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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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Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
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Fuzzy Decision-Based Efficient Task Offloading Management Scheme in Multi-Tier MEC-Enabled Networks.

Md Delowar Hossain1, Tangina Sultana1, Md Alamgir Hossain1

  • 1Department of Computer Science and Engineering, Kyung Hee University, Global Campus, Yongin-si 17104, Korea.

Sensors (Basel, Switzerland)
|March 6, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces the FTOM system for optimal task offloading in multi-access edge computing (MEC). FTOM enhances 5G network quality of service (QoS) by dynamically selecting servers to reduce latency and task failures.

Keywords:
5Gfuzzy logicmulti-access edge computingorchestratortask offloading

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Area of Science:

  • Computer Science
  • Networking
  • Artificial Intelligence

Background:

  • Multi-access edge computing (MEC) is crucial for 5G networks but faces challenges in dynamic environments.
  • Predicting user demands is difficult, leading to suboptimal server selection, increased task failures, and latency.
  • Existing solutions like vertical offloading overlook underutilized nearby edge servers.

Purpose of the Study:

  • To develop a fuzzy decision-based cloud-MEC collaborative task offloading management system (FTOM).
  • To optimize task offloading by considering server capacity, latency sensitivity, and network conditions.
  • To improve Quality of Service (QoS) in dynamic MEC environments.

Main Methods:

  • Introduction of the FTOM scheme for intelligent task offloading.
  • Dynamic decision-making for offloading tasks to local, nearby MEC, or remote cloud servers.
  • Utilizing fuzzy logic for optimal target node selection based on multiple parameters.

Main Results:

  • FTOM significantly improves task execution success rates and reduces completion time compared to existing schemes.
  • Demonstrated improvements over Local Edge Offloading (LEO), Two-Tier Edge Orchestration (TTEO), Fuzzy Orchestration-based Load Balancing (FOLB), Fuzzy Workload Orchestration-based Task Offloading (WOTO), and Fuzzy Edge-Orchestration based Collaborative Task Offloading (FCTO).
  • Specific improvements include up to 68.5% increase in task success and 66.6% reduction in completion time versus LEO.

Conclusions:

  • The FTOM scheme effectively manages cloud-MEC collaborative task offloading in dynamic environments.
  • FTOM enhances network performance by intelligently distributing tasks based on their requirements and server availability.
  • The proposed system offers a superior approach to task offloading, addressing limitations of previous methods.