A Bayesian approach to performance modelling for multi-tenant applications using Gaussian models

Accurately predicting response times of service queries is necessary for deployments optimisation in the multi-tenant applications system. This task is particularly challenging owing to the fact that the mixes of tenants with different business scale and operating characteristics and the interaction among the concurrently running queries have a great impact on the response time of queries in the multi-tenant applications systems, and an accurate model needs to capture them. In this paper, our goal is to build such a performance model for the interactions of multi-tenant using an experiment-driven modelling approach. We use a Bayesian approach and build novel Gaussian models that take into account a variety of factors that influence the response time of each interaction that is sent from the different tenants in the multi-tenant environments. We experimentally demonstrate that our models are accurate and effective which have an average prediction error of 12.6% in the worst case.

[1]  Min Yao,et al.  A novel hybrid model for image classification , 2011, Int. J. Comput. Sci. Eng..

[2]  Hong Shen,et al.  Self projecting time series forecast: an online stock trend forecast system , 2006, Int. J. Comput. Sci. Eng..

[3]  Frederick Chong,et al.  Multi ‐ Tenant Data Architecture June 2006 , 2016 .

[4]  Shivnath Babu,et al.  Predicting completion times of batch query workloads using interaction-aware models and simulation , 2011, EDBT/ICDT '11.

[5]  Amine Bermak,et al.  Gaussian process for nonstationary time series prediction , 2004, Comput. Stat. Data Anal..

[6]  Robert B. Gramacy,et al.  tgp: An R Package for Bayesian Nonstationary, Semiparametric Nonlinear Regression and Design by Treed Gaussian Process Models , 2007 .

[7]  Archana Ganapathi,et al.  Predicting Multiple Metrics for Queries: Better Decisions Enabled by Machine Learning , 2009, 2009 IEEE 25th International Conference on Data Engineering.

[8]  Shijun Liu,et al.  S-BM: A benchmark suite for multi-tenant supplier relationship management service , 2013, 2013 10th International Conference on Service Systems and Service Management.

[9]  Carl E. Rasmussen,et al.  Gaussian process dynamic programming , 2009, Neurocomputing.

[10]  Gregory R. Ganger,et al.  Towards self-predicting systems: What if you could ask ‘what-if’? , 2006, The Knowledge Engineering Review.

[11]  Bojan Likar,et al.  Gas-liquid separator modelling and simulation with Gaussian-process models , 2008, Simul. Model. Pract. Theory.

[12]  Ana Cortés,et al.  Varying the domain size of the dynamic load-balancing algorithm DASUD for SPMD and MPMD programming scenarios , 2004, Int. J. High Perform. Comput. Netw..

[13]  Mark Girolami,et al.  Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors , 2006, Neural Computation.

[14]  Gregory F. Cooper,et al.  A Bayesian Method for the Induction of Probabilistic Networks from Data , 1992 .

[15]  Kamesh Munagala,et al.  Interaction-aware scheduling of report-generation workloads , 2011, The VLDB Journal.

[16]  Pascal Poupart,et al.  A bayesian approach to online performance modeling for database appliances using gaussian models , 2011, ICAC '11.

[17]  Gregory R. Ganger,et al.  Towards Self-Predicting Systems: What If You Could Ask "What-If"? , 2005, 16th International Workshop on Database and Expert Systems Applications (DEXA'05).

[18]  Chetan Gupta,et al.  PQR: Predicting Query Execution Times for Autonomous Workload Management , 2008, 2008 International Conference on Autonomic Computing.

[19]  Shivnath Babu,et al.  iTuned: a tool for configuring and visualizing database parameters , 2010, SIGMOD Conference.

[20]  Qi Zhang,et al.  A Regression-Based Analytic Model for Dynamic Resource Provisioning of Multi-Tier Applications , 2007, Fourth International Conference on Autonomic Computing (ICAC'07).

[21]  Manish Marwah,et al.  Probabilistic performance modeling of virtualized resource allocation , 2010, ICAC '10.

[22]  Tor Arne Johansen,et al.  Explicit stochastic predictive control of combustion plants based on Gaussian process models , 2008, Autom..

[23]  Tim Brecht,et al.  Q-Cop: Avoiding bad query mixes to minimize client timeouts under heavy loads , 2010, 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010).

[24]  Anastasia Ailamaki,et al.  Continuous resource monitoring for self-predicting DBMS , 2005, 13th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems.