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Edge Computing Resource Allocation for Dynamic Networks: The DRUID-NET Vision and Perspective.

Dimitrios Dechouniotis1, Nikolaos Athanasopoulos2, Aris Leivadeas3

  • 1National Technical University of Athens - NTUA, 15780 Zografou, Greece.

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|April 17, 2020
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Summary
This summary is machine-generated.

The DRUID-NET framework addresses limited node capacity in the Internet of Things (IoT) by dynamically distributing resources. This enhances performance for time-critical tasks through a holistic, multidisciplinary approach.

Keywords:
control co-designedge computinginternet of thingsmobile robotsresource allocation

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Limited computational capacity of local nodes hinders the potential of the Internet of Things (IoT).
  • Key challenges include optimally exploiting network, computing, and storage resources for time-critical tasks.
  • Existing solutions often fail to address dynamic resource demands and service differentiation.

Purpose of the Study:

  • To propose the DRUID-NET framework for dynamic resource distribution in the IoT.
  • To develop novel resource allocation mechanisms ensuring Quality of Service (QoS) metrics.
  • To create a holistic, multidisciplinary approach integrating fragmented research.

Main Methods:

  • Analytic dynamical modeling of resources, workload, and networking environments.
  • Development of new estimators for time-varying profiles in wireless communications and mobile edge computing.
  • Integration of Automata and Graph theory, Machine Learning, Modern Control Theory, and Network Theory.

Main Results:

  • The DRUID-NET framework enables dynamic resource distribution for rapidly varying demands.
  • Novel resource allocation mechanisms incorporate service differentiation and context-awareness.
  • The framework explicitly includes resource allocation in the decision strategy, advancing control algorithms.

Conclusions:

  • DRUID-NET offers a holistic solution to optimize resource utilization in the IoT.
  • The framework guarantees well-defined QoS metrics for time-critical and mission-critical applications.
  • This multidisciplinary approach bridges gaps between different research communities, advancing the field.