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Intelligent Management of Virtualized Resources for

Database Systems in Cloud Environment

Pengcheng Xiong (Georgia Tech); Yun Chi (NEC Labs America); Shenghuo Zhu (NEC Labs America); Hyun

Moon (NEC Labs America); Calton Pu (Georgia Tech); Hakan Hacigumus (NEC Labs America)

Overview

Motivation Background System modeling Resource Allocation Conclusions

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Overview

Motivation Background System modeling Resource Allocation Conclusions

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Hosting database management systems

Common practice: One-to-one mapping of DBMSs to nodes

Problem: Poor utilization

(1)Both nodes are utilized under 10% most of the time. (2) The maximum CPU usage is much higher than the average CPU usage.

Pradeep Padala et al, Adaptive control of virtualized resources in utility computing environments(EuroSys '07)

Solution: consolidation based on virtualization

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Solution: consolidation based on virtualization

3 CPUs

2 CPUs

4 is enough!

Problem: Provisioning under variable workload

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Solution: Adaptive provisioning under variable

workload

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GoalsGood performance

Maintain SLAService differentiation

Good resource utilization

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Overview

Motivation Background System modeling Resource Allocation Conclusions

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Background

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Virtualized Server I Virtualized Server II

VM 1

VM 2

VM 3

VM 4

MySQLMaster

MySQLSlave

MySQLMaster

MySQLSlave

Gold Client

Silver Client

Gold Client

Silver Client

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What are we controlling ?

Inner level controller

GoalsSLA penalty cost =>Good

performanceInfrastructure cost=>Good

utilizationAction cost+)------------------------------------Total costMinimized? NO

Set CPU/Memory

shares

Outer level controller

Set number of

replicas

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Related work Existing research

Resource allocation & scheduling Service differentiation

Our contribution: Non-linear model of DBMS behavior Two level controller

High resource utilization Low query response time, less SLA penalty cost,

Service differentiation Action cost

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System overview

Overview

Motivation Background System modeling Resource Allocation Conclusions

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System modeling How is the average SLA penalty

cost correlated with the system configuration? Statistic analysis, draw marginal

distribution How can we accurately predict

the average SLA penalty cost? Machine learning techniques,

linear and non-linear models17

System modeling: statistical analysis

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Looks like straight line Non-straight line

System modeling: machine learning

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Overview

Motivation Background System modeling Resource Allocation Conclusions

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Resource allocation: two level controller

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Find the next direction which can minimize the total costPerformance cost (related to SLA)Infrastructure cost (related to replicas)Action cost

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Evaluation Experiment Environment

MySQL v5.0, MySQL replication Xen hypervisor

Workload Generator Dynamic arrival rate follows Poisson

distribution TPC-W Ordering (the browsing requests

and the ordering requests are 50%, respectively.)

TPC-W 100 EBs, 10K items. The whole database size is about 280MB.

Baseline

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The total SLA penalty cost is 2802

Inner Level Controller

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The total SLA penalty cost is 2363

Inner Level Controller Total SLA penalty cost under different

number of replicas

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Outer level controller without action cost

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<-EC2, Small instance, $0.085 per hour

Action cost modeling

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Amortization factor Factor=0:the action cost will

be distributed along the infinity intervals. (optimistic)

Factor=1:the action cost can be compensated in the next interval. (pessimistic)

Factor between (0,1):the action cost can be compensated in several intervals.

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Outer level controller with action cost

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Conclusion Virtual resource management for

database management systems in Cloud computing Good performance

Maintain SLA Service differentiation

Good utilization SmartSLA

Non-linear model of DBMS behavior Two level controller which takes in to

consideration of SLA penalty cost, infrastructure cost and also action cost.

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Thank you!

ELBA project page: http://www.cc.gatech.edu/systems/projects/Elba/index.html

E-mail: xiong@gatech.edu