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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

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Summary
This summary is machine-generated.

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.