A Cost Driven Approach to Information Collection for Mobile Environments

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A Cost Driven Approach to Information Collection
for Mobile Environments

• Qi Han
• Nalini Venkatasubramanian
• Department of Information and Computer Science
• University of California-Irvine

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QoS Aware Information Infrastructure
Data servers
QoS Enabled Wide Area Network
CollaborativeMultimedia Application
Mobile hosts

• Quality of Service enhanced resource management
  at all levels - storage management, networks,
  applications, middleware

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Motivation

• Advanced level of tetherless mobile multimedia
  services requires
• The development of a wireless network that
  supports integrated multimedia services
• Focus of prior work
• The development of intelligent network management
  middleware services that provides agile
  interfaces to mobile multimedia services
• Our objective to provide support for mobility
  and QoS management at the middleware layer
  independent of the underlying specific network
  architecture

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QoS-based Resource Provisioning

• Issues
• Effective middleware infrastructure that must
  adapt to changing network conditions
• Resource provisioning algorithms that utilize
  current system resource availability information
  to ensure that applications meet their QoS
  requirements
• Additional Challenges
• In mobile environments, system conditions are
  constantly changing
• Maintaining accurate and current system
  information is important to efficient execution
  of resource provisioning algorithms

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The Information Collection Problem

• Goal
• To provide information good enough for resource
  provisioning tasks such as admission control,
  load balancing etc.
• Need an information collection mechanism that
• is aware of multiple levels of imprecision in
  data
• is aware of quality requirements of applications
• makes optimum use of the system (network and
  server) resources while tolerating imprecision of
  the information
• Collected Parameters
• Network link status, Data server capacity (Remote
  disk bandwidth, Processor capacity), Mobile host
  status

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Directory Enabled Network Information Collection

• Provide directory service as an information base
  for QoS-provisioning algorithms
• feasible servers for requests, available network
  and server resources
• Uses distributed probes to monitor traffic and
  collect dynamic load state information
• Directory Enabled Information Collection
• Information Acquisition
• Directory Organization and Manipulation
• Approximation and Cost
• Scalability Hierarchical directory organization
  Caching

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Former Information Collection Approaches for
Non-mobile Environments

• Instantaneous snapshot based techniques (SS)
• Monitoring module samples residue capacity of
  network link periodically and updates directory
  with latest value
• Static range based intervals
• Partition link capacity into static intervals and
  update directory with the interval number
• 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

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Challenges in Information Collection Problem for
Mobile Environments

• Inherent tradeoff between information accuracy
  and system performance
• Solutions for non-mobile environments are not
  appropriate for mobile environments
• Increased dynamicity
• Constant change of client access points to fixed
  network

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Our Approach

• Dynamic range-based representation
• Mobile host Aggregation driven collection
• Source and consumer-initiated triggers and
  updates
• 2 phase information collection process
• Address the tradeoff between accuracy of
  directory information and the update overhead
  costs for mobile environments

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Information Collection Framework
Information Consumer
Server selection
Mobility management
Information Repository
QoS management
Mobile QoS management

Information Mediator
Location management
Information collection
Information Source
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Components of Information Collection Framework

• Information source
• Managed entities server, link, mobile or
  stationary host
• Information consumer
• Consumers data collected from sources
• Information mediator
• Decision point of the information collection
• Information repository
• Holds system state information about sources

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AutoSeC (Automatic Service Composition) Framework
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Aggregate Mobility Model
Region i
Xregion
Mobile host j at (xj(t),yj(t))
Yregion
Ymax
Aggregation of Region i at time t
Xmax
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Resource Utilization Factor

• Resource utilization factor for network links
• Resource utilization factor for servers

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Generalized Aggregation Based Information
Collection (Gen-ABIC)

• Use a range RL,U to represent the monitored
  parameter
• Phase 1
• Derives the aggregate mobility patterns
• Utilizes the aggregation status and current
  resource utilization status to adjust the
  collection parameters such as sampling frequency
  SF and range size R
• Phase 2
• Utilizes feedback from the sources
  (source-initiated triggers and updates) and
  consumers (consumer-initiated triggers and
  updates) for further customization of the
  collection process

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State Diagram of Information Collection Process
Current range
Current range
Directory service
Information source
Information consumer
Information mediator
Regular probing
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Cost Factors in an Information Collection Process

• Regular sampling overhead Crs
• Regular directory update overhead Cru
• Source/consumer-initiated trigger overhead Cst
  and Cct
• Source/consumer-initiated directory update
  overhead Csu and Ccu

consumer
Cct
Ccu
Cru
Directory service
mediator
Csu
Crs
Cst
source
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Optimal Range Size to Minimize the Cost

• To minimize the overall cost, a good range size
  is needed to reduce the need for further updates
• To avoid source-initiated triggers and updates, R
  should be big enough
• PstKst/R2 , PsuKsu/R2
• To avoid consumer-initiated triggers and updates,
  R should be small enough
• PctKctR , PcuKcu R
• To minimize Cost

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The CDIC Algorithm
CDIC Algorithm( ) / invoked periodically /
/ Phase 1 aggregation driven coarse-grained
adjustment of parameters / Compute
host aggregation level Compute resource
utilization level switch ( resource
utilization) case
high set SF and R to be minimum
case low set SF and R to be minimum
case medium increase/decrease SF and R based
on
current aggregation level
/Phase 2 fine-grained adjustment of range
size / Calculate Kst, Ksu, Kct, Kcu
based on monitored cost factors appropriately
Set R to be optimal which minimizes the
cost.
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Issues of CDIC

• The model parameters such as Pst, Psu, Pct, Pct
  need to be monitored
• Monitoring complexity affects the system
  performance to a great extent
• User QoS may be compromised
• Utilizing mobile host aggregation status to drive
  the information collection process could
  sacrifice some individual requests QoS, but
  overall system performance is improved

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Optimized Cost Driven Information Collection
(Opt-CDIC)

• Further reduce communication overhead without
  sacrificing the overall QoS
• Selective triggering
• Turn off consumer-initiated triggering
• Lazy sampling
• Reduce sampling frequency when
• The number of source-initiated triggers in a
  given period is less than a pre-determined value
• The range is relaxed to exceed a certain value

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Simulation Environments

• Request model
• Request arrival as a Poisson distribution
• Request holding time is exponentially distributed
• Traffic model
• Uniform pattern
• Non-uniform pattern
• Mobility model
• Incremental individual mobility model
• High mobility and low mobility
• Four Scenarios

High mobility Low mobility
Uniform traffic HM-UT LM-UT
Non-uniform traffic HM-NUT LM-NUT
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Simulation Objectives

• Analyze the impact of information collection
  mechanisms on the overall resource provisioning
  performance
• Information collection mechanisms
• SS, SR, TR, Gen-ABIC, CDIC, Opt-CDIC
• Resource provisioning algorithm
• CPSS (Comined Path and Server Selection)
• Performance Metrics
• Request completion ratio
• Overhead involved
• Overall efficiency

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Simulation Results (Comparison of SS, SR, TR,
Gen-ABIC under HM-NUT)

• Completion ratio
• Gen-ABIC shows the highest completion ratio
• SS, SR and TR exhibit similar completion ratios
• Overhead
• Increases with the increase of the number of
  requests
• SS introduces the highest overhead, while
  Gen-ABIC has the least overhead
• Overall Efficiency
• Gen-ABIC shows the highest overall efficiency

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Simulation Results (Comparison of SS, SR, TR,
Gen-ABIC under HM-NUT)
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Simulation Results (Comparison of SS, SR, TR,
Gen-ABIC under HM-NUT)
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Simulation Results (Comparison of Gen-ABIC, CDIC
and Opt-CDIC under HM-NUT)

• For completion ratio
• Gen-ABIC is marginally higher than Opt-CDIC, but
  much higher than CDIC
• Decreases with an increase of the number of
  requests in the system
• For overhead
• CDIC is the highest, and Opt-CDIC is the lowest
• For overall efficiency
• Opt-CDIC is the highest

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Simulation Results (Comparison of Gen-ABIC, CDIC
and Opt-CDIC under HM-NUT)
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Simulation Results (Comparison of Gen-ABIC, CDIC
and Opt-CDIC under HM-NUT)
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Simulation Results (Comparison of Gen-ABIC, CDIC
and Opt-CDIC under LM-UT)
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Simulation Results (Comparison of Gen-ABIC, CDIC
and Opt-CDIC under LM-UT)
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Conclusions

• Coarse assignment of collection parameters (e.g.
  SF and R) is adequate to render satisfactory
  completion ratios under most traffic workloads
  and mobility patterns
• Optimization of turning off consumer-initiated
  triggers and lazy sampling help reduce overhead
  to a great extent without lowering the completion
  ratio
• Therefore, Opt-CDIC is a desirable strategy to
  collect network and server information in mobile
  environments

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Future Work

• Enhance AutoSeC for mobile environments by
  integrating Opt-CDIC with the other resource
  provisioning algorithms
• Develop a scalable information collection
  architecture suitable for wide-area environments
  that incorporates distributed directory services