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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: 2 nd International Conference on Cloud Networking

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

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Motivation : Scalability of Cloud Computing Cloud Computings 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, 13 th November 2013

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Related Work and Research Objectives 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 Existing ML-based Datacenter Management Systems 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 Observations Numerous ML-based solutions exist for predictive resource scaling BN has not been used for prediction of resource demands CLOUDNET 2013, 13 th November 2013

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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 Bayes rule to estimate unobserved nodes based on evidence of observed nodes CLOUDNET 2013, 13 th November 2013

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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 Bayes rule / classifier function CLOUDNET 2013, 13 th November 2013

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

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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, 13 th November 2013

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

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Simulation Results : Influence Diagram 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 CLOUDNET 2013, 13 th November 2013 BNSC (Bayesian Networks-based Scalability Control) Utility Table for Reward Node of BNSC Decisions of Scale_Up or Scale_Down are the actions of Scalability_Control node

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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, 13 th November 2013

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Simulation Results : Diagnostic Reasoning Sample decision to find the probable cause of Low Response_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, 13 th November 2013

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Summary & Conclusions CLOUDNET 2013, 13 th November 2013 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

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

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THANK YOU CLOUDNET 2013, 13 th November 2013

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