Autonomic Scaling of Cloud Computing Resources using BN-based Prediction Models

1. Autonomic Scaling of Cloud Computing Resources using BN-based Prediction Models Dr. Abul Bashar, abashar@pmu.edu.sa Assistant Professor College of Computer Engineering and Sciences Prince Mohammad Bin Fahd University Al-Khobar, KSA 31952 IEEE CLOUDNET 2013: 2nd International Conference on Cloud Networking

2. Outline • Introduction & Motivation • Related Work • Proposed Approach • Implementation Details • Results and Discussion • Future Work and Conclusion CLOUDNET 2013, 13th November 2013

3. Motivation : Scalability of Cloud Computing • Cloud Computing’s popularity: Quality service provisioning • Benefits: Reduced CAPEX and OPEX, Pay-as-you-go IT service, “Unlimited” computing resources • Challenges: Dynamic Provisioning, Resource usage optimization, Ensuring QoS/SLA • Motivation: Develop autonomic resource scaling solution • Machine Learning: Autonomic, Scalable and Predictive solutions • Our contribution: Bayesian Networks-based Scalability Control CLOUDNET 2013, 13th November 2013

4. Related Work and Research Objectives Existing ML-based Datacenter Management Systems • ARIMA models for resource prediction of cloud applications • String Matching Algorithms for cloud resource forecasting • Discrete Time Markov Chains for long-term demand predictions • Time Delay Neural Networks for predicting future workloads • Bayesian Networks (BN) for detecting failures in a cloud datacenter Observations • Numerous ML-based solutions exist for predictive resource scaling • BN has not been used for prediction of resource demands Our proposed objectives • To study prominent ML-based Cloud Computing Management methodologies • To model and implement BN based Decision Support System • To assess the performance of BNSC for predictive/diagnostic reasoning and decision-making CLOUDNET 2013, 13th November 2013

5. Bayesian Network Representation • BN is a probabilistic graphical model, a mapping of physical system variables into a visual and intuitive model • Directed Acylic Graph structure : using nodes and arcs • Encodes conditional independence relation among system random variables • Defined mathematically using joint probability distribution formulation • Inference feature : Repeated use of Baye’s rule to estimate unobserved nodes based on evidence of observed nodes CLOUDNET 2013, 13th November 2013

6. Conceptual Framework : BN-based DSS • DMS (Datacenter Management System): Monitors and provides monitoring data • DSS (Decision Support System): Uses DMS data and builds predictive models • Structural Learning: PC & NPC algorithms • Parameter Learning: EM Algorithm • Validation procedure: k-fold cross validation • Inference & Prediction: repeated Baye’s rule / classifier function CLOUDNET 2013, 13th November 2013

7. Experimental Setup Details Characteristics of Workload Demands BN Nodes Definition Cloud Datacenter Simulation in OPNET CLOUDNET 2013, 13th November 2013

8. Simulation Results : BN Model BN model provides the structural relationships among the nodes Cause and effect nodes : parent child relationships Marginal probabilities of all the nodes (shown in the monitor windows) Useful in scenarios when there are numerous variables CLOUDNET 2013, 13th November 2013

9. Simulation Results : CPTs Conditional Probability Tables EM Algorithm for learning parameters (CPTs) Strength of relationships between the nodes CLOUDNET 2013, 13th November 2013

10. Simulation Results : Influence Diagram BNSC (Bayesian Networks-based Scalability Control) Utility node named Reward is added along with an action node named Scalability_Control Reward node values depend on the states of Response_Time node for making scaling decisions Decisions of Scale_Up or Scale_Down are the actions of Scalability_Controlnode Utility Table for Reward Node of BNSC CLOUDNET 2013, 13th November 2013

11. Simulation Results : Predictive Reasoning Sample decision to Scale_Up when Workload_Demand is High The system is under the influence of heavy workload demand BNSC rightly decides to scale up the resources with a reward of +88.9 CLOUDNET 2013, 13th November 2013

12. Simulation Results : Diagnostic Reasoning Sample decision to find the probable cause of LowResponse_Time BNSC is now scaling down the resources with a reward of +100.0 Diagnoses the reason for low response time (Workload_Demand is Low with probability of 0.996) CLOUDNET 2013, 13th November 2013

13. Summary & Conclusions • Offline modeling of BNSC is achievable and practically implementable • Scaling decisions were found to be coherent and plausible • BNSC solution demonstrated successful predictive and diagnostic decision making for scaling up/down of Cloud Computing resources • Novelty of BNSC solution is to provide autonomic decision making • Future work involves incorporating more performance metrics in the BNSC model for more realistic resource scaling decisions • Another aspect worth researching is to model multiple distributed datacenter performance behavior • To make BNSC a comprehensive online learning and decision support system CLOUDNET 2013, 13th November 2013

14. Acknowledgement The author would like to acknowledge the support of Prince Mohammad Bin Fahd University, KSA for performing this research work. CLOUDNET 2013, 13th November 2013

15. THANK YOU CLOUDNET 2013, 13th November 2013