autonomic scaling of cloud computing resources using bn-based prediction models dr. abul bashar,...

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

CLOUDNET 2013, 13th November 2013

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


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


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


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


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


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)


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

Acknowledgement

The author would like to acknowledge the support of Prince Mohammad Bin Fahd University, KSA for performing this research work.


THANK YOU


autonomic scaling of cloud computing resources using bn-based prediction models dr. abul bashar,...

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