Post on 31-Dec-2015
description
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
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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