Measuring Cloud Workload Burstiness

Workload burstiness and spikes are among the main reasons for service disruptions and decrease in the Quality-of-Service (QoS) of online services. They are hurdles that complicate autonomic resource management of datacenters. In this paper, we review the state-of-the-art in online identification of workload spikes and quantifying burstiness. The applicability of some of the proposed techniques is examined for Cloud systems where various workloads are co-hosted on the same platform. We discuss Sample Entropy (Samp En), a measure used in biomedical signal analysis, as a potential measure for burstiness. A modification to the original measure is introduced to make it more suitable for Cloud workloads.

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

[2]  Alma Riska,et al.  Disk Drive Level Workload Characterization , 2006, USENIX Annual Technical Conference, General Track.

[3]  Evgenia Smirni,et al.  Fastrack for taming burstiness and saving power in multi-tiered systems , 2010, 2010 22nd International Teletraffic Congress (lTC 22).

[4]  Walter Willinger,et al.  On the Self-Similar Nature of Ethernet Traffic ( extended version ) , 1995 .

[5]  Sheng-Fu Liang,et al.  Fast computation of sample entropy and approximate entropy in biomedicine , 2011, Comput. Methods Programs Biomed..

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

[7]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[9]  Calton Pu,et al.  When average is not average: large response time fluctuations in n-tier systems , 2012, ICAC '12.

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

[11]  Patrick Wendell,et al.  Going viral: flash crowds in an open CDN , 2011, IMC '11.

[12]  Xiaohui Gu,et al.  CloudScale: elastic resource scaling for multi-tenant cloud systems , 2011, SoCC.

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

[14]  Xinnian Chen,et al.  Comparison of the Use of Approximate Entropy and Sample Entropy: Applications to Neural Respiratory Signal , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

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

[16]  Bo Hong,et al.  Managing flash crowds on the Internet , 2003, 11th IEEE/ACM International Symposium on Modeling, Analysis and Simulation of Computer Telecommunications Systems, 2003. MASCOTS 2003..

[17]  R. Hilgers,et al.  Parameter , 2019, Springer Reference Medizin.

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

[19]  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.

[20]  Johan Tordsson,et al.  The Challenge of Cloud Control , 2013, Feedback Computing.

[21]  Nick Koudas,et al.  TwitterMonitor: trend detection over the twitter stream , 2010, SIGMOD Conference.

[22]  Walter Willinger,et al.  On the self-similar nature of Ethernet traffic , 1993, SIGCOMM '93.

[23]  M. P. Griffin,et al.  Sample entropy analysis of neonatal heart rate variability. , 2002, American journal of physiology. Regulatory, integrative and comparative physiology.

[24]  Johan Tordsson,et al.  Workload Classification for Efficient Auto-Scaling of Cloud Resources , 2013 .

[25]  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.

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

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

[28]  Albert-László Barabási,et al.  Universal features of correlated bursty behaviour , 2011, Scientific Reports.

[29]  Johan Tordsson,et al.  How will Your Workload Look Like in 6 Years? Analyzing Wikimedia's Workload , 2014, 2014 IEEE International Conference on Cloud Engineering.

[30]  Vanish Talwar,et al.  Online detection of utility cloud anomalies using metric distributions , 2010, 2010 IEEE Network Operations and Management Symposium - NOMS 2010.

[31]  Dimitrios Gunopulos,et al.  Identifying similarities, periodicities and bursts for online search queries , 2004, SIGMOD '04.

[32]  Virgílio A. F. Almeida,et al.  In search of invariants for e-business workloads , 2000, EC '00.

[33]  T. Creighton.  Methods in Enzymology , 1968, The Yale Journal of Biology and Medicine.

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

[35]  M. Taqqu,et al.  Estimating long-range dependence in the presence of periodicity: An empirical study , 1999 .

[36]  Christos Faloutsos,et al.  Capturing the spatio-temporal behavior of real traffic data , 2002, Perform. Evaluation.