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In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...
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Related Experiment Video

Updated: Aug 25, 2025

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
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Framework for Efficient Auto-Scaling of Virtual Network Functions in a Cloud Environment.

Saima Zafar1, Usman Ayub1, Hend I Alkhammash2

  • 1Department of Electrical Engineering, National University of Computer and Emerging Sciences, FAST-NU Lahore Campus, Faisal Town B Block, Lahore 54700, Pakistan.

Sensors (Basel, Switzerland)
|October 14, 2022
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Summary
This summary is machine-generated.

This study introduces a framework using Gnocchi, a time-series database, for efficient auto-scaling of Virtual Network Functions (VNFs) in Network Function Virtualization (NFV). Results show improved performance and reduced resource usage compared to legacy systems.

Keywords:
Quality of Service (QoS)auto-scalingnetwork virtualizationperformance of systems

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Area of Science:

  • Cloud Computing
  • Network Engineering
  • Database Management

Background:

  • Network Function Virtualization (NFV) decouples network functions from hardware, enabling dynamic resource allocation for Virtual Network Functions (VNFs).
  • Efficient auto-scaling and load management of VNFs require a robust cloud-based metering component for processing performance metrics.
  • Existing solutions face challenges in storing, indexing, and retrieving time-series data for real-time VNF scaling decisions.

Purpose of the Study:

  • To propose and validate an integrating framework for efficient VNF auto-scaling using Gnocchi, a time-series database.
  • To evaluate the framework's efficacy based on aggregated data points, database size, data recovery speed, and memory consumption.
  • To demonstrate significant improvements over traditional Ceilometer configurations for VNF auto-scaling.

Main Methods:

  • Developed an integrating framework for VNF auto-scaling incorporating Gnocchi for time-series data management.
  • Deployed a functional cloud environment utilizing OpenStack components to implement the Network Function Virtualization (NFV) architecture.
  • Conducted a detailed empirical analysis comparing the Gnocchi-based framework against the legacy Ceilometer configuration.

Main Results:

  • The Gnocchi-based framework achieved a lower metering storage size.
  • Demonstrated reduced memory utilization for processing and managing VNF metrics.
  • Showcased a reduced time delay in retrieving monitoring data for auto-scaling alarms.

Conclusions:

  • The proposed framework effectively utilizes Gnocchi for efficient auto-scaling of VNFs in NFV environments.
  • Employing Gnocchi leads to substantial improvements in resource efficiency and data retrieval performance.
  • This approach offers a viable solution for enhancing the scalability and management of virtualized network functions.