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16-cs672-dynamic-control
Course: CS 8710, Fall 2008School: UGA
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QoS Preserving of E-commerce Sites Through Self-Tuning: A Performance Model Approach Daniel Menasc and Daniel Barbar E-Center for E-Business George Mason University Fairfax, VA, and Ronald Dodge Jr United States Army Forth Leavenworth, KS 2001 Menasc, Barbar, and Dodge. All Rights Reserved. 1 workload E-commerce Site QoS response time throughput probability of rejection 2001 Menasc, Barbar, and Dodge. All...
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QoS Preserving of E-commerce Sites Through Self-Tuning: A Performance Model Approach
Daniel Menasc and Daniel Barbar E-Center for E-Business George Mason University Fairfax, VA, and Ronald Dodge Jr United States Army Forth Leavenworth, KS
2001 Menasc, Barbar, and Dodge. All Rights Reserved. 1
workload
E-commerce Site
QoS
response time throughput probability of rejection
2001 Menasc, Barbar, and Dodge. All Rights Reserved. 2
Outline of the Talk
q The need for dynamic QoS control. q Definition of a QoS metric. q Discussion of the approach. q Experimental results. q Concluding remarks.
2001 Menasc, Barbar, and Dodge. All Rights Reserved.
3
Dynamic QoS Control: Motivation
q E-commerce sites are complex and composed of multiple tiers.
2001 Menasc, Barbar, and Dodge. All Rights Reserved.
4
Multi-tier Architecture
2001 Menasc, Barbar, and Dodge. All Rights Reserved.
5
Dynamic QoS Control: Motivation
q E-commerce sites are complex and composed of multiple tiers. qThe workload presents short-term variations with high peak-to-average ratios.
2001 Menasc, Barbar, and Dodge. All Rights Reserved.
6
E-commerce Bursty Workloads
60 sec
1 sec
2001 Menasc, Barbar, and Dodge. All Rights Reserved.
7
Dynamic QoS Control: Motivation
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!
q E-commerce sites are complex and composed of
2001 Menasc, Barbar, and Dodge. All Rights Reserved.
8
Dynamic QoS Control for E-commerce
q Definition of a combined QoS metric.
2001 Menasc, Barbar, and Dodge. All Rights Reserved.
9
QoS Deviation
q Relative difference between the observed QoS value and the QoS goal.
Response time deviation:
DQoS R = Rmax - Rmeasured Rmax
2001 Menasc, Barbar, and Dodge. All Rights Reserved.
10
QoS Deviation
q Relative difference between the observed QoS value and the QoS goal.
Response time deviation: Throughput deviation:
DQoS R = Rmax - Rmeasured Rmax
DQoS X =
X measured - X min X min
2001 Menasc, Barbar, and Dodge. All Rights Reserved.
11
QoS Deviation
q Relative difference between the observed QoS value and the QoS goal.
Response time deviation: Throughput deviation: Probability of rejection deviation:
DQoS R = Rmax - Rmeasured Rmax
DQoS X =
X measured - X min X min
MAX measured Prej - Prej MAX Prej
DQoS Prej =
2001 Menasc, Barbar, and Dodge. All Rights Reserved.
12
QoS Deviation
q Relative difference between the observed QoS value and the QoS goal.
Response time deviation: Throughput deviation: Probability of rejection deviation:
DQoS R = Rmax - Rmeasured Rmax
DQoS X =
X measured - X min X min
MAX measured Prej - Prej MAX Prej
DQoS Prej =
A negative deviation means that the QoS level for the metric has not been met.
2001 Menasc, Barbar, and Dodge. All Rights Reserved. 13
QoS Metric
q The QoS metric is defined as a linear combination of QoS deviations. q The weights are determined by management based on the relative importance of each metric.
QoS v = (DQoS R WR ) + (DQoS Prej WPrej ) + (DQoS X W X )
determined by management
2001 Menasc, Barbar, Dodge. and All Rights Reserved. 14
Dynamic QoS Control for E-commerce
q Definition of a combined QoS metric. q Use of hill-climbing techniques combined with predictive queuing models.
2001 Menasc, Barbar, and Dodge. All Rights Reserved.
15
Heuristic Optimization Approach
QoS0: observed QoS value for current QoS1 1 configuration QoS1, QoS2, and QoS3 are determined by a predictive queuing model of QoS0 0 2 QoS2 3 QoS3 the site.
r QoS = f W c1 c2
cm
16
2001 Menasc, Barbar, and Dodge. All Rights Reserved.
E-commerce Site Queuing Model
2001 Menasc, Barbar, and Dodge. All Rights Reserved.
17
Heuristic Optimization Approach
Server Parameter 2
Parameters for the queuing model are collected dynamically from the site. The QoS values for surrounding points are calculated The path with the greatest QoS gain is chosen. If no configuration improves the QoS or a limit is reached, the search ends
18
Server Parameter 1
2001 Menasc, Barbar, and Dodge. All Rights Reserved.
Dynamic QoS Control for E-commerce
q Definition of a combined QoS metric. q Use of hill-climbing techniques combined with predictive queuing models. q Implemented a TPC-W site in a multi-tier architecture.
2001 Menasc, Barbar, and Dodge. All Rights Reserved.
19
Prototype Configuration
QoS Controller
TPC-W site
Database Server
Web Server
Application Server
100 Mbps Hub
Workload Generator
Workstation
2001 Menasc, Barbar, and Dodge. All Rights Reserved.
20
Dynamic QoS Control for E-commerce
q Definition of a combined QoS metric. q Use of hill-climbing techniques combined with predictive queuing models. q Implemented a TPC-W site in a multi-tier architecture. q Implemented a QoS Controller on a dedicated machine and evaluated the approach.
2001 Menasc, Barbar, and Dodge. All Rights Reserved. 21
Dynamic QoS Controller
Arriving Request E-commerce System Completed Request
Configuration Instructions Arrival Proces...
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