.

[55]  Tao Li,et al.  META: Multi-resolution Framework for Event Summarization , 2014, SDM.

[56]  Sheng Ma,et al.  Generic adapter logging toolkit , 2004, International Conference on Autonomic Computing, 2004. Proceedings..

[57]  Jian Pei,et al.  On computing condensed frequent pattern bases , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[58]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[59]  Luc De Raedt,et al.  k-Pattern Set Mining under Constraints , 2013, IEEE Transactions on Knowledge and Data Engineering.

[60]  Frank Höppner.  Discovery of Temporal Patterns. Learning Rules about the Qualitative Behaviour of Time Series , 2001, PKDD.

[61]  Liang Tang,et al.  Discovering lag intervals for temporal dependencies , 2012, KDD.

[62]  David Lanyi,et al.  Classifying server behavior and predicting impact of modernization actions , 2013, Proceedings of the 9th International Conference on Network and Service Management (CNSM 2013).

[63]  Huaiyu Zhu.  On Information and Sufficiency , 1997 .

[64]  Alessandro Margara,et al.  Processing flows of information: From data stream to complex event processing , 2012, CSUR.

[65]  Jon M. Kleinberg,et al.  Bursty and Hierarchical Structure in Streams , 2002, Data Mining and Knowledge Discovery.

[66]  George Karypis,et al.  Evaluation of Item-Based Top-N Recommendation Algorithms , 2001, CIKM '01.

[67]  Anoop Jain,et al.  A Comprehensive Survey on Sequential Pattern Mining , 2012 .

[68]  David Lanyi,et al.  Impact of HW and OS type and currency on server availability derived from problem ticket analysis , 2014, 2014 IEEE Network Operations and Management Symposium (NOMS).

[69]  Jaideep Srivastava,et al.  Event detection from time series data , 1999, KDD '99.

[70]  Avinash Achar,et al.  Pattern-growth based frequent serial episode discovery , 2013, Data Knowl. Eng..

[71]  Jorma Rissanen,et al.  Minimum Description Length Principle , 2010, Encyclopedia of Machine Learning.

[72]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[73]  Wei Peng,et al.  A Clustering Model Based on Matrix Approximation with Applications to Cluster System Log Files , 2005, ECML.

[74]  Joseph L. Hellerstein,et al.  Mining mutually dependent patterns , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[75]  Jeffrey O. Kephart,et al.  The Vision of Autonomic Computing , 2003, Computer.

[76]  Brian Randell,et al.  Fundamental Concepts of Dependability , 2000 .

[77]  Jasmina Bogojeska,et al.  Analysis of Labor Efforts and their Impact Factors to Solve Server Incidents in Datacenters , 2014, 2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

[78]  Tao Li,et al.  Mining temporal patterns without predefined time windows , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[79]  Shashi Shekhar,et al.  Mixed-Drove Spatio-Temporal Co-occurrence Pattern Mining : A Summary of Results , 2006 .

[80]  Brian Randell,et al.  Fundamental Concepts of Computer System Dependability , 2001 .

[81]  Naren Ramakrishnan,et al.  Efficient Episode Mining of Dynamic Event Streams , 2012, 2012 IEEE 12th International Conference on Data Mining.

[82]  Heikki Mannila,et al.  Discovery of Frequent Episodes in Event Sequences , 1997, Data Mining and Knowledge Discovery.

[83]  Larisa Shwartz,et al.  Knowledge Guided Hierarchical Multi-Label Classification Over Ticket Data , 2017, IEEE Transactions on Network and Service Management.

[84]  Navendu Jain,et al.  ConfSeer: Leveraging Customer Support Knowledge Bases for Automated Misconfiguration Detection , 2015, Proc. VLDB Endow..

[85]  Takeaki Uno,et al.  Frequent Pattern Mining , 2016, Encyclopedia of Algorithms.

[86]  Tao,et al.  A Survey on Event Mining for ICT Network Infrastructure Management , 2016 .

[87]  Seraphin B. Calo,et al.  Towards a practical alarm correlation system , 1995, Integrated Network Management.

[88]  Liang Tang,et al.  Recommending resolutions for problems identified by monitoring , 2013, 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013).

[89]  Liang Tang,et al.  LogTree: A Framework for Generating System Events from Raw Textual Logs , 2010, 2010 IEEE International Conference on Data Mining.

[90]  Wei Peng,et al.  Event summarization for system management , 2007, KDD '07.

[91]  Joong Hyuk Chang,et al.  Mining weighted sequential patterns in a sequence database with a time-interval weight , 2011, Knowl. Based Syst..

[92]  Tao Li,et al.  Event Mining: Algorithms and Applications , 2015 .

[93]  Qiming Chen,et al.  PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth , 2001, Proceedings 17th International Conference on Data Engineering.

[94]  Nikolay Mehandjiev,et al.  Multi-criteria service recommendation based on user criteria preferences , 2011, RecSys '11.

[95]  Massimiliano Di Penta,et al.  An approach to classify software maintenance requests , 2002, International Conference on Software Maintenance, 2002. Proceedings..

[96]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[97]  Detlef Nauck,et al.  Data stream mining of event and complex event streams: a survey of existing and future technologies and applications in big data , 2016 .

[98]  Jiawei Han,et al.  Summarizing itemset patterns: a profile-based approach , 2005, KDD '05.

[99]  Petr Jan Horn,et al.  Autonomic Computing: IBM's Perspective on the State of Information Technology , 2001 .

[100]  Noureddine Mouaddib,et al.  Time sequence summarization to scale up chronology-dependent applications , 2009, CIKM.

[101]  George Karypis,et al.  SLIM: Sparse Linear Methods for Top-N Recommender Systems , 2011, 2011 IEEE 11th International Conference on Data Mining.

[102]  Jean S. Bozman,et al.  Server Refresh: Meeting the Changing Needs of Enterprise IT with Hardware/Software Optimization , 2010 .

[103]  Lajos Jeno Fülöp,et al.  Survey on Complex Event Processing and Predictive Analytics , 2010 .

[104]  Antonio Hernández-Barrera,et al.  Finding an o(n2 log n) Algorithm Is Sometimes Hard , 1996, Canadian Conference on Computational Geometry.

[105]  Juan Carlos Niebles,et al.  Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words , 2008, International Journal of Computer Vision.

[106]  Srinivasan Parthasarathy,et al.  Summarizing itemset patterns using probabilistic models , 2006, KDD '06.

[107]  Lisandro Zambenedetti Granville,et al.  A solution for identifying the root cause of problems in IT change management , 2011, 12th IFIP/IEEE International Symposium on Integrated Network Management (IM 2011) and Workshops.

[108]  Maitreya Natu,et al.  Problem Identification by Mining Trouble Tickets , 2014, COMAD.

[109]  Svetha Venkatesh,et al.  Event extraction using behaviors of sentiment signals and burst structure in social media , 2013, Knowledge and Information Systems.

[110]  John Riedl,et al.  Application of Dimensionality Reduction in Recommender System - A Case Study , 2000 .

[111]  Yixin Diao,et al.  Rule-Based Problem Classification in IT Service Management , 2009, 2009 IEEE International Conference on Cloud Computing.

[112]  Xue Li,et al.  Time weight collaborative filtering , 2005, CIKM '05.

[113]  Sedigheh Abbasghorbani,et al.  Survey on sequential pattern mining algorithms , 2015, 2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI).

[114]  Hinrich Schütze,et al.  Book Reviews: Foundations of Statistical Natural Language Processing , 1999, CL.

[115]  Jiawei Han,et al.  Modeling Probabilistic Measurement Correlations for Problem Determination in Large-Scale Distributed Systems , 2009, 2009 29th IEEE International Conference on Distributed Computing Systems.

[116]  Liang Tang,et al.  Mining temporal lag from fluctuating events for correlation and root cause analysis , 2014, 10th International Conference on Network and Service Management (CNSM) and Workshop.

[117]  Joseph L. Hellerstein,et al.  Mining partially periodic event patterns with unknown periods , 2001, Proceedings 17th International Conference on Data Engineering.

[118]  Qiang Fu,et al.  Correlating events with time series for incident diagnosis , 2014, KDD.

[119]  Ren Jiadong,et al.  An algorithm based on temporary table for mining top-k closed frequent patterns in data streams , 2012 .

[120]  Mohammed J. Zaki,et al.  SPADE: An Efficient Algorithm for Mining Frequent Sequences , 2004, Machine Learning.

[121]  Avinash Achar,et al.  A unified view of the apriori-based algorithms for frequent episode discovery , 2011, Knowledge and Information Systems.

[122]  Ricardo Vilalta,et al.  Predicting rare events in temporal domains , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[123]  Abdus Salam,et al.  Mining top−k frequent patterns without minimum support threshold , 2010, Knowledge and Information Systems.

[124]  Mudasser Iqbal,et al.  Wavelet-based Burst Event Detection and Localization in Water Distribution Systems , 2013, J. Signal Process. Syst..

[125]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[126]  Alexander Aiken,et al.  Alert Detection in System Logs , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[127]  Jian Pei,et al.  Mining Access Patterns Efficiently from Web Logs , 2000, PAKDD.

[128]  Heikki Mannila,et al.  Discovering frequent episodes in sequences extended abstract , 1995, KDD 1995.

[129]  Haixun Wang,et al.  An algorithmic approach to event summarization , 2010, SIGMOD Conference.

[130]  Yixin Diao,et al.  Predicting service delivery cost for non-standard service level agreements , 2014, 2014 IEEE Network Operations and Management Symposium (NOMS).