Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Distributed Loads01:19

Distributed Loads

528
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.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
528
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

639
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...
639
Local Anesthetics: Differential Sensitivity of Nerve Fibers01:24

Local Anesthetics: Differential Sensitivity of Nerve Fibers

807
Local anesthetics (LAs) block the sodium channels of nerve trunks, sensory nerve endings, and neuromuscular junctions. Although LAs can block all kinds of nerves, the sensitivity of nerve fibers differs according to nerve types and structures. LAs are known to block myelinated fibers faster than unmyelinated ones. Also, they block pain or sensory neurons at low concentrations without affecting the motor neurons involved in muscle contractions. This helps relieve labor pain without affecting the...
807
Storage01:23

Storage

83
A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
83
Lagging Strand Synthesis01:59

Lagging Strand Synthesis

50.9K
During replication, the complementary strands in double-stranded DNA are synthesized at different rates. Replication first begins on the leading strand. Replication starts later, occurs more slowly, and proceeds discontinuously on the lagging strand.
There are several major differences between synthesis of the leading strand and synthesis of the lagging strand. 1) Leading strand synthesis happens in the direction of replication fork opening, whereas lagging strand synthesis happens in the...
50.9K
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

613
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
613

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

DeepCMS: A Feature Selection-Driven Model for Cancer Molecular Subtyping with a Case Study on Testicular Germ Cell Tumors.

Diagnostics (Basel, Switzerland)·2025
Same author

Characterization of CO<sub>2</sub> Adsorption Behavior in Pyrolyzed Shales for Enhanced Sequestration Applications.

Molecules (Basel, Switzerland)·2025
Same author

Enhancing security in financial transactions: a novel blockchain-based federated learning framework for detecting counterfeit data in fintech.

PeerJ. Computer science·2024
Same author

Evaluating Federated Learning Simulators: A Comparative Analysis of Horizontal and Vertical Approaches.

Sensors (Basel, Switzerland)·2024
Same author

Multi-Objective Task-Aware Offloading and Scheduling Framework for Internet of Things Logistics.

Sensors (Basel, Switzerland)·2024
Same author

A Novel Markov Model-Based Traffic Density Estimation Technique for Intelligent Transportation System.

Sensors (Basel, Switzerland)·2023
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jun 21, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

531

Latency-Sensitive Function Placement among Heterogeneous Nodes in Serverless Computing.

Urooba Shahid1,2, Ghufran Ahmed1, Shahbaz Siddiqui1

  • 1Department of Computer Science, National University of Computer and Emerging Sciences, Karachi 75030, Pakistan.

Sensors (Basel, Switzerland)
|July 13, 2024
PubMed
Summary
This summary is machine-generated.

Function as a Service (FaaS) offers adaptable smart city solutions. This study introduces an adaptive machine learning model to optimize FaaS placement, improving resource utilization and deadline adherence.

Keywords:
clouddistributededgehierarchicalmachine learningmulti-tierserver less

More Related Videos

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
10:15

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

Published on: February 3, 2021

3.8K
Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.0K

Related Experiment Videos

Last Updated: Jun 21, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

531
Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem
10:15

Integration of 5G Experimentation Infrastructures into a Multi-Site NFV Ecosystem

Published on: February 3, 2021

3.8K
Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
07:49

Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

Published on: November 26, 2019

8.0K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Smart City Technologies

Background:

  • Function as a Service (FaaS) provides serverless computing, simplifying infrastructure management for developers.
  • FaaS integration with the Internet of Things (IoT) enables event-driven actions and real-time computations, crucial for smart cities.
  • Optimizing function placement in FaaS is critical for meeting performance requirements like deadlines and efficient resource use.

Purpose of the Study:

  • To develop and evaluate a likelihood-based adaptive machine learning model for optimal FaaS function placement.
  • To enhance resource utilization and ensure deadline compliance in FaaS deployments within smart city contexts.
  • To address the challenges of network latency and computational demands in distributed FaaS environments.

Main Methods:

  • Employed an adaptive machine learning model utilizing XGBoost regressor for execution time estimation and decision tree regressor for network latency prediction.
  • Incorporated factors such as network delay, arrival computation, and resource emphasis into the machine learning model for placement decisions.
  • Utilized Docker containers for replication, focusing on serverless node types, function location, deadlines, and edge-cloud topology.

Main Results:

  • The proposed machine learning model effectively aids in selecting the optimal placement for FaaS functions.
  • Effective resource utilization was demonstrated to directly correlate with enhanced deadline compliance.
  • The research validates the benefits of FaaS in smart city infrastructure through optimized placement strategies.

Conclusions:

  • The adaptive machine learning model provides an effective solution for optimizing FaaS function placement in smart cities.
  • Efficient resource management and meeting strict deadlines are achievable through intelligent FaaS deployment strategies.
  • This research contributes to the advancement of serverless computing for scalable and responsive smart city applications.