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Economical and Robust Provisioning of N-Tier Cloud

Workloads: A Multi-level Control Approach

Pengcheng Xiong1, Zhikui Wang2, Simon Malkowski1, Qingyang Wang1, Deepal

Jayasinghe1, Calton Pu1

1Georgia Institute of Technology2HP Labs

Email: xiong@gatech.edu

Overview

MotivationBackgroundResource partition

controllerApplication controllerConclusions

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Overview

MotivationBackgroundResource partition

controllerApplication controllerConclusions

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Applications in a typical Cloud environment

Different feedback controller design for a single/multi-tiered application (1)

5Zhu et al, ACC 2006

Different feedback controller design for a single/multi-tiered application (2)

6Wang et al, FeBID 2007

C3

C2

E1

MRT (Mean Response Time)

+

RTref

N (Transaction Mix)

Feedback Controller

Client Feed-Forward Controller

Umdl

U U

-

Utilization Controller 1

+

WWW Server

App. Server

DB server

RT

AutoParam

Utilization Controller 2

Utilization Controller 3

E2

E3

C1

U1 ref

U2 ref

U3 ref

TUC

TFB

TFF

Different controllability under different workload generator (1)

7Schroeder et al, NSDI 2006

Different controllability under different workload generator (2)

8Xiong et al, NOMS 2010

Goals Economical

– We want to meet the performance requirement for the N-tier web application with the minimum total resources.

Robust– We want to be robust to different

time-varying workload types, e.g., open, closed, semi-open.

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Overview

MotivationBackgroundResource partition

controllerApplication controllerConclusions

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Control Architecture

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Test bed

Experiment Environment– Apache, Tomcat, Mysql– Xen hypervisor

Workload Generator– RUBiS “Browsing mix” workload that has 10

transaction types, e.g., Home, Browse, ViewItem. (just like eBay)

– Workload types (open, closed, semi-open)– Workload intensity

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Overview

MotivationBackgroundResource partition

controllerApplication controllerConclusions

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System modeling

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Optimal resource partition

Solution 1(Shares)

Solution 2(Util.)

Our solution(Opt.)

Evaluation of resource partition controller

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Overview

MotivationBackgroundResource partition

controllerApplication controllerConclusions

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Application controller design

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System model between the RTT and S– System identification method based on ARMA

model

Controller design– Root-locus method based on control theory

System identification

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Controller design

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ARX01 model

Proportional-integral (PI) controller

The closed model transfer function

Performance controller(setting=35ms)

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Util has MORE fluctuation than Opt.

Performance controller(setting=200ms)

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Conclusions

We propose economical and robust provisioning for Cloud resources for N-tier web applications through a multi-level control approach.

Experimental results show that our

solution outperforms other existing approaches– Almost the same performance but save up to

20% CPU resources. – Robust to deal with different workload styles.

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Thanks