QoS Challenges and Directions for Large Distributed Systemsmenasce/papers/menasce-rome-2004.pdf ·...
Transcript of QoS Challenges and Directions for Large Distributed Systemsmenasce/papers/menasce-rome-2004.pdf ·...
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QoS Challenges and Directions for Large Distributed Systems
Prof. Daniel Menascé Department of Computer Science
E-Center for E-Business George Mason University
Fairfax, VA, USA [email protected]
www.cs.gmu.edu/faculty/menasce.html
2004 D. A. Menascé. All Rights Reserved.
Workshop on QoS for Geographically Distributed Systems,Rome, Italy, June 9-10, 2004
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Huge number of devicesHuge number of data sourcesMany different data formats
Heterogeneous devicesWidely varying capacity
Wired and wirelessWidely varying QoS requirements
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Node failuresConnectivity failures
Security attacksLimited battery power
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Characteristics of the new generation of distributed software systems
qHighly distributedqComponent-based (for reusability)qService-oriented architectures (SOA)qUnattended operationqHostile environmentsqComposed of a large number of “replaceable”
components discovered at run-time qRun on a multitude of (unknown and heterogeneous)
hardware and network platforms
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Requirements of Next Generation of Large Distributed SystemsqAdaptable and self-configurable to changes in
workload intensity:Ø QoS requirements at the application and
component level must be met.
qAdaptable and self-configurable to withstand attacks and failures:Ø Availability and security requirements must be
met.
2004 D. A. Menascé. All Rights Reserved.
self-configurable, self-optimizing, self-healing,and self-protecting
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Important Technologies
qWeb Services:§ SOAP, UDDI, WSDL
q Grid Computingq Peer to Peer NetworksqWireless Networkingq Sensor and ad-hoc networks
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Challenges
q Dynamically changing application structure. q Hard to characterize the workload.Ø unpredictableØ dynamically changing servicesØ application adaptation
q Difficult to build performance models.Ø moving target
q Multitude of QoS metrics at various levels of a distributed architectureØ response time, jitter, throughput, availability, survivability, recovery
time after attack/failure, call drop rate, access failure rate, packet delay, packet drop rate.
q Tradeoffs between QoS metrics (response time vs. availability, response time vs. security)
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Challenges (cont’d)
qNeed to perform transient, critical time (e.g., terrorism attack or catastrophic failures) analysis of QoS compliance. Steady-state analysis is not enough.qMapping of global SLAs to local SLAsØCost and pricing issues.
qQoS monitoring, negotiation, and enforcement.qPlatform-neutral representation of QoS goals and
contracts.qResource management: resource reservation,
resource allocation, admission control.Ø non-dedicated resources
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What we need …
q Design self-regulating (autonomic systems)q Embed within each system component:Ø Monitoring capabilitiesØ Negotiation capabilities (requires predictive modeling power)Ø Self-protection and recovery capabilities (for attacks, failures, and
overloads)q Push more of the desired system features to individual
componentsq Design QoS-aware middlewareØ QoS negotiation protocolsØ Mapping of global to local SLAsØ QoS monitoring
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Rest of this talk …
q Novel uses for performance modelsq Two examples of self-regulating systems:Ø A three-tiered e-commerce systemØ QoS-aware software components
q Concluding Remarks
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Rest of this talk …
q Novel uses for performance modelsq Two examples of self-regulating systems:Ø A three-tiered e-commerce systemØ QoS-aware software components
q Concluding Remarks
2004 D. A. Menascé. All Rights Reserved.
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What are performance models good for?
q At the design stage:ØCompare competing design alternatives.§ A large number of low capacity servers vs. a small
number of large capacity servers?
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LoadBalancer
WebServers
LoadBalancer.
.
.
?
Site Design
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What are performance models good for?
q At the design stage:ØCompare competing design alternatives.§ A large number of low capacity servers vs. a small
number of large capacity servers?
qDuring production:ØMedium and long-term (weeks and months):§ Capacity planning.
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Capacity Planning
ResponseTime (sec)
Load (sessions/sec)
3 mo. 6 mo. 9 mo.
QoS Goal
2
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What are performance models good for?
q At the design stage:ØCompare competing design alternatives.§ A large number of low capacity servers vs. a small
number of large capacity servers?
qDuring production:ØMedium and long-term (weeks and months):§ Capacity planning.
Ø Short-term (minutes):§ Dynamic reconfiguration.
2004 D. A. Menascé. All Rights Reserved.
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Rest of this talk …
q Novel uses for performance modelsq Two examples of self-regulating systems:Ø A three-tiered e-commerce systemØ QoS-aware software components
2004 D. A. Menascé. All Rights Reserved.
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Automatic QoS Control: Motivation
qModern computer systems are complex and composed of multiple tiers.
2004 D. A. Menascé. All Rights Reserved.
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Multi-tier Architecture
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Automatic QoS Control: Motivation
qModern computer systems are complex and composed of multiple tiers.qThe workload presents short-term variations
with high peak-to-average ratios.
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3600 sec
60 sec
1 sec
Multi-scale time workload variation
2004 D. A. Menascé. All Rights Reserved.
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Automatic QoS Control: Motivation
q Modern computer systems are complex and composed of multiple tiers.qThe workload presents short-term variations with high
peak-to-average ratios.qMany software and hardware parameters influence the
performance of e-commerce sites.Manual reconfiguration is not an option!Need self-managing systems.
2004 D. A. Menascé. All Rights Reserved.
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Computer System
ServiceDemand
Computation
WorkloadAnalyzer
PerformanceModelSolver
QoSControllerAlgorithm
arrivingrequests
completingrequests
QoS Controller
(2)
(1)
(3) (4)
(5)
(6) (7)
(8)
(9)
(10) (11)
(12)
QoSgoals
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Computer System
ServiceDemand
Computation
WorkloadAnalyzer
PerformanceModelSolver
QoSControllerAlgorithm
arrivingrequests
completingrequests
QoS Controller
(2)
(1)
(3) (4)
(5)
(6) (7)
(8)
(9)
(10) (11)
(12)
QoSgoals
iU 0X
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Computer System
ServiceDemand
Computation
WorkloadAnalyzer
PerformanceModelSolver
QoSControllerAlgorithm
arrivingrequests
completingrequests
QoS Controller
(2)
(1)
(3) (4)
(5)
(6) (7)
(8)
(9)
(10) (11)
(12)
QoSgoals
iU 0X
0/ XUD ii =
2004 D. A. Menascé. All Rights Reserved.
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Computer System
ServiceDemand
Computation
WorkloadAnalyzer
PerformanceModelSolver
QoSControllerAlgorithm
arrivingrequests
completingrequests
QoS Controller
(2)
(1)
(3) (4)
(5)
(6) (7)
(8)
(9)
(10) (11)
(12)
QoSgoals
λ 2004 D. A. Menascé. All Rights Reserved.
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Computer System
ServiceDemand
Computation
WorkloadAnalyzer
PerformanceModelSolver
QoSControllerAlgorithm
arrivingrequests
completingrequests
QoS Controller
(2)
(1)
(3) (4)
(5)
(6) (7)
(8)
(9)
(10) (11)
(12)
QoSgoals
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Controller Interval
measurements fromservers in e-commerce
site.
measurements fromservers in e-commerce
site.
reconfigurationcommands
reconfigurationcommands
reconfigurationcommands
controller algorithm
i-th interval (i+1)-th interval
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Combined QoS Metric
XXPPRR QoSwQoSwQoSwQoS ∆×+∆×+∆×=
Rw Pw Xw, , and are relative weights that indicate the relativeimportance of response time, throughput, and probability of rejection.
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QoS Metric
PQoS∆
XXPPRR QoSwQoSwQoSwQoS ∆×+∆×+∆×=
Rw Pw Xw, , and are relative weights that indicate the relativeimportance of response time, throughput, and probability of rejection.
RQoS∆ , , and are relative deviations of theresponse time, throughput, and probability of rejection metrics withrespect to their desired levels.
XQoS∆
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QoS Metric
PQoS∆
XXPPRR QoSwQoSwQoSwQoS ∆×+∆×+∆×=
Rw Pw Xw, , and are relative weights that indicate the relativeimportance of response time, throughput, and probability of rejection.
RQoS∆ , , and are relative deviations of theresponse time, throughput, and probability of rejection metrics withrespect to their desired levels.
XQoS∆
The QoS metric is a dimensionless number in the interval [-1, 1].
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QoS Metric
PQoS∆
XXPPRR QoSwQoSwQoSwQoS ∆×+∆×+∆×=
Rw Pw Xw, , and are relative weights that indicate the relativeimportance of response time, throughput, and probability of rejection.
RQoS∆ , , and are relative deviations of theresponse time, throughput, and probability of rejection metrics withrespect to their desired levels.
XQoS∆
The QoS metric is a dimensionless number in the interval [-1, 1].
If all metrics meet or exceed their QoS targets, QoS = 0.
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Response Time Deviation
),max( max
max
measured
measuredR RR
RRQoS
−=∆
• = 0 if the response time meets its target.• > 0 if the response time exceeds its target.• < 0 if the response time does not meet its target.
•
•
1/)(1 max1
<−≤∆ ∑=
RDQoSK
iiR
Rmeasured QoSRR ∆≤−−<− )/1(1 max
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Probability of Rejection Deviation
• = 0 if the probability of rejection meets its target.• > 0 and = 1 if the probability of rejection exceeds its target.• < 0 and = -1 if the probability of rejection does not meet its target.
),max( max
max
measured
measuredP PP
PPQoS −=∆
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Throughput Deviation
•• = 0 if the throughput meets its target.• > 0 and < 1 if the throughput exceeds its target.• < 0 and > -1 if the throughput does not meet its target.
),max( *min
*min
XXXX
QoSmeasured
measuredX
−=∆
),min( min*min XX λ=
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Heuristic Optimization Approach
Server Parameter 1
ServerParameter 2
• The space of configurationpoints is searched usinga combinatorial search technique.
• Each point has a QoS valuecomputed through ananalytic performancemodel.
),...,,,( 21 mcccWfQoSr
=
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Heuristic Optimization Approach
Server Parameter 1
ServerParameter 2
• The space of configurationpoints is searched usingcombinatorial search techniques.
• Each point has a QoS valuecomputed through ananalytic performancemodel.
Currentconfiguration
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Heuristic Optimization Approach
Server Parameter 1
ServerParameter 2
• The space of configurationpoints is searched usingcombinatorial search techniques.
• Each point has a QoS valuecomputed through ananalytic performancemodel.
Newconfiguration
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A Queuing Model is Used to Compute QoS Values
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Prototype Configuration
Workstation
100 Mbps Hub
WebServer
ApplicationServer
DatabaseServer
QoSController
WorkloadGenerator
TPC-Wsite
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Experiment Results
Arrival rate
0
10
20
30
40
50
60
70
80
90
100
0 5 10 15 20 25 30 35
Time (Controller Intervals)
Arr
ival
Rat
e (r
equ
ests
/sec
)
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Results of QoS Controller
-0.8
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
14.4
14.0
14.4
41.3
38.1
36.6
62.2
61.5
63.5
77.3
80.4
78.5
83.8
85.7
85.9
88.2
88.3
89.2
90.0
90.3
89.5
85.7
81.4
83.7
79.0
75.9
73.5
66.2
64.1
64.4
Arrival Rate (req/sec)
Qo
S
Controlled QoS Uncontrolled QoS
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Experiment Results
Arrival rate
0
10
20
30
40
50
60
70
80
90
100
0 5 10 15 20 25 30 35
Time (Controller Intervals)
Arr
ival
Rat
e (r
equ
ests
/sec
)
QoS is not met!
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Variable inter-arrival and service times of requests
qReal workloads exhibit high variability in:ØTraffic intensityØService demands at various system resources
qNeed to investigate the efficiency of the proposed self-managing technique under these conditionsqConsider variability in requests inter-arrival
time and requests service times at physical resources (e.g., CPU, disk)
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Effect of Varying the COV of the Service Time (Ca = 1)
Ca = 1, Cs = 2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Controller Interval
Ave
rage
QoS
No Controller Beam Search Hill Climbing
Ca = 1, Cs =4
-0.2
0
0.2
0.4
0.6
0.8
1
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Controller Interval
Ave
rag
e Q
oS
No Controller Beam Search Hill Climbing
Ca = 1, Cs = 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Controller Interval
Ave
rag
e Q
oS
No Controller Beam Search Hill Climbing
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Cs = 1 Cs = 2
Cs = 4
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Effect of Varying the COV of the InterarrivalTime (Cs = 1)
Ca = 1, Cs = 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Controller Interval
Ave
rage
QoS
No Controller Beam Search Hill Climbing
Ca = 2, Cs = 1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Controller Interval
Ave
rage
QoS
No Controller Beam Search Hill Climbing
Ca = 4, Cs = 1
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Controller Interval
Ave
rage
QoS
No Controller Beam Search Hill Climbing
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Ca = 1 Ca = 2
Ca = 4
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Extreme values for Ca and CsCa = 4, Cs = 4
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Controller Interval
Ave
rage
QoS
No Controller Beam Search Hill Climbing
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49
Dynamic Controller Interval and Workload Forecasting
-0.2
0
0.2
0.4
0.6
0.8
1
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Monitoring Interval
Ave
rag
e Q
oS
No Forecasting +Dynamic Controller IntervalForecasting + Dynamic Controller Interval
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Sensitivity of Controller to SLAs
qNeed to investigate the controller behavior in the case of a variation in the SLAsqWe ran experiments for stricter and more
relaxed SLAsØBase: Rmax = 1.2 sec, Xmin = 5 req/sec, Pmax = 0.05
ØStrict: Rmax = 1.0 sec, Xmin = 7 req/sec, Pmax = 0.03
ØRelaxed: Rmax = 1.5 sec, Xmin = 4 req/sec, Pmax = 0.10
qUsed Ca = Cs = 2
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Sensitivity of Controller to SLAs
Controller Sensitivity
-40
-30
-20
-10
0
10
20
30
40
50
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Controller Interval
Ave
rage
QoS
Rel
ativ
e V
aria
tion
No Controller Relaxed SLA No Controller Strict SLABeam Search Relaxed SLA Beam Search Strict SLA
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Rest of this talk …
q Novel uses for performance modelsq Two examples of self-regulating systems:Ø A three-tiered e-commerce systemØ QoS-aware software components
q Concluding Remarks
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53
Q-Applications and Q-components
Q-component
Q-component
Q-component
Q-component
Q-component
Q-application
registration
Servicedirectory
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Q-Applications and Q-components
Q-component
Q-component
Q-component
Q-component
Q-component
Q-application
discovery
Servicedirectory
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Q-Applications and Q-components
Q-component
Q-component
Q-component
Q-component
Q-component
Q-application
QoS Negotiation
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Q-Applications and Q-components
Q-componentQ-component
Q-component
Q-component
Q-component
Q-application
Service Access
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QoS-Aware Software Components: Q-Components
q Engage in QoS Negotiations (accept, reject, counter-offer)q Provide QoS guarantees for multiple concurrent
servicesq Maintain a table of QoS commitmentsq Service dispatching based on accepted QoS
commitmentsq Q-components are the building blocks of QoS-aware
applications
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Service 1Service 1
ServiceDispatcher
Service kService k
. . .
Architecture of a typical software component
ServiceRegistration
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Service 1Service 1
QoSRequestHandler
ServiceDispatcher
Service kService k
. . .
Architecture of a Q-component (QoS Negotiation)
ServiceRegistration
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Service 1Service 1
QoSRequestHandler
QoSEvaluator
ServiceDispatcher
Service kService k
. . .
Table ofQoS commitmentsService
Registration
QoSNegoti-
ator
Architecture of a Q-component (QoS Negotiation)
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Service 1Service 1
QoSRequestHandler
QoSEvaluator
PerformanceModelSolver
ServiceDispatcher
Service kService k
. . .
Table ofQoS commitmentsService
Registration
QoSNegoti-
ator
Architecture of a Q-component (QoS Negotiation)
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Service 1Service 1
ServiceDispatcher
Service kService k
. . .
PerformanceMonitor
Table ofQoS commitments
Architecture of a Q-component –Service Requests
ServiceRegistration
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Service 1Service 1
QoSRequestHandler
QoSEvaluator
PerformanceModelSolver
ServiceDispatcher
Service kService k
. . .
PerformanceMonitor
Table ofQoS commitmentsService
Registration
QoSNegoti-
ator
Architecture of a Q-component
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Successful QoS Negotiation
Client Q-componentQoSRequest(rid,Sid,N,Rmax,Xmin)
Accept(rid,token) Request in ToC
ServiceReq (…,token)
ReplyReq (…)
ServiceReq (…,token)
ReplyReq (…)
. . .
EndSession (token)Request removed from ToC
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On-time Accepted Counteroffer
Client Q-componentQoSRequest(rid,Sid,N,Rmax,Xmin)
CounterOffer (rid,N’,token) Request in ToC
AcceptCounterOffer (token)timeout
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Expired Accepted Counteroffer
Client Q-componentQoSRequest(rid,Sid,N,Rmax,Xmin)
CounterOffer (rid,N’,token) Request in ToC
AcceptCounterOffer (token)
timeout
ExpiredCounterOffer (rid)
Request removed from ToC
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Rejected Counteroffer
Client Q-componentQoSRequest(rid,Sid,N,Rmax,Xmin)
CounterOffer (rid,N’,token) Request in ToC
RejectCounterOffer (token)timeout
Request removed from ToC
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Rejected QoS Negotiation
Client Q-componentQoSRequest(rid,Sid,N,Rmax,Xmin)
RejectQoSRequest (rid)
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Decision Tablefor QoSNegotiation
Accept
Reason Remedy Current Others Decision
OK OK Counter OfferOK Not OK Reject
Not OK & MAXR is violated
OK Reject
Not OK & MAXR is violated Not OK
Reject
MINX & MAXR are
violatedReject
Reason Remedy Current Others DecisionOK OK Counter OfferOK Not OK Reject
MINX & MAXR are
violatedReject
Reason Remedy Current Others DecisionOK OK Counter Offer
OKNot OK: N=1 but others still
violatedReject
Any Decrease N
4. Only Other Requests are Violated
Not OK: MINX
violated or N=0
OK or not OK Reject
OK OK Counter Offer
Not OK: MINX is
violated or N=0
OK Reject
Only MINX is violated
Decreasing N reduces X and increasing N increases R. So, there is no solution.
Increase N
Decrease N
X could be increased by increasing N. But this would further violate the QoS of
other classes.
Decreasing N reduces X and increasing N increases R. So, there is no solution.
Not OK: MINX is
violated or N=0
OK or not OK
1. Current and other requests are satisfied
Reject
Only MINX is violated
2. Only Current is Violated
Only MAXR is violated Decrease N
3. Current Request and Others are Violated
Only MAXR is violated Reject
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Concurrency level
Concurrency level
Resp. Time
Throughput
N
N
current
current
others
others
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Building a Performance ModelNew Request: Sid = 3, N = 12
Base Matrix of Service Demands (in msec): Table of Commitments (ToC):Commitment
ID Service ID N …1 2 3 1 2 10 …
CPU 25 34 20 2 3 15 …Disk 1 30 50 24 3 1 8 …Disk 2 28 42 31 4 1 20 …
5 2 13 …
Matrix of Service Demands (in msec)
1 2 3 4 5 6CPU 34 20 25 25 34 20Disk 1 50 24 30 30 50 24Disk 2 42 31 28 28 42 31
Vector N: 10 15 8 20 13 12
Service
Class
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Building a Performance ModelNew Request: Sid = 3, N = 12
Base Matrix of Service Demands (in msec): Table of Commitments (ToC):Commitment
ID Service ID N …1 2 3 1 2 10 …
CPU 25 34 20 2 3 15 …Disk 1 30 50 24 3 1 8 …Disk 2 28 42 31 4 1 20 …
5 2 13 …
Matrix of Service Demands (in msec)
1 2 3 4 5 6CPU 34 20 25 25 34 20Disk 1 50 24 30 30 50 24Disk 2 42 31 28 28 42 31
Vector N: 10 15 8 20 13 12
Service
Class
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Service 0Results:
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Service 1Results:
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Service 2Results:
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Svc No.
No. Dropped Sessions
No. of Sessions % Drop
% Resp. Time Reduction
0 24 440 5 111 19 470 4 92 59 590 10 7
Total 102 1500 7 9
Svc No.
No. Dropped Sessions
No. of Sessions % Drop
% Resp. Time Reduction
0 52 440 12 211 66 470 14 162 148 590 25 12
Total 266 1500 18 16
Svc No.
No. Dropped Sessions
No. of Sessions % Drop
% Resp. Time Reduction
0 92 440 21 281 140 470 30 262 263 590 45 20
Total 102 1500 33 24
f = 0.0
f = 0.10
f = 0.25
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Real -time Stock Quote Response Time
0.000
0.050
0.100
0.150
0.200
0.250
0.300
0.350
0.400
0.450
4 13 18 21 31 35 42 50 55 61 106
111
114
121
126
133
139
144
148
sessionID
Res
pons
eTim
e(s)
R_NonQoS R_QoS
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Stock Quote Service
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Delayed Stock Quote Response Time
0.000
0.050
0.100
0.150
0.200
0.250
0.300
0.350
0.400
1 5 8 15 24 28 34 38 46 49 57 62 109
118
125
130
137
142
sessionID
Res
pons
eTim
e(s)
R_NonQoS R_QoS
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79
Concluding Remarks
q Performance models can be used to build QoS controllers for complex multi-tiered systems:ØControlled system provides better QoS values even in case
of high variability in request’s inter-arrival and service timesØShort term workload forecasting improves the QoS,
especially when the workload intensity gets close to system saturation levelØDynamic adjustment of the controller interval length
improves the QoS furtherØEven when basic model assumptions are violated, the
models are robust enough to track the evolution of the performance metrics as the workload and configuration parameters change.
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Concluding Remarks (Cont’d)
q Performance models can be used by software components to make admission control decisions.ØQoS components should be able to negotiate QoS
requests and perform admission control ØQoS negotiation overhead is small (it did not
exceed 10% of the CPU service demand in our experiments).
2004 D. A. Menascé. All Rights Reserved.
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Bibliographyq “Assessing the Robustness of Self-Managing Computer Systems under
Highly Variable Workloads,” M. Bennani and D. Menascé, Proc. International Conf. Autonomic Computing (ICAC-04), New York, NY, May 17-18, 2004.
q “A Framework for QoS-Aware Software Components,” D. Menascé, H. Ruan ,and H. Gomaa, Proc. 2004 ACM Workshop on Software and Performance (WOSP’04), San Francisco, CA, January 14, 2004.
q “On the Use of Performance Models to Design Self-Managing Systems,” D. Menascé and M. Bennani, Proc. 2003 Computer Measurement Group Conference, Dallas, TX, Dec. 71-2, 2003.
q “Automatic QoS Control,” IEEE Internet Computing, January/February 2003, Vol. 7, No. 1.
q “Preserving QoS of E-commerce Sites Through Self-Tuning: A Performance Model Approach,” D. A. Menascé, R. Dodge and D. Barbara, Proc. 2001 ACM Conference on E-commerce, Tampa, FL, October 14-17, 2001.
2004 D. A. Menascé. All Rights Reserved.
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Grazie. Thanks.
For slides go to:
http://www.cs.gmu.edu/faculty/menasce.html
and click on Talks (after June 16, 2004).
2004 D. A. Menascé. All Rights Reserved.