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in Real-Time Reliable Edge Computing for Internet of Things + Chao - - PowerPoint PPT Presentation

Adaptive Data Replication in Real-Time Reliable Edge Computing for Internet of Things + Chao Wang, * Christopher Gill, * Chenyang Lu + Department of Computer Science and Information Engineering, National Taiwan Normal University * Department of


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SLIDE 1

Adaptive Data Replication in Real-Time Reliable Edge Computing for Internet of Things

+Chao Wang, *Christopher Gill, *Chenyang Lu + Department of Computer Science and Information Engineering, National Taiwan Normal University * Department of Computer Science and Engineering, Washington University in St. Louis, USA

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Challenges in performant IoT edge computing

  • Reliable and timely computing

at a resource-constraint network waist

Example: structural health inference and control

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an IoT gateway

  • Single point of failure
  • Traffic congestion and delay
  • Limited network bandwidth
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SLIDE 3

Specific IoT gateway requirements

  • Quantitative requirements
  • Data subscriber cannot accept more than

Li consecutive losses for data topic i

  • Data subscriber imposes an end-to-end

soft deadline for data

  • Qualitative requirements
  • The gateway should not consume too

much local network bandwidth

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System model

  • Publish-subscribe data model
  • with a minimum inter-publishing

time for each topic

  • Each embedded sensing device can
  • nly keep Ni latest data elements
  • Primary-backup fault tolerance
  • consider the crash failure (fail-stop)
  • data replication to backup

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Networked Cyber-Physical Systems Laboratory

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Key idea for adaptive data replication

  • In the IoT gateway, once data is processed/delivered, it is irrelevant
  • Therefore, for each data, we may postpone replication activities to

reduce the need of actually performing the replication!

  • Only start to replicate an arrival of data at the last starting time
  • Once start, perform a batch of pending replications

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SLIDE 6

Adaptive data replication architecture

  • Edge computing engine

schedules both computing tasks and replication tasks using the EDF policy

  • Replication handler decides

the intended rate of replication:

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Lower intended rate -> tighter replication deadline

(see the paper for the analysis)

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SLIDE 7

Empirical performance: efficiency in network bandwidth usage

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Networked Cyber-Physical Systems Laboratory

88% saving in bandwidth. A higher intended replication rate is preferred, because it permits a longer replication deadline, which in turn would allow the system to skip many more replication activities.

(Payload size = 512 bytes)

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Concluding remarks

  • Performant IoT edge computing is challenging
  • Reliability, timeliness, and resource constraints
  • In this work we addressed the challenges for the case of IoT gateways
  • Key idea: adaptive data replication
  • Our empirical validation shows that the proposed architecture can
  • meet the required levels of data-loss tolerance
  • save network bandwidth consumption
  • meet application-specific end-to-end deadlines
  • Email us for further discussion: cw@ntnu.edu.tw

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Networked Cyber-Physical Systems Laboratory