autosec : an integrated middleware framework for dynamic service brokering
DESCRIPTION
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. - PowerPoint PPT PresentationTRANSCRIPT
AutoSeC: An Integrated Middleware Framework for Dynamic Service Brokering
Qi Han and Nalini Venkatasubramanian
Distributed Systems Middleware Grouphttp://www.ics.uci.edu/~dsm
Dept. of Information and Computer ScienceUniversity of California-Irvine
•Quality of Service enhanced resource management at all levels - storage management, networks, applications, middleware
QoS Aware Information Infrastructure
QoS Enabled WideArea Network
BattlefieldVisualization
BattlePlanning
BattlefieldVisualization
Data servers
CollaborativeMultimedia Application
Collaborative task ClientsData servers
BattlePlanning
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)
information about the residual capacity of network nodes and server nodes is based on an absolute value obtained from a periodic snapshot
Static Interval (SI) residual capacity information is maintained using a static
range-based representation Throttle (TR)
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 ratio of number of successful requests over the number of
whole requests Information collection overhead:
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
18 replicated data servers and 30 backbone links
Capacities of network links from 1.5Mbps to 155Mbps (mean= 64Mbps)
Capacities of servers 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
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
Not-all-req-monitored 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 throttle-based approach is higher
Static interval based algorithm results in higher request success ratio and overall efficiency than other three approaches
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
Not-all-req-monitored 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-HOPNot-All-Req-Monitored
SVRS-UFNot-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-req-monitored
Not-all-req-monitored
SVRS-HOP Static Interval
Throttle
SVRS-UF Static Interval
Throttle
CPSS Throttle Throttle
Preliminary Dynamic Service Composition Rules
Server type
Request type Web server Multimedia server
Computation server
General purpose server
Web request N/A N/A Multimedia
request CPSS+TR CPSS+TR CPSS+TR
Computation request
SVRS+SI SVRS+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