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Refresh Rate-Based Caching and Prefetching Strategies for Internet of Things Middleware.

Alexey Medvedev1, Alireza Hassani1, Gleb Belov2

  • 1School of Information Technology, Deakin University, Geelong, VIC 3145, Australia.

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Summary
This summary is machine-generated.

This study introduces a novel method for optimizing Internet of Things (IoT) data caching and prefetching. By using Service Level Agreements (SLAs), it minimizes operational costs for Context Management Platforms (CMPs).

Keywords:
Internet of Things (IoT)cachingcontext managementcost efficiencymiddleware platformsoptimal strategyservice level agreement (SLA)

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Area of Science:

  • Computer Science
  • Internet of Things (IoT)
  • Distributed Systems

Background:

  • Context Management Platforms (CMPs) are crucial IoT middleware enabling connectivity across diverse IoT systems.
  • Efficient processing and monetization of IoT data streams are essential for large-scale IoT ecosystems.
  • Caching transient context information incurs significant computational and network costs.

Purpose of the Study:

  • To develop a method for modeling and predicting context metrics and operational costs in IoT data exchange.
  • To propose strategies for minimizing context management costs through efficient caching and prefetching.
  • To maximize the profit of CMP operations under multiple Service Level Agreements (SLAs).

Main Methods:

  • Utilizing Service Level Agreements (SLAs) to quantify service imperfection and link it to costs.
  • Developing mathematical models for context caching and prefetching strategies.
  • Proposing a novel method to determine the optimal rate of IoT data prefetching and caching.

Main Results:

  • Demonstrated that efficient caching and prefetching strategies can significantly minimize context management costs.
  • Showcased how proactive caching strategies maximize CMP profits when multiple SLAs are defined.
  • Achieved a Root Mean Square Percentage Error of up to 0.0016 in profit estimation against simulation results.

Conclusions:

  • The proposed method effectively optimizes IoT data caching and prefetching to reduce operational costs.
  • The mathematical models and strategies presented enable profit maximization for CMPs.
  • The model's validity is confirmed through experimental scenarios with a t-test value tending to 0.