1. Web switch support for differentiated services
PAWS 2001
Web switch support for differentiated services
V. Cardellini, E. Casalicchio
M. Colajanni, M. Mambelli
University of Roma “Tor Vergata”
University of Modena
Speaker:
Michele Colajanni
Additional Info:

2. Outline Motivations Cluster-based Web server systems
V. Cardellini,E. Casalicchio, M. Colajanni, M. Mambelli
Outline
Motivations
Quality of Service & Quality of Web Services
Cluster-based Web server systems
Policies for the Quality of Web Services
Results
System and workload model
Performance metrics
Simulation results
Summary

3. Why QoS in Web services? The second generation of Web sites
V. Cardellini,E. Casalicchio, M. Colajanni, M. Mambelli
Why QoS in Web services?
The second generation of Web sites
communication channel for critical information
fundamental technology for information systems of the most advanced companies and organizations
business-oriented media
The new Web requires
differentiation of users and services
supports to heterogeneous applications and user expectation
differentiated pricing for content hosting and service providing

4. Server-side proposals for QoWS
V. Cardellini,E. Casalicchio, M. Colajanni, M. Mambelli
Server-side proposals for QoWS
QoS
Network side
Server side
Single server
Multiple servers
Operating system
Web server
Single Web site
Web hosting

5. A Web cluster architecture
V. Cardellini,E. Casalicchio, M. Colajanni, M. Mambelli
A Web cluster architecture
WAN
Application servers .
Client requests
Layer-7 Web switch .
LAN
Web
servers
One-way vs. two-way architectures
Layer-4 vs. Layer-7 Web switches
(content-blind vs. content-aware switches)

6. Quality of Web Services (QoWS)
V. Cardellini,E. Casalicchio, M. Colajanni, M. Mambelli
Quality of Web Services (QoWS)
High performance systems  Systems for Quality of Service
QoS principles and mechanisms have been deeply investigated in the computer network area, but
QoS principles are not immediately applicable to the server side of the Web system
Network QoS and server QoWS principles must be combined to provide a peer-to-peer QoS for Web services
The focus of this talk will be on QoWS for Web clusters

7. From QoS to QoWS in Web clusters
V. Cardellini,E. Casalicchio, M. Colajanni, M. Mambelli
From QoS to QoWS in Web clusters
To define QoWS principles we need to find out
feasible mechanisms to achieve QoWS
Web cluster components able to implement QoWS principles and mechanisms
Our idea: start from main QoS principles
classification of services
performance isolation
high resource utilization
request admission
declaration
access control
QoWS

8. QoWS principles Classification (at Web switch) Performance isolation
V. Cardellini,E. Casalicchio, M. Colajanni, M. Mambelli
QoWS principles
Classification (at Web switch)
users and services identification
users and services classification
Performance isolation
queuing scheduling policies (at Web server)
resource partitioning (at the Web server for fine-grained level, at the Web Switch for coarse-grained level)
High resource utilization (at Web switch/server)
dynamic resource partitioning
Request admission (at Web switch/server)
estimation of resource demand (at Web switch)
access control mechanism (at Web switch/server)

9. Web switch support for QoWS (Classification and request admission)
V. Cardellini,E. Casalicchio, M. Colajanni, M. Mambelli
Web switch support for QoWS (Classification and request admission)
Application servers
WAN
Dropped requests
Admitted requests
Client requests
Layer-7 Web switch . .
LAN
Web
servers

10. V. Cardellini,E. Casalicchio, M. Colajanni, M. Mambelli
Web switch support for QoWS (Performance isolation and high resource utilization)
High users
WAN
Application servers
Dropped requests
Admitted requests
Client requests
Layer-7 Web switch .
LAN
Low users
Web servers

11. V. Cardellini,E. Casalicchio, M. Colajanni, M. Mambelli
Policies for QoWS (1)
Policies classified on the basis of the increasing number of QoWS principles they satisfy
classification and request admission
plus performance isolation
plus high resource utilization
Classification and request admission
SwitchAdm
service denied to the lower class of users by the Web switch
rejection mechanism based on cluster load (load threshold)

12. Policies for QoWS (2) Plus performance isolation
V. Cardellini,E. Casalicchio, M. Colajanni, M. Mambelli
Policies for QoWS (2)
Plus performance isolation
StaticPart
server nodes are statically partitioned in High Set (HS) and Low Set (LS)
different classes of requests are assigned to different server sets
Plus high resource utilization
Demand-Driven Service Differentiation (DDSD) [Zhu01]
dynamic partitioning based on the resolution of a constrained optimization problem
the resolution provides the number of servers assigned to each service class and the admission rate for each service class

13. Policies for QoWS (3) Plus high resource utilization DynamicPart
V. Cardellini,E. Casalicchio, M. Colajanni, M. Mambelli
Policies for QoWS (3)
Plus high resource utilization
DynamicPart

14. Policies and QoWS principles
V. Cardellini,E. Casalicchio, M. Colajanni, M. Mambelli
Policies and QoWS principles
SwitchAdm
StaticPart
DynamicPart
DSSD
Yes Yes No No
Yes Yes Yes No
Yes Yes Yes Yes
Classification Request Performance High resource
admission isolation utilization

15. System and workload model
V. Cardellini,E. Casalicchio, M. Colajanni, M. Mambelli
System and workload model
Parameter
Value (default)
Number of servers
Disk
Memory transfer rate
Intra-server bandwidth
10-20 (10)
20 MBps, 7200 RPM, 0.05 msec c.d., 9 msec s.t.
100 MBps
100 Mbps switched LAN
Session arrival rate
Requests per session
User think time
Objects per request
HTML size - body
- tail
Embedded object size
clients per second
Inverse Gaussian ( = 3.86,  = 9.46 )
Pareto ( = 1.4, k = 1)
Pareto ( = 1.33, k = 2)
Lognormal ( = 7.630,  = )
Pareto ( = 1, k = 10240)
Lognormal ( = 8.215,  = 1.46 )
Types of service
Dynamic requests
(80% of static requests, 20% of dynamic requests)
Hyper-exponential (high, medium, low intensive)

16. Performance metrics 95-percentile of latency time
V. Cardellini,E. Casalicchio, M. Colajanni, M. Mambelli
Performance metrics
95-percentile of latency time
latency time: completion time of a Web page request at the Web cluster side
X-percentile is used to define the Service Level Agreement (SLA) for predictive services
SLA on dynamic requests for high class requests set to 4 seconds
Percentage of dropped requests
percentage of users that perceive a deny of service
Stretch factor
ratio between latency time and service time

17. Sensitivity of latency time to client arrival rate
V. Cardellini,E. Casalicchio, M. Colajanni, M. Mambelli
Simulation results (1)
Sensitivity of latency time to client arrival rate
High class
Low class

18. Sensitivity of stretch factor to client arrival rate
V. Cardellini,E. Casalicchio, M. Colajanni, M. Mambelli
Simulation results (2)
Sensitivity of stretch factor to client arrival rate
High class
Low class

19. Sensitivity to the variable workload composition
V. Cardellini,E. Casalicchio, M. Colajanni, M. Mambelli
Simulation results (3)
Sensitivity to the variable workload composition
Latency time
Dropped requests

20. Simulation results (4) Latency time Dropped requests
V. Cardellini,E. Casalicchio, M. Colajanni, M. Mambelli
Simulation results (4)
Latency time
Dropped requests
Sensitivity to the number of Web servers (constant ratio between offered load and number of Web servers)

21. V. Cardellini,E. Casalicchio, M. Colajanni, M. Mambelli
Conclusions
Basic requirements for QoWS are satisfied by policies that integrate main mechanisms for network-QoS into the Web switch:
request classification and admission control
high resource utilization
dynamic server partitioning
Policies that satisfy all QoWS principles are able to meet SLA for different load and system conditions
The violation of even a single QoWS principle prevents to meet the SLA

22. Work in progress Address scalability issue for the Web switch
V. Cardellini,E. Casalicchio, M. Colajanni, M. Mambelli
Work in progress
Address scalability issue for the Web switch
New policies for dynamic request partitioning
Prototype implementation
Additional information: