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Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
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Dynamically Controlling Offloading Thresholds in Fog Systems.

Faten Alenizi1, Omer Rana1

  • 1School of Computer Science and Informatics, Cardiff University, Cardiff CF24 3AA, UK.

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Summary
This summary is machine-generated.

This study introduces a dynamic offloading scheme for fog computing (FC) to enhance Internet of Things (IoT) task processing. The proposed system significantly reduces latency and energy consumption while improving throughput.

Keywords:
computational offloadingdynamic offloading thresholdfog computingmaximizing throughputsminimizing delayminimizing energy consumptionresource management

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Area of Science:

  • Computer Science
  • Networking
  • Distributed Systems

Background:

  • Cloud computing faces latency, location awareness, and security challenges for Internet of Things (IoT) tasks due to server distance.
  • Fog computing (FC) addresses these issues but requires efficient resource management and computational offloading for optimal performance.

Purpose of the Study:

  • To propose a dynamic, online offloading scheme for delay-sensitive tasks in fog computing environments.
  • To introduce a fog node architecture with dynamically adjustable offloading thresholds.

Main Methods:

  • Development of two algorithms: dynamic task scheduling (DTS) and dynamic energy control (DEC).
  • Implementation of a fog node architecture that dynamically adjusts task offloading criteria.
  • Evaluation of the scheme against existing benchmarks for performance metrics.

Main Results:

  • Reduced latency by up to 95% compared to benchmarks.
  • Improved throughput by up to 71%.
  • Decreased energy consumption at the fog layer by up to 67%.

Conclusions:

  • The proposed dynamic offloading scheme effectively optimizes fog computing resource utilization.
  • The architecture enhances fog node performance by minimizing delay and energy usage while maximizing throughput for IoT tasks.