Workload modeling for resource usage analysis and simulation in cloud computing

We model a web application to support analysis and simulation of cloud environments.We implement the model as an extension of the CloudSim simulator.We found that the user behavior has a strong influence on the resource utilization.We found Generalized Extreme Value and Generalized Lambda distributions instead of Exponential distribution to represent session time. Display Omitted Workload modeling enables performance analysis and simulation of cloud resource management policies, which allows cloud providers to improve their systems' Quality of Service (QoS) and researchers to evaluate new policies without deploying expensive large scale environments. However, workload modeling is challenging in the context of cloud computing due to the virtualization layer overhead, insufficient tracelogs available for analysis, and complex workloads. These factors contribute to a lack of methodologies and models to characterize applications hosted in the cloud. To tackle the above issues, we propose a web application model to capture the behavioral patterns of different user profiles and to support analysis and simulation of resources utilization in cloud environments. A model validation was performed using graphic and statistical hypothesis methods. An implementation of our model is provided as an extension of the CloudSim simulator.

[1]  Rajkumar Buyya,et al.  Performance Modelling and Simulation of Three-Tier Applications in Cloud and Multi-Cloud Environments , 2015, Comput. J..

[2]  Jasjeet S. Sekhon,et al.  Genetic Optimization Using Derivatives , 2011, Political Analysis.

[3]  D. Dickey,et al.  Testing for unit roots in autoregressive-moving average models of unknown order , 1984 .

[4]  tcpdump Tcpdump/Libpcap public repository , 2010 .

[5]  Russell C. H. Cheng,et al.  Estimating Parameters in Continuous Univariate Distributions with a Shifted Origin , 1983 .

[6]  Daniel A. Menascé,et al.  TPC-W: A Benchmark for E-Commerce , 2002, IEEE Internet Comput..

[7]  Diwakar Krishnamurthy,et al.  Web workload generation challenges – an empirical investigation , 2012, Softw. Pract. Exp..

[8]  P. Phillips,et al.  Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? , 1992 .

[9]  Jean-Marc Vincent,et al.  A Self-Scalable and Auto-Regulated Request Injection Benchmarking Tool for Automatic Saturation Detection , 2014, IEEE Transactions on Cloud Computing.

[10]  Hoang Pham,et al.  On Recent Generalizations of the Weibull Distribution , 2007, IEEE Transactions on Reliability.

[11]  Virgílio A. F. Almeida,et al.  A hierarchical and multiscale approach to analyze E-business workloads , 2003, Perform. Evaluation.

[12]  ArlittMartin,et al.  Web workload generation challenges - an empirical investigation , 2012 .

[13]  Yohan Chalabi,et al.  New directions in statistical distributions, parametric modeling and portfolio selection , 2012 .

[14]  Peter Dalgaard,et al.  R Development Core Team (2010): R: A language and environment for statistical computing , 2010 .

[15]  Wayne Luk,et al.  A Comment on the Implementation of the Ziggurat Method , 2005 .

[16]  Jasjeet S. Sekhon,et al.  Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching Package for R , 2008 .

[17]  Vern Paxson,et al.  Bro: a system for detecting network intruders in real-time , 1998, Comput. Networks.

[18]  Dror G. Feitelson,et al.  Workload Modeling for Computer Systems Performance Evaluation , 2015 .

[19]  Constantinos Dovrolis,et al.  Measuring the Congestion Responsiveness of Internet Traffic , 2007, PAM.

[20]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[21]  Archana Ganapathi,et al.  Statistics-driven workload modeling for the Cloud , 2010, 2010 IEEE 26th International Conference on Data Engineering Workshops (ICDEW 2010).

[22]  Behrouz Shahgholi Ghahfarokhi,et al.  Context-aware multi-objective resource allocation in mobile cloud , 2015, Comput. Electr. Eng..

[23]  J. R. Wallis,et al.  Probability Weighted Moments: Definition and Relation to Parameters of Several Distributions Expressable in Inverse Form , 1979 .

[24]  Adam Gold,et al.  Understanding the Mann-Whitney test , 2007 .

[25]  Angelo M. Mineo,et al.  A Software Tool for the Exponential Power Distribution: The normalp Package , 2005 .

[26]  A. Jenkinson The frequency distribution of the annual maximum (or minimum) values of meteorological elements , 1955 .

[27]  Hui Li Realistic Workload Modeling and Its Performance Impacts in Large-Scale eScience Grids , 2010, IEEE Transactions on Parallel and Distributed Systems.

[28]  Jie Xu,et al.  An Approach for Characterizing Workloads in Google Cloud to Derive Realistic Resource Utilization Models , 2013, 2013 IEEE Seventh International Symposium on Service-Oriented System Engineering.

[29]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[30]  John M. Acken,et al.  Cloud Workload Characterization , 2013 .

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

[32]  Rajeev Gandhi,et al.  An Analysis of Traces from a Production MapReduce Cluster , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[33]  F. Mosteller,et al.  Low Moments for Small Samples: A Comparative Study of Order Statistics , 1947 .