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Related Concept Videos

Singularity Functions for Shear01:26

Singularity Functions for Shear

148
In structural analysis, singularity functions are crucial in simplifying the representation of shear forces in beams under discontinuous loading. These functions describe discontinuous  variations in shear force across a beam with varying loads by using a single mathematical expression, regardless of the complexity of the loading conditions. The singularity functions are derived from creating a free-body diagram of the beam and then making conceptual cuts at specific points to examine the...
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Controller Configurations01:22

Controller Configurations

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Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller...
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Deflection of a Beam01:19

Deflection of a Beam

284
Accurately determining beam deflection and slope under various loading conditions in structural engineering is crucial for ensuring safety and structural integrity. Singularity functions offer a streamlined approach to analyzing beams, especially when multiple loading functions complicate the bending moment equation.
Singularity functions, described in an earlier lesson, are powerful mathematical tools that represent discontinuities within a function commonly encountered in structural loading...
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Elasticity01:12

Elasticity

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Elasticity is the ability of an object to withstand the effects of distortion and to return to its original size and shape once the forces causing deformation are removed. When an elastic material deforms under the action of an external force, it experiences internal resistance to the deformation. However, if no external force is applied, it returns to its original state.
The elasticity of an object can be described by a stress-strain curve, which represents the relationship between stress...
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Lift01:23

Lift

180
Lift is a fundamental aerodynamic force that acts perpendicular to the direction of airflow. It plays a central role in achieving and sustaining flight and in stabilizing various vehicles. Lift primarily originates from pressure differences created across surfaces, such as an airfoil. A lower pressure region forms above the wing, while a higher pressure region forms below it, generating an upward force. This differential results from the shape and orientation of the airfoil, enabling the wing...
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Midrange01:07

Midrange

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A somewhat easy to compute quantitative estimate of a data set’s central tendency is its midrange, which is defined as the mean of the minimum and maximum values of an ordered data set.
Simply put, the midrange is half of the data set’s range. Similar to the mean, the midrange is sensitive to the extreme values and hence the prospective outliers. However, unlike the mean, the midrange is not sensitive to all the values of the data set that lie in the middle. Thus, it is prone to...
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A Time-Driven Cloudlet Placement Strategy for Workflow Applications in Wireless Metropolitan Area Networks.

Sensors (Basel, Switzerland)·2022
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FireFace: Leveraging Internal Function Features for Configuration of Functions on Serverless Edge Platforms.

Ming Li1,2,3, Jianshan Zhang4, Jingfeng Lin1,2,3

  • 1College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China.

Sensors (Basel, Switzerland)
|September 28, 2023
PubMed
Summary

FireFace optimizes serverless edge computing by predicting function execution times and using APSO-GA to select cost-effective resource configurations, reducing expenses by up to 44.8%. This adaptive approach minimizes financial overhead while meeting service level objectives (SLOs).

Keywords:
SLOconfiguration optimizationfunction as a serviceserverless computing

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Area of Science:

  • Computer Science
  • Distributed Computing
  • Cloud Computing

Background:

  • Serverless computing is a popular cloud application deployment model, abstracting infrastructure management.
  • Existing serverless resource configuration methods rely on historical data or interpolation, which are inefficient for edge platforms.
  • Serverless edge platforms face challenges with resource heterogeneity and higher overhead, increasing costs for developers.

Purpose of the Study:

  • To propose an adaptive and efficient approach, FireFace, for optimizing serverless edge computing resource configurations.
  • To minimize financial overhead for developers while ensuring Service Level Objectives (SLOs) are met.
  • To address the challenges of resource heterogeneity and dynamic environments in serverless edge platforms.

Main Methods:

  • Developed a prediction module to forecast function execution times based on internal function features and configuration schemes.
  • Implemented a decision module utilizing the Adaptive Particle Swarm Optimization and Genetic Algorithm Operator (APSO-GA) algorithm.
  • The decision module analyzes environment information to select optimal configurations for CPU, memory, and edge platforms.

Main Results:

  • The prediction model achieved optimal results across all metrics with a prediction error rate of 4.25%–9.51% for real-world serverless applications.
  • FireFace achieved average cost savings of 7.2%–44.8% compared to classic algorithms by finding optimal resource configurations.
  • The approach demonstrated rapid adaptability, efficiently adjusting resource allocation in dynamic environments.

Conclusions:

  • FireFace effectively minimizes financial overhead in serverless edge computing while satisfying SLOs.
  • The proposed method offers significant cost savings and improved efficiency over existing solutions.
  • FireFace provides a robust and adaptable solution for the complexities of serverless edge resource management.