INTRODUCTION TO AUTONOMIC PHARMACOLOGY: Part V Actions of autonomic nerves:
Autonomic Scaling of Cloud Computing Resources using BN-based Prediction Models
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Transcript of Autonomic Scaling of Cloud Computing Resources using BN-based Prediction Models
Autonomic Scaling of Cloud Computing Resourcesusing BN-based Prediction Models
Dr. Abul Bashar, [email protected]
Assistant ProfessorCollege of Computer Engineering and Sciences
Prince Mohammad Bin Fahd University Al-Khobar, KSA 31952
IEEE CLOUDNET 2013: 2nd International Conference on Cloud Networking
Outline
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Introduction & Motivation Related Work Proposed Approach Implementation Details Results and Discussion Future Work and Conclusion
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
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
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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 Baye’s rule to estimate unobserved
nodes based on evidence of observed nodes
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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 Baye’s rule / classifier functionCLOUDNET 2013, 13th November 2013
Experimental Setup Details
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Cloud Datacenter Simulation in OPNET
Characteristics of Workload Demands
BN Nodes Definition
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 variablesCLOUDNET 2013, 13th November 2013
Simulation Results : CPTs
EM Algorithm for learning
parameters (CPTs)
Strength of relationships between
the nodes
Conditional Probability Tables
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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
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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
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
+88.9CLOUDNET 2013, 13th November 2013
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)
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Summary & Conclusions
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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
Acknowledgement
The author would like to acknowledge the support of Prince Mohammad Bin Fahd University, KSA for performing this research work.
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THANK YOU
CLOUDNET 2013, 13th November 2013