Workload Classification for Efficient Auto-Scaling of Cloud Resources

Elasticity algorithms for cloud infrastructures dynamically change the amount of resources allocated to a running service according to the current and predicted future load. Since there is no perfe ...

[1]  Raouf Boutaba,et al.  Characterizing Task Usage Shapes in Google Compute Clusters , 2011 .

[2]  Eyke Hüllermeier,et al.  Online clustering of parallel data streams , 2006, Data Knowl. Eng..

[3]  Michael I. Jordan,et al.  Automating Datacenter Operations Using Machine Learning , 2010 .

[4]  Sudipto Guha,et al.  Clustering Data Streams: Theory and Practice , 2003, IEEE Trans. Knowl. Data Eng..

[5]  Kevin Lee,et al.  Empirical prediction models for adaptive resource provisioning in the cloud , 2012, Future Gener. Comput. Syst..

[6]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[7]  Randy H. Katz,et al.  Heterogeneity and dynamicity of clouds at scale: Google trace analysis , 2012, SoCC '12.

[8]  Prashant J. Shenoy,et al.  Agile dynamic provisioning of multi-tier Internet applications , 2008, TAAS.

[9]  Jeffrey S. Chase,et al.  Automated control for elastic storage , 2010, ICAC '10.

[10]  Allen B. Downey,et al.  The elusive goal of workload characterization , 1999, PERV.

[11]  Dick H. J. Epema,et al.  A Realistic Integrated Model of Parallel System Workloads , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[12]  Travis E. Oliphant,et al.  Python for Scientific Computing , 2007, Computing in Science & Engineering.

[13]  Johan Tordsson,et al.  Efficient provisioning of bursty scientific workloads on the cloud using adaptive elasticity control , 2012, ScienceCloud '12.

[14]  Benoit Hudzia,et al.  Future Generation Computer Systems Optimis: a Holistic Approach to Cloud Service Provisioning , 2022 .

[15]  Naveen Sharma,et al.  Towards autonomic workload provisioning for enterprise Grids and clouds , 2009, 2009 10th IEEE/ACM International Conference on Grid Computing.

[16]  A. Rubin Statistics for Evidence-Based Practice and Evaluation , 2006 .

[17]  Riccardo Gusella,et al.  Characterizing the Variability of Arrival Processes with Indexes of Dispersion , 1991, IEEE J. Sel. Areas Commun..

[18]  Chita R. Das,et al.  Towards characterizing cloud backend workloads: insights from Google compute clusters , 2010, PERV.

[19]  João Gama,et al.  Hierarchical Clustering of Time-Series Data Streams , 2008, IEEE Transactions on Knowledge and Data Engineering.

[20]  J. G. Bryan,et al.  STATISTICAL METHODS IN FORECASTING , 1962 .

[21]  T. Hughes,et al.  Signals and systems , 2006, Genome Biology.

[22]  Guillaume Pierre,et al.  Wikipedia workload analysis for decentralized hosting , 2009, Comput. Networks.

[23]  J. Richman,et al.  Sample entropy. , 2004, Methods in enzymology.

[24]  Archana Ganapathi,et al.  Towards Understanding Cloud Performance Tradeoffs Using Statistical Workload Analysis and Replay , 2010 .

[25]  T. Kudoh,et al.  Realtime Burstiness Measurement , 2006 .

[26]  Anand Sivasubramaniam,et al.  Managing server energy and operational costs in hosting centers , 2005, SIGMETRICS '05.

[27]  Waheed Iqbal,et al.  Adaptive resource provisioning for read intensive multi-tier applications in the cloud , 2011, Future Gener. Comput. Syst..

[28]  Eyal de Lara,et al.  SnowFlock: rapid virtual machine cloning for cloud computing , 2009, EuroSys '09.

[29]  David Barber,et al.  Bayesian reasoning and machine learning , 2012 .

[30]  Haifeng Chen,et al.  Understanding internet video sharing site workload: a view from data center design , 2008, WWW.

[31]  Balaji Viswanathan,et al.  CloudMap: Workload-aware placement in private heterogeneous clouds , 2012, 2012 IEEE Network Operations and Management Symposium.

[32]  Alexandru Iosup,et al.  The Grid Workloads Archive , 2008, Future Gener. Comput. Syst..

[33]  Shicong Meng,et al.  Tide: achieving self-scaling in virtualized datacenter management middleware , 2010, Middleware Industrial Track '10.

[34]  D. Ruppert The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[35]  M. Morari Robust stability of systems with integral control , 1983, The 22nd IEEE Conference on Decision and Control.

[36]  Michael I. Jordan,et al.  Characterizing, modeling, and generating workload spikes for stateful services , 2010, SoCC '10.

[37]  Johan Tordsson,et al.  Runtime Virtual Machine Recontextualization for Clouds , 2012, Euro-Par Workshops.

[38]  Jason Weston,et al.  Breaking SVM Complexity with Cross-Training , 2004, NIPS.

[39]  Archana Ganapathi,et al.  Analysis and Lessons from a Publicly Available Google Cluster Trace , 2010 .

[40]  Johan Tordsson,et al.  An adaptive hybrid elasticity controller for cloud infrastructures , 2012, 2012 IEEE Network Operations and Management Symposium.

[41]  Chung-Kang Peng,et al.  Multiscale Analysis of Heart Rate Dynamics: Entropy and Time Irreversibility Measures , 2008, Cardiovascular engineering.

[42]  D. Cuesta-Frau,et al.  Characterization of Sample Entropy in the Context of Biomedical Signal Analysis , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[43]  H. Lilliefors On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown , 1967 .

[44]  Samuel Kounev,et al.  Self-adaptive workload classification and forecasting for proactive resource provisioning , 2013, ICPE '13.

[45]  Martin Arlitt,et al.  A workload characterization study of the 1998 World Cup Web site , 2000, IEEE Netw..

[46]  Eamonn J. Keogh,et al.  On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration , 2002, Data Mining and Knowledge Discovery.

[47]  Evanghelos Zafiriou,et al.  Robust process control , 1987 .

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

[49]  Philip S. Yu,et al.  On demand classification of data streams , 2004, KDD.

[50]  Giuseppe Serazzi,et al.  Workload characterization: a survey , 1993, Proc. IEEE.

[51]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[52]  Ajay Mohindra,et al.  Dynamic Scaling of Web Applications in a Virtualized Cloud Computing Environment , 2009, 2009 IEEE International Conference on e-Business Engineering.

[53]  Prashant J. Shenoy,et al.  Autonomic mix-aware provisioning for non-stationary data center workloads , 2010, ICAC '10.

[54]  Alexandru Iosup,et al.  Grid Computing Workloads , 2011, IEEE Internet Computing.