SLA Decomposition: Translating Service Level Objectivesto System Level Thresholds

Yuan Chen, Subu Iyer, Xue Liu, Dejan Milojicic, Akhil Sahai

Enterprise Systems and Software LabHewlett Packard Labs

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Introduction• Service Level Agreements (SLAs)

− service behavior guarantees, e.g. performance, availability, reliability, security

− penalties in case the guarantees are violated

• The ability to deliver according to pre-defined SLAs agreements is a key to success

• SLAs management− capturing the guarantees between a service provider and a

customer− meeting these service level agreements, by designing

systems/services accordingly− monitoring for violations of agreed SLAs − enforcing SLAs in case of violations

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SLA ManagementSLA Specification

Physical resources

Virtual resources

Clients

Applications

SLA Negotiation

SLA MonitoringSLA Enforcement

Design

4

Design• Services/systems need to be designed to meet the agreed

SLAs − to ensure that the system/service behaves satisfactorily before

putting it in production

• Enterprise systems and services are comprised of multiple sub-components − each sub system or component potentially affects the overall

behavior− any high level goals specified for a service in SLA potentially

relates to low level system components

• Traditional designs usually involve domain experts − manual and ad-hoc − costly, time-consuming, and often inflexible

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• Virtualized data center− on demanding computing− application share resources using virtual

technologies

• Scenario− a 3-tier (Apache-Tomcat-Mysql) application

− SLO: average response time < 10 secs− determine the percentage of CPU assigned to

each VMs to meet the SLO with reasonable CPU utilization

Scenario1

0

5

10

15

20

20 50 90 Percentage of CPU Assigned to Tomcat

Ave

rag

e R

esp

on

se T

ime

(sec

.)

SLO

Motivational Scenario

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Problem Statement

SLA Decomposition: given high level Service Level Objectives (SLOs), translate the SLOs to low level system thresholds

The system thresholds are used to created an effective design to meet the SLOs

− determine resource allocation for each individual component − determine software configuration − SLOs monitoring and assessment

SLA Decomposition

Service Level Objectives (SLOs)

response time throughput workload

system metrics

applicationattributes

healthy ranges

low level system thresholds

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Challenges• The decomposition problem requires domain experts to be involved,

which makes the process manual, complex, costly and time consuming

• Complex and dynamic behavior of multi-component applications− components interact with each other in a complex manner

− multi-thread/multi-server, various configurations, cache & optimization

− various workload

− different software architectures, e.g., 2- vs 3-tier, 3-tier Servlet vs 3-tier EJB

− different kinds of software components and performance behaviors, e.g., Microsoft IIS, Apache, JBoss, WebLogic, WebSphere; Oracle, MySQL Microsoft SQL server

• Impact of virtualization and application sharing, e.g., Xen, VMware− granular allocation of resources

− environments are dynamic

• Different kinds of SLOs, e.g., performance, availability, security, …

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Goal

Develop a SLA decomposition approach for multi-component applications, which translates high level SLOs to the state of each component involved in providing the service

− Effective: ensures that the overall SLO goals are met reasonably well

− Automated: eliminates the involvement of domain experts− Extensible: applicable to commonly used multi-component

applications − Flexible: easily adapts to changes in SLOs, application

topology, software configuration and infrastructure

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Outline

• Problem Statement and Challenges• Our Approach

− Overview− Analytical Model for Multi-tier Applications− Component Profile Creation− SLA Decomposition

• Validation• Related Work• Summary and Future Work

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Our Approach• Combine performance model and component characterization to create decomposition model

− model the behavior of the service

− characterize the behavior of each component

− combine them to create decomposition model

• Given a service instance and SLOs, use the decomposition model to derive low level thresholds

• Create an effective design of the service to meet the SLOs based on low level thresholds

PerformanceModeling

PerformanceModeling

Component Profiling & Regression Analysis

Component Profiling & Regression Analysis

DecompositionDecomposition

SLOs

low level system thresholds

Resource Allocation

Configuration SLA monitoring Assessment

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SLA Decomposition

• Decomposition− given SLOs, R < r, X > x, find the set of cpu, mem, … n_clients, n_threads,

s_cache

g1(f1(cpuhttp,memhttp,n_clients),f2(cpuapp,memapp,n_threads),f3(cpudb,memdb,s_cache)) < r

− objective function, e.g. minimize (cpuhttp+ cpuapp+ cpudb)

Profiling& Regression

Analysis

Applications

Component Profiles

µhttp = f1 (cpuhttp,memhttp, n_clients,) µapp = f2 (cpuapp,memapp,n_threads) µdb = f3(cpudb,memdb,s_cache)

Performance Modeling

Performance Model

R = g1 (µhttp, µapp, µdb, w)X = g2 (µhttp, µapp, µdb, w)

high level goalsR < r, X > x

system thresholds cpuhttp > υ1 memcpu > m1cpuapp > υ2, memapp > m2cpudb > υ2 memdb > m3

workload characteristics w

Resource Allocation

configuration parametersn_clients = c,n_threads = ns_cache = s

Configuration

component performanceRhttp < r1Rapp< r2Rdb < r3

SLA monitoring Assessment

2

)

)

http1 1 http

app2 app

db3 db

http1 http

app2 app

db3 db

R = g (f (cpu ,mem ,n_clients),

f (cpu ,mem ,n_threads),

f (cpu ,mem ,s_cache,w)

X = g (f (cpu ,mem ,n_clients),

f (cpu ,mem ,n_threads),

f (cpu ,mem ,s_cache,w)

Decomposition

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Outline

• Problem Statement and Challenges• Our Approach

− Overview− Analytical Model for Multi-tier Applications− Component Profile Creation− SLA Decomposition

• Validation• Related Work• Summary and Future Work

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Modeling Multi-Tier Applications

• Multi-tier architecture

• Closed multi-station queuing network− general multi-station queue G/G/K representing each tier and the

underlying server − arbitrary service time distribution and visit rate to each tier− capture multi-thread/multi-server structure and concurrency− handle realistic user session based interactions

− Si: mean service time− Vi: visit rate− Ki: number of stations− N: number of users− Z: think time

…

…

Users

N , Z, V0

Q1

K1 , S1 , V1

…

…

… …

…

Q2 QM

K2 , S2 , V2 KM , SM , VM

……

User

T1 T2 TM

. . ....

.

.

....

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Approximate Model for Mean Value Analysis (MVA)• Analytical performance model

− (M, N, Z, S1,V1, KI , … SM,VM, KM) R, X

• A queue with m stations and service demand D at each station is replaced with two tandem queues− a single-station queue with service demand D/m− a pure delay center, with delay D×(m-1)/m

Users

N , Z, V0

Q1

V1, D1, DD1

Q2QM

……

…

V2, D2, DD2 VM, DM, DDM

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Deriving Queuing Network Performance

• (M, N, Z, Ki, Si, Vi) R, X• Di = Si * (Vi / V0)• Mean Value Analysis (MVA)

• Input− N: number of users− Z: think time− M: number of tiers− Ki: number of stations at tier i (i = 1,…, M)− S: mean service time at tier i (i = 1,…, M)− Vi: mean request rate of tier i (i = 1,…, M)

• Output− R: average response time− X: throughput − Ri: response time of tier i (i = 1,…, M)− Qi: queue length of tier i (i = 1,…, M)

• Complexity O(MN)

delay resource( )

(1 ( 1) queueing resourcek

kk k

DR i

D Q i

1

( )( )

K

kk

iX i

R i

( ) ( )k kQ i X R i

Input: N, Z, M, Ki, Si, Vi (i = 1,.. M) Output: R, X //initialization R0 = Z; D0 = Z; Q0 = 0; for i = 1 to M { // Tandem approximations for each tier Qi = 0; Di = Si ( Vi / V0); qrDi = Di/Ki ; drDi = Di ( Ki-1)/Ki ; } //introduce N users one by one for i = 1 to N {

for j = 1 to M { Rj = qrDj (1 + Qj); // queueing resource RRj = drDj; //delay resource

} for j = 1 to M Qj = X Rj;

X =

)(1

0

m

j

ii RRRR

i

}

R = )(1

M

i

ii RRR

Modified MVA algorithm for multi-station queue

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Component Profiling• Capture component performance characteristics

− S= f(CPU, MEM,,nConnections, CacheSize …)

− independent of other components

• Profiling− deploy the application on a testbed − change the resource allocation and configurations of each component− while profiling a component, configure other component at its maximum

capacity− apply certain workload and collect the performance and workload data− apply statistical analysis to derive the correlation between a

component’s performance and its resource assignments and configuration

− archive the result as the component’s profile

• Capture workload characteristics e.g., visit rate, think time• Challenges

− measurement methodology: accurate, practical, general− non-intrusive approach− appropriate statistical analysis techniques, e.g., regression analysis

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Decomposition• Performance model of M-tier applications

• Profiles for each tier/component − Si = fi (CPUi, MEMi, nConnectionsi) i =1,…, M

• Workload characteristics− visit rate Vi , number of stations Ki ; think time Z

• Decomposition

− (M, Ki, Vi, Z, N, R, X) (CPU1, MEM1, nConnection1…. CPUM, MEMM,

nConnectionM)

− given a M-tier application with the SLOs of R < r, X > x, N users, find the set

of CPUi, MEMi, nConnectionsi satisfying

− e.g., optimization problem with objective function

1 m i 1 1, 1 M M M M 1 MR = g(N, Z, K , ...K , f (CPU , MEM nConnectons ), ..., f (CPU , MEM , nConnections ), V , ... V )

X = N / (R+ Z)

( , , , , , ), /( )i i iR g N Z M K S V X N R Z

1 m i 1 1, 1 M M M M 1 Mg(N, Z, K , ...K , f (CPU , MEM nConnectons ), ..., f (CPU , MEM , nConnections ), V , ... V ) < r

N / (R+ Z) > x

1

minM

i

i

CPU

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Outline

• Problem Statement and Challenges• Our Approach

− Overview− Analytical Model for Multi-tier Applications− Component Profile Creation− Decomposition

• Validation− Performance Model Validation− Component Profile Creation− SLA Decomposition Validation

• Related Work• Summary and Future Work

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Virtualized Data Center Testbed

• Setup− a cluster of HP Proliant servers with Xen virtual machines (VMs)− each of the server nodes has two processors, 4 GB of RAM, and 1G

Ethernet interfaces− each running Fedora 4, kernel 2.6.12, and Xen 3.0-teseting− TPC-W and RUBiS− VMs hosting different tiers on different server nodes

• Estimate component service time − TS1: when an idle thread is assigned or when a new thread is created

− TS2: when a thread is returned to the thread pool or destroyed

− T = TS2 – TS1,S = T – waiting-time

− fine grained, works well for both light load and overload conditions

• Estimate number of stations− Max clients for Apache, Max threads for Tomcat, − MySQL: average number of running threads

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Performance Model Validation (1)

• Experiment setup− TPC-W, an industry standard e-

commerce application − Apache 2.0, Tomcat 5.5 and

MySQL 5.0− 10,000 items, 288,000 customers

in DB − exponential distribution with a

mean 3.5ms think time

• The model predicts the response time and throughput very accurately.

• The model works well even when the system load is high.

Response Time

0

1

2

3

4

5

6

7

8

1 5 10 50 100 150 200

Numer of Sessions

Re

sp

on

se

Tim

e (

se

cs

) Model

Measurement

Throughput

0

5

10

15

20

25

30

35

40

1 5 10 50 100 150 200Number of Sessions

Th

ruo

gh

pu

t (R

eq

s/s

ec

) Model

Measurement

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Performance Model Validation (2)

• Experiment setup− RUBiS, an eBay like auction site

application− Apache 2, Tomcat 5.5, MySQL 5.0− 1,000,000 users and 1,000,000 items

in DB − exponential distribution with a mean

3.5s think time− same set of model parameters

profiled with 200 users

• Using the same set of model input parameters, the model still predicts the performance for different workloads

• The model works for different applications with different performance characteristics

Response Time

0

2

4

6

8

10

12

14

50 100 150 200 250 300Number of Sessions

Av

g. R

es

po

ne

Tim

e (

se

cs

) Model

Measurement

Throughput

0

5

10

15

20

25

30

50 100 150 200 250 300

Number of Sessions

Av

g. T

hro

ug

hp

ut

(re

qs

/s) Model

Measurement

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Component Profiles Creation• Change CPU assignments to VMs

− management domain (dom0) uses one CPU and VMs use the other CPU− Simple Earliest Deadline First scheduling (SEDF) to set the CPU share− capped mode enforces a VM cannot use more than its share of the total CPU

• VMs hosting different tiers run on different servers.

• While profiling a component, fix the CPU assignment of other components at 100%

• Change the CPU assignment from 10% to 60% with an increase of 5% and collect the performance data

• Derive the component service time (workload independent) from the measurements

Tomcat Profile

0

1

2

3

4

5

6

7

8

10 15 20 25 30 35 40 45 50 55 60CPU Assignment (% of Total CPU)

Co

mp

on

ent

Ser

vice

Tim

e (s

ecs)

MySQL Profile

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

10 15 20 25 30 35 40 45 50 55 60CPU Assignment (% of Total CPU)

Co

mp

on

ent

Ser

vice

Tim

e (s

ecs)

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Designing a 3-tier RUBiS

6.5

139.5

Tomcat Queue Length

Per-component performance

69%74%13 4.8330%15%15%System0Users =100Resp. < 5 sec Throughput > 10 reqs/sec

21%23%24.38.86180%90%90%System2

92%99%11.115.9340%20%20%System1

67%61%21.599.8975%30%45%System0

70%67%21.419.7965%25%40%optimalUsers =300Resp < 10 secThroughput > 20 reqs/sec

MySQLTomcatThroughput.

(reqs/sec)

Resp.(sec)

TotalMySQLTomcat

CPU UtilizationApplicationPerformance

CPU Assignment

Design

SLOs

6.5

139.5

Tomcat Queue Length

Per-component performance

69%74%13 4.8330%15%15%System0Users =100Resp. < 5 sec Throughput > 10 reqs/sec

21%23%24.38.86180%90%90%System2

92%99%11.115.9340%20%20%System1

67%61%21.599.8975%30%45%System0

70%67%21.419.7965%25%40%optimalUsers =300Resp < 10 secThroughput > 20 reqs/sec

MySQLTomcatThroughput.

(reqs/sec)

Resp.(sec)

TotalMySQLTomcat

CPU UtilizationApplicationPerformance

CPU Assignment

Design

SLOs

design SLA monitoring and assessment

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Designing a 2-tier RUBiS

• Meets the SLOs and optimizes the resource usage

• Applicable to multi-tier applications with different SLOs, different software architectures and different performance characteristics

SLOsDesign

CPU Assignment Performance CPU Utilization

Apache MySQL Total Resp(secs.)

Throughput(reqs/sec)

Apache MySQL

Users =100Resp < 5 sec Throughput > 10 reqs/sec

System0 10% 15% 25% 4.83 13 72% 53%

Users=500Resp < 10 sec.Throughput > 40reqs/sec

System0 35% 30% 65% 8.2 42 76% 61%

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Outline

• Problem Statement and Challenges• Our Approach

− Overview− Analytical Model for Multi-tier Applications− Component Profile Creation− Decomposition

• Validation• Related Work• Summary and Future Work

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Related Work

• Using Queuing theory models for provisioning− C. Stewart, and K. Shen, NSDI 2005 − B. Urgaonkar, et. al., ICAC 2005

− A. Zhang, P. Santos, D. Beyer, and H. Tang, HPL-2002-301

• Performance models for multi-tier applications− B. Urgaonkar, et. al., SIGMETRICS 2005

− T. Kelley, WORLDS 2005

− U. Herzog and J. Rolia, layered queueing model

• Classification-based decomposition− Y. Udupi, A. Sahai and S. Singhal, IM 2007

• ACTS: Automated Control and Tuning of Systems

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Summary• Proposed a systematic approach to combine performance modeling and

component profiling to derive low level system thresholds from performance oriented SLOs

− create an effective design (e.g., resource selection and allocation, software configuration) to ensure SLAs

− SLA monitoring and assessment

• Presented an effective analytical performance model for multi-tier applications

− accurately predict the performance

− work well for applications with different software architectures, workloads and performance characteristics

• Validated the proposed approach for multi-tier applications in virtualized environment

− design the system to meet the given SLOs with reasonable resource usage

− work for common multi-tier applications with different SLOs goals and software architectures

− easy to adapt to changes in applications and environments

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Open Issues• Extensions to other parameters like memory and configuration

parameters − “nice” regression function?

• Non-stationary workload− multi-class queueing network, layering queueing model− combine regression model and queueing model

• Profiling and measurement− tools and technologies from Mercury Interactive− non-intrusive approach: derive model parameters via regression model

• Long running transactions − e.g., HPC applications, complex composed service

• Non-performance based SLOs− e.g., availability goals − tradeoff analysis

• Complex and large scale systems− advanced constraint solving and optimization algorithms required

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Future Work• Extend profiling to other parameters

− other system resources in addition to CPU resources, e.g., memory, I/O, cache

− software configuration parameters

− apply regression analysis on the profiling results

− general and practical measurement methodology

• Apply the approach to realistic applications and workloads− non-stationary workload: multi-class queueing network model

− non-intrusive profiling and measurement

− enterprise applications, HPC applications, and composed services

− non-performance SLOs, e.g., availability

− non-traditional SLOs, e.g., represented as utility functions

• Use advanced constraint solving and optimization algorithms for complex and large scale problems

• Integrate SLA decomposition into SLA life-cycle management− integrate with tradeoff analysis, SLA monitoring and SLA assessment

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• SLA Decomposition: Translating Service Level Objectives SLOs) to Low-level System Thresholds. Yuan Chen, Subu Iyer, Xue Liu, Dejan Molojicic, and Akhil Sahai. To appear in Proceedings of the 4th IEEE International Conference on Autonomic Computing (ICAC 2007), June 2007.

• HP Technical Report: http://www.hpl.hp.com/techreports/2007/HPL-2007-17.html

Papers

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Thank you!