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Dosage Compensation02:50

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In animals, gender is determined by the number and type of sex chromosome. For example, human females have two X chromosomes, and males have one X and one Y chromosome, whereas C.elegans with one X chromosome is a male, and the one with two X chromosomes is a hermaphrodite.
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Levels of Organization01:09

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This lesson defines the leveling effect in acidic and basic solutions and its role in aqueous and non-aqueous solutions. It is essential to understand the competing nature of various species in a chemical system.
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Related Experiment Video

Updated: Jan 22, 2026

High-density Electroencephalographic Acquisition in a Rodent Model Using Low-cost and Open-source Resources
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A Micro-Level Compensation-Based Cost Model for Resource Allocation in a Fog Environment.

Sudheer Kumar Battula1, Saurabh Garg2, Ranesh Kumar Naha3

  • 1Discipline of ICT, School of Technology, Environment and Design (TED), University of Tasmania, Hobart, TAS 7005, Australia. sudheerkumar.battula@utas.edu.au.

Sensors (Basel, Switzerland)
|July 7, 2019
PubMed
Summary

This study introduces a new cost model and resource allocation method for fog computing, ensuring fair compensation for providers and reduced costs for users. The proposed approach enhances system performance and lowers application processing expenses.

Keywords:
IoTcost modelfog computingmatching theoryresource allocation

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Area of Science:

  • Computer Science
  • Distributed Systems
  • Edge Computing

Background:

  • Fog computing utilizes edge resources for low-latency, scalable applications.
  • Current fog resource allocation often overlooks provider compensation and accurate cost estimation.
  • Existing cloud cost models are inadequate for heterogeneous fog environments.

Purpose of the Study:

  • To develop a micro-level compensation cost model for fog computing resources.
  • To propose a novel resource allocation method integrating cost estimation and provider compensation.
  • To enhance efficiency and cost-effectiveness in fog-based service delivery.

Main Methods:

  • Development of a micro-level compensation cost model tailored for fog environments.
  • Design of a new resource allocation algorithm based on the proposed cost model.
  • Experimental evaluation comparing the new algorithm against the best-fit algorithm.

Main Results:

  • The proposed resource allocation method demonstrates superior performance compared to the best-fit algorithm.
  • Application processing costs are significantly reduced using the new cost-aware allocation strategy.
  • The model effectively balances resource allocation efficiency with provider compensation.

Conclusions:

  • The novel cost model and resource allocation method address critical gaps in fog computing.
  • The approach benefits both resource providers through compensation and users via cost reduction.
  • This work offers a more efficient and economically viable solution for fog resource management.