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Resolving QoS Policy Configuration Challenges for
Publish/Subscribe Middleware Platforms
AFRL JBI PI Meeting
Outline
• Motivating Example
• QoS Policy Configuration Challenges
• Evaluating Common Solutions
• DQML Overview
Motivating Example – Unmanned Aerial Vehicle (UAV)
• UAV Desirable Data Flows– Storage and dissemination of
data for late arriving subscribers (e.g., other aircraft and satellites coming into range)
– Reliable communication of critical data (e.g., sensor data to satellite to ground station)
– Prioritization of data delivery for mission-critical or high value data (e.g., time-sensitive targets)
– Ordered and/or grouped data dissemination to ensure ordering and appropriate level of granularity
QoS Policy Configuration Challenges (1 of 3)
•QoS Policy Compatibility– QoS policies for the
communicating entities must be compatible between what’s requested and offered
Best effort data transfer
offered
Reliable data transfer
requested
XData will not be transferred– For example, if subscriber
requests reliable data transfer the publisher can not offer best effort data transfer
QoS Policy Configuration Challenges (2 of 3)
•QoS Policy Consistency– QoS policies for any one
entity must be consistent with each other Deadline’s period
= 5 ms.
Time based filter’s minimum separation =
10 ms.
X
QoS policies will not be set
– For example, deadline period must be >= minimum separation for time based filter
QoS Policy Configuration Challenges (3 of 3)
•Accurate QoS Policy Configuration Transformation– Propagating QoS policy
configuration through development stages
– For example, transforming design of QoS configuration into application code
Evaluating Common Solutions
•Three Common Solutions to Challenges– Point-based solutions
– Patterns-based solutions– Domain specific modeling
language (DSML) based solutions
•Categorized by – Robust vs. non-robust
solution documentation– Robust vs. non-robust
solution implementation
Point-based Solutions
• Focused on particular solution/application– QoS policy configuration is
developed• Configuration is designed
• Cons –Non-robust solution
documentation–Non-robust solution
implementation
– Application is compiled & run
– Feedback is gathered & evaluated
– Process is repeated as necessary
DesignCode
TestEvaluate
• Pros –Low overhead
• Configuration is implemented
Patterns-based Solutions
• Enables codification of configuration expertise
• Cons –No help with
solution implementation (i.e., non-robust)
• Pros –Reuse of expert
configuration design knowledge (i.e., robust design)
Continuous Data Pattern
Application A Application B Application C
• Documented use of QoS policies for– Dataflow management– Dataflow prioritization– Dataflow shaping
DSML-based Solutions
• Uses domain specific terminology and constructs
• Cons –Learning
curve/training overhead
• Pros –Robust solution
documentation (via metamodel constraints)
–Robust solution implementation (via interpreters)
Application A Application B Application C
Metamodel
QoSConfig
Application model
QoSConfig
• Metamodel created in metamodeling tool
QoS Config
DDS QoS Modeling Language (DQML 1 of 2)
• Models relevant DDS entities
• Models DDS QoS polices
• Models relationships between entities and QoS policies