AutoSeC: An Integrated
Middleware Framework for
Dynamic Service Brokering
Qi Han and Nalini Venkatasubramanian
Distributed Systems Middleware Group
http://www.ics.uci.edu/~dsm
Dept. of Information and Computer Science
University of California-Irvine
QoS Aware Information Infrastructure
Data servers
QoS Enabled Wide
Area Network
Battlefield
Visualization
Collaborative
Multimedia
Application
Battle
Planning
Battlefield
Visualization
Data servers
Battle
Planning
Collaborative
task Clients
•Quality of Service enhanced resource management at all levels - storage
management, networks, applications, middleware
Global Information
Infrastructure




Proliferation of devices
 System support for multitude of smart devices that
 attach and detach from a distribution
infrastructure
 produce large volume of information at a high
rate
 limited by communication and power constraints
Require a customizable global networking backbone..
Applications (e.g. multimedia) may have QoS
requirements should be translated to system level
resource requirements
Explore effective middleware infrastructures which can
be used to support efficient QoS-based resource
provisioning algorithms
QoS-based Resource
Provisioning

Issues



Degree of network awareness that middleware and applications
must have to deal with network conditions
Resource provisioning algorithms utilize current system resource
availability information to ensure that applications meet their QoS
requirements
Additional Challenges


In highly dynamic (e.g. mobile) environments, system conditions
are constantly changing
Maintaining accurate and current system information is important
to efficient execution of resource provisioning algorithms
Automatic Service Composition
(AutoSeC)

Tools needed to securely and dynamically
manage an adaptable network infrastructure
while ensuring user QoS


a set of network management middleware
services is critical to providing these tools
AutoSeC:

dynamically select an appropriate combination of
information collection and resource provisioning
policies based on current system status
AutoSeC Framework
Network and Server
Information Collection Policies

System Snapshot (SS)


Static Interval (SI)


residual capacity information is maintained using a static rangebased representation
Throttle (TR)


information about the residual capacity of network nodes and
server nodes is based on an absolute value obtained from a
periodic snapshot
the directory holds a range-based representation of the monitored
parameter, with upper and lower bounds that can vary dynamically
Time Series (MA)

time series models are used to predict future trends in sample
values with some defined level of confidence.
Resource Provisioning Policies

Server Selection (SVRS): attempt to choose the best
replica and server for a given request



Least Utilization Factor Policy (SVRS-UF): This policy chooses
the server with the minimal utilization factor
Shortest Hop Policy (SVRS-HOP): This policy chooses the
nearest server in terms of the number of hops.
Combined Path and Server Selection (CPSS)

Given a client request with QoS requirements, we select the
server and links that maximize the overall use of resources.

This allows load balancing not only between replicated servers,
but also among network links to maximize the request success
ratio and system throughput.
Performance Evaluation

Objective:

To determine the best combination of information collection policies
and resource provisioning policies under varying application
workload



All-req-monitored: all the applications have QoS requirements
Not-all-req-monitored: some requests don’t have QoS requirements
Metrics:

Request success ratio


Information collection overhead:


ratio of number of successful requests over the number of whole
requests
sampling overhead and directory service update overhead
Overall performance efficiency:

ratio of the number of successful request to the information collection
overhead
Simulation Environment

Simulation topology


Capacities of network links


from 1.5Mbps to 155Mbps (mean= 64Mbps)
Capacities of servers


18 replicated data servers and 30 backbone links
based on real ISP data-center settings
Request and traffic generation model

Request arrival as
Impact of Information
Collection on CPSS


Compare the performance of the four
information collection policies with the CPSS
algorithm under similar conditions
All-req-monitored:



Snapshot based approach is very sensitive to
sampling period
Given the same sampling period, throttle based
approach is superior to other three approaches in
terms of performance efficiency
Not-all-req-monitored:

Exhibits similar results to above case
CPSS, All-Req-Monitored
CPSS, Not-All-Req-Monitored
Impact of Information
Collection on Server Selection

All-req-monitored



Not-all-req-monitored

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The overall performance efficiency of the throttle-based approach
is higher than that of MA based one
Static interval based algorithm results in higher request success
ratio and overall efficiency than the other three approaches
With fewer requests: the static interval based approach
yields higher request success ratios and performance
efficiency
When more requests arrive, the request success ratio
decreases and gets closer to the dynamic range based
approaches
In terms of overall performance efficiency,
the throttle based algorithm is better than
other approaches
Impact of Information
Collection on Server Selection

All-req-monitored


For both svrs-hop and svrs-uf, throttle-based and MA model based approaches have
similar request success ratios, but the overall performance efficiency of the throttlebased approach is higher
Static interval based algorithm results in higher request success ratio and overall
efficiency than other three approaches


Not-all-req-monitored



Only server resource factors are considered in server selection and also all requests are
reflected in resource provisioning module, representing residual link bandwidth with a static
interval is accurate enough
With fewer requests, the static interval based approach yields higher request success
ratios and also higher performance efficiency than other other dynamic ranged based
approaches; but when more requests arrive, the request success ratio decreases and
gets closer to the dynamic range based approaches
With a larger number of request, the success ratio is more sensitive to the application
workload change.
In terms of overall performance efficiency, the throttle based algorithm is better
than other approaches
SVRS-HOP, All-Req-Monitored
SVRS-UF, All-Req-Monitored
SVRS-HOP
Not-All-Req-Monitored
SVRS-UF
Not-All-Req-Monitored
Performance Summary



Both the accuracy and overhead of information
collection policies have a significant impact on the
performance of resource provisioning process
Although Snapshot based collection can obtain very
accurate information, the huge overhead introduced
by frequent sampling and directory updates makes it
a bad choice
MA based collection does not always perform very
well practically, while throttle based algorithm adapts
pretty well to the constantly changing environment
and turns out to be a very good choice in most cases
Optimal Combinations of
Information Collection and
Resource Provisioning Policies
All-reqmonitored
Not-all-reqmonitored
SVRS-HOP
Static Interval
Throttle
SVRS-UF
Static Interval
Throttle
CPSS
Throttle
Throttle
Preliminary Dynamic Service
Composition Rules
Server type
Request type
Web server
Web request
Multimedia
request
Computation
request
N/A
CPSS+TR
Multimedia
server
Computation
server
General
purpose
server
N/A
CPSS+TR
SVRS+SI
SVRS+TR
CPSS+TR
Future Work


To integrate policies for AutoSeC into
CompOSE|Q
To study network management middleware
services applicable to mobile environment



mobility management
adaptive probing architecture
distributed directory service management