Response Time Analysis of a Response Time Analysis of a Middleware Demultiplexing Middleware Demultiplexing

Pattern for Network ServicesPattern for Network Services

Aniruddha Gokhalea.gokhale@vanderbilt.edu

Asst. Professor of EECS, Vanderbilt

University, Nashville, TN

Swapna Gokhale ssg@engr.uconn.edu

Asst. Professor of CSE, University of

Connecticut, Storrs, CT

Presented at IEEE Globecom 2005Symposium on Advances in Networks and Internet

St. Louis MO

Nov 28-Dec 1, 2005

Work supported by collaborative grant from NSF CSR-SMA Program

Jeff Gray gray@cis.uab.edu

Asst. Professor of CISUniv. of Alabama at

BirminghamBirmingham, AL

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Problem Statement: Estimating Performance Characteristics of Network Services at Design-time

Standards middleware is increasingly being used to develop network services e.g., J2EE, .NET, CORBA, Web services

Middleware frameworks incorporate elegant patterns-based building blocks

Problem boils down to estimating performance of middleware

Provider Edge (PE)

Provider Edge (PE)

Provider Edge (PE)VR

VR

VR

VR

CE

CE

CE

VR

VR

VR

VR

CE

CE

CE

VR

VR

VR

VR

CE

CE

CE

CE

CE

Provider Edge (PE)VR

VR

VR

VR

Level 2 Service Provider

Backbone 1

Provider Edge (PE) VR

VR

VR

VR

Level 1 Service Providers

Provider Edge (PE) VR

VRVR

Backbone 2

VRVR

VR

CE

CE

CE

CE

CE

CE

CE

VP

N1

VP

N2

VP

N3

VP

N1

VP

N2

VP

N3

Virtual Router

FirewallMultiple tunnels to customer edge or virtual routers

Multiple tunnels to backbone or virtual routers

Level 1 Service Providers

• .e.g., VPN Service provided by a virtual router

• Provides differentiated services to customers, e.g., prioritized service

• VPN setup messages must be efficiently (de) multiplexed, serviced and forwarded

• Need to estimate capacity of the system at design-time

Network services need support for efficient (de)-multiplexing, dispatching and routing/forwarding

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Solution Approach: Middleware Performance Analysis using Stochastic Reward Nets

• Stochastic Reward Nets (SRNs) are an extension to Generalized Stochastic Petri Nets (GSPNs) which are an extension to Petri Nets.

• Extend the modeling power of GSPNs by allowing: Guard functions Marking-dependent arc multiplicities General transition probabilities Reward rates at the net level• Allow model specification at a level closer to intuition.• Solved using tools such as SPNP (Stochastic Petri Net Package).

N1 N2A1 A2

B1 B2

Sn1 Sn2

S2S1

Sr1 Sr2

StSnpSht

SnpShtInProg

T_SrvSnpSht T_EndSnpSht

(a) (b)

Transition

Place

Immediate transition

Inhibitor arc

Token

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Goal: Performance Analysis of Reactor Pattern in VR

The Reactor architectural pattern allows event-driven applications to demultiplex & dispatch service requests that are delivered to an application from one or more clients.

• Customers send VPN setup messages to router

• VPN setup messages manifest as events at the VR

• VR must service these events (e.g., resource allocation) and honor the prioritized service, if any

• Accepted messages are forwarded

• Events could be dropped in overload conditions

•Reactor pattern decouples the detection, demultiplexing, & dispatching of events from the handling of events

•Participants include the Reactor, Event handle, Event demultiplexer, abstract and concrete event handlers

Provider Edge (PE)VR

VR

VR

VR

CE

CE

CE

VP

N1

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Modeling VR Capabilities in a Reactor

network

Single Threaded Reactor

Event Handler with

exponential service time m1

select-based event demultiplexer

Event Handler with

exponential service time m2

l2 Poisson arrival rate

l1 Poisson arrival rate

N1

N2

incoming events

• Consider VPN service for two customer classes Reactor accepts and handles two types

of input events

• Differentiated services for two classes Events are handled in prioritized order

• Each event type has a separate queue to hold the incoming events. Buffer capacity for events of type one is 1 and of type two is 2.

• Event arrivals are Poisson for type one and type two events with rates l1and l2resp.

• Event service time is exponential for type one and type two events with rates m1and m2, resp.

Model of a single-threaded, select-based reactor implementation

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Performance Metrics of Interest for VR (i.e., Reactor) •Throughput:

-Number of events that can be processed -Applications such as telecommunications call processing.

•Queue length: -Queuing for the event handler queues. -Appropriate scheduling policies for applications with real-time requirements.

•Total number of events: -Total number of events in the system. -Scheduling decisions. -Resource provisioning required to sustain system demands.

•Probability of event loss: -Events discarded due to lack of buffer space. -Safety-critical systems. -Levels of resource provisioning.

•Response time: -Time taken to service the incoming event. -Bounded response time for real-time systems.

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Modeling the Reactor using SRN (1/2)

• Models arrivals, queuing, and prioritized service of events. • Transitions A1 and A2: Event arrivals.• Places B1 and B2: Buffer/queues.• Places S1 and S2: Service of the events.• Transitions Sr1 and Sr2: Service completions.• Inhibitor arcs: Place B1and transition A1 with multiplicity N1 (B2, A2, N2) - Prevents firing of transition A1 when there are N1 tokens in place B1. • Inhibitor arc from place S1 to transition Sr2: - Offers prioritized service to an event of type one over event of type two. - Prevents firing of transition Sr2 when there is a token in place S1.

N1 N2A1 A2

B1 B2

Sn1 Sn2

S2S1

Sr1 Sr2

StSnpSht

SnpShtInProg

T_SrvSnpSht T_EndSnpSht

(a) (b)

Event arr.

Service queue

Servicing the event

Drop events on overflow

Prioritized service

Service completion

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Modeling the Reactor using SRN (2/2)

N1 N2A1 A2

B1 B2

Sn1 Sn2

S2S1

Sr1 Sr2

StSnpSht

SnpShtInProg

T_SrvSnpSht T_EndSnpSht

(a) (b)

• Process of taking successive snapshots• Reactor waits for new events when currently enabled events are

handled• Sn1 enabled: Token in StSnpSht & Tokens in B1 & No Token in S1.• Sn2 enabled: Token in StSnpSht & Tokens in B2 & No Token in S2.• T_SrvSnpSht enabled: Token in S1 and/or S2.• T_EndSnpSht enabled: No token in S1 and S2.• Sn1 and Sn2 have same priority• T_SrvSnpSht lower priority than Sn1 and Sn2

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VR SRN: Performance Estimates• SRN model solved using Stochastic Petri Net Package (SPNP) to obtain

estimates of performance metrics.• Parameter values:l1secl2/sec, m12secm22/sec.

• Two cases: N1 = N2 = 1, and N1 = N2 = 5.

Observations:• Probability of event loss is higher when the buffer space is 1• Total number of events of type two is higher than type one. • Events of type two stay in the system longer than events of type one.• May degrade the response time of event requests for class 2 customers

compared to requests from class 1 customers

N1 = N2 = 1 N1 = N2 = 5Perf. metric

#1 #2 #1 #2

Throughput 0.37/s 0.37/s 0.40/s 0.40/s

Queue length 0.065 0.065 0.12 0.12

Total events 0.25 0.27 0.32 0.35

Loss probab. 0.065 0.065 .00026 .00026

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VR SRN: Sensitivity Analysis• Analyze the sensitivity of performance metrics to variations in input

parameter values.• Vary l1from 0.5/sec to 2.0/sec. • Values of other parameters:l2/sec, m12secm22/sec, N1 =

N2 = 5.• Compute performance measures for each one of the input values.

Observations:• Throughput of event requests from customer class #1 increases, but rate

of increase declines.• Throughput of event requests from customer class #2 remains

unchanged.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0.4 0.44 0.5 0.57 0.66 0.8 1 1.33 2

Lambda1

Th

rou

gh

pu

t

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Next Steps: Addressing Variability in Middleware

Per Building Block Variability– Incurred due to variations in

implementations & configurations for a patterns-based building block

– E.g., single threaded versus thread-pool based reactor implementation dimension that crosscuts the event demultiplexing strategy (e.g., select, poll, WaitForMultipleObjects

Although middleware provides reusable building blocks that capture commonalities, these blocks and their compositions incur variabilities that impact performance in significant ways.

Compositional Variability– Incurred due to variations in the

compositions of these building blocks

– Need to address compatibility in the compositions and individual configurations

– Dictated by needs of the domain

– E.g., Leader-Follower makes no sense in a single threaded Reactor

Reactor

event demultiplexing strategy

event handling strategy

single threaded

thread pool

select poll WaitForMultipleObjects

Qt Tk

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Composed System

Next Steps: Model-driven Performance Analysis of Middleware-based Network Services

Build and validate performance models for invariant parts of middleware building blocks

Weaving of variability concerns manifested in a building block into the performance models

Compose and validate performance models of building blocks mirroring the anticipated software design of DPSS systems

Estimate end-to-end performance of composed system

Iterate until design meets performance requirements

Applying design-time performance analysis techniques to estimate the impact of variability in middleware-based DPSS systems

Invariant model of a

pattern

Refined model of a

patternvariability variabilityweave weave

Refined model of a

pattern

Refined model of a

pattern

Refined model of a

pattern

Refined model of a

pattern

Refined model of a

pattern

Refined model of a

patternworkload

workloadsystem

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Concluding Remarks Network services are implemented using middleware building

blocks

Need to estimate performance early in development lifecycle

Stochastic Reward Nets enables scalable & intuitive performance analysis

Goal is to use model-driven generative techniques to automatically synthesize performance models for network services

Analysis for other dimensions of quality of service e.g., trustworthiness, dependability

www.cse.uconn.edu/~ssg (Swapna Gokhale)

www.dre.vanderbilt.edu/~gokhale (Aniruddha Gokhale)

www.gray-area.org (Jeff Gray)

Questions?

EXTRAS

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Designing & Evaluating SRNs for Network Services

N1 N2A1 A2

B1 B2

Sn1 Sn2

S2S1

Sr1 Sr2

StSnpSht

SnpShtInProg

T_SrvSnpSht T_EndSnpSht

(a) (b)

Initial Step• Obtain performance measures for individual patterns-based building blocks

Iterative Algorithm• Compose systems vertically and horizontally to form a DPSS system• Determine performance measures for specified workloads and service times• Alter the configurations until DPSS performance meets specifications.

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VR SRN: Disruption Detection•Obtain an anomaly score for the Reactor based on each one of the performance metrics for each event type.

•Correlate the anomaly scores based on each event type to obtain an overallanomaly score for the Reactor. - Anomaly score for the Reactor used at each CE to demultiplex events from two groups within a single organization.

•Anomaly score for the Reactor in the VR used to demultiplex events from the two organizations.

•Correlate the anomaly score of the Reactor in the VR with the score of the Reactor in CE #1 to determine service disruptions for organization #1.

•Correlate the anomaly score of the Reactor in the VR with the score of the Reactor in CE #2 to determine service disruptions for organization #2.

•Source of disruption may be identified by correlating the scores at various layers.

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Collaborative Research Performance analysis methodology (UConn – S. Gokhale)

– Develop and validate performance models for invariant characteristics of building blocks.

– Compose and validate performance models for common building block compositions.

– Develop model decomposition and solution strategies to alleviate state-space explosion issue.

Model-driven generative methodology (Vanderbilt – A. Gokhale)– Manually developing performance models of each block with its variations

is cumbersome– Compositions of building blocks cannot be made in ad hoc, arbitrary

manner– Model-driven generative tools use visual modeling languages and model

interpreters to automate tedious tasks and provide “correct-by-construction” development

Aspect-oriented methodology (Univ of Alabama, Birmingham – J. Gray)– Variability in building blocks and compositions is a primary candidate for

separating the concern as an aspect– Aspect weaving technology can be used to refine and enhance the models

by weaving in the concerns into the performance models

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VR SRN: Expected Behavior•VPN service has two modes of operation: normal & inclement.

•Normal mode: - Daily basis, some employees have negotiated telecommute plans and use VPN for remote access.

•Inclement mode: - Hazardous driving conditions due to bad weather may keep people at home. - Large number of telecommuters - Increase in the connection set up and tear down requests.

•Modes of operation can be defined at a finer level of granularity, such as a few hours, rather than a day.

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VR SRN: Expected Behavior•Normal mode: - l1secl2/sec, m12secm22/sec, N1 = N2 = 5 - Probability – 0.9

•Inclement mode: - l11secl21/sec, m12secm22/sec, N1 = N2 = 5 - Probability – 0.1

Perf. Metric Normal Inclement AverageEvent #1

Throughput 0.40/s 0.90/sec 0.4510/s

Queue length 0.12 1.86 0.2940

Loss probab. 0.09 0.21 0.0291