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

Optimization Problems01:26

Optimization Problems

102
Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...
102
Maximum Size of Aggregate01:12

Maximum Size of Aggregate

606
The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can...
606
Distributed Loads01:19

Distributed Loads

1.0K
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...
1.0K
Buffers: Buffer Capacity01:09

Buffers: Buffer Capacity

2.6K
Buffer capacity is the quantitative measure of a buffer to resist the change in pH. As shown in the following equation, the buffer capacity, denoted by 'beta', is expressed as the number of moles of acid or base needed to change the pH of a one-liter buffer solution by 1 unit. Here, Ca and Cb indicate the number of moles of acid and base, respectively. Note that dpH represents the change in pH.
In the graph, pH is plotted as a function of the number of moles of base (Cb) added to a weak...
2.6K
Deleterious Substances in Aggregate01:25

Deleterious Substances in Aggregate

622
Deleterious substances in aggregates can be detrimental to the quality and durability of concrete. These substances include organic impurities like loam, which interfere with cement hydration and are usually present in the sand. These prevent a good bond between aggregate and cement paste. Organic impurities can be detected using the colorimetric test, where the darkness of a solution after agitation indicates the level of organic content.
Another type of impurity is clay and fine material that...
622
Storage01:23

Storage

440
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...
440

You might also read

Related Articles

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

Sort by
Same author

A web-based semi-supervised deep learning platform for automated AS-OCT assessment and monitoring of infectious keratitis.

NPJ digital medicine·2026
Same author

A blood-biomarker based CTC-ALRI score predicts recurrence in hepatocellular carcinoma patients following curative resection.

Frontiers in oncology·2026
Same author

Validated semi-supervised early and accurate screening for anterior segment diseases: a 3PM-guided conceptual and technological innovation.

The EPMA journal·2026
Same author

A retrospective outcomes study 25-gauge 10,000 CPM beveled-tip and 25-gauge flat-tip microincision vitrectomy for proliferative diabetic retinopathy treatment.

Frontiers in medicine·2025
Same author

A Generalized and Interpretable Multi-Label Multi-Disease Screening System for Ocular Anterior Segment Disease Detection.

Ophthalmology science·2025
Same author

Aberrant activation of the PI3K/AKT/HIF‑1α pathway promotes glycolysis and lenvatinib resistance in liver cancer.

Molecular medicine reports·2025
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: Mar 3, 2026

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
11:05

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Published on: December 13, 2016

12.7K

Static Memory Deduplication for Performance Optimization in Cloud Computing.

Gangyong Jia1, Guangjie Han2, Hao Wang3

  • 1Department of Computer Science and Technology, Hangzhou Dianzi University, No. 1108, Street 1, Xiasha, Hangzhou 310018, China. gangyong@hdu.edu.cn.

Sensors (Basel, Switzerland)
|April 28, 2017
PubMed
Summary
This summary is machine-generated.

Static memory deduplication (SMD) reduces cloud memory demand by sharing pages offline. This technique optimizes performance and minimizes response time costs for virtual machines (VMs) in cloud computing.

Keywords:
cloud computingmain memorymemory deduplicationperformancevirtualization

Related Experiment Videos

Last Updated: Mar 3, 2026

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
11:05

Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

Published on: December 13, 2016

12.7K

Area of Science:

  • Computer Science
  • Cloud Computing
  • Virtualization

Background:

  • Growing number of virtual machines (VMs) and applications increase memory demand and energy consumption in cloud environments.
  • Insufficient memory capacity presents a significant bottleneck for cloud scalability and virtualization performance.
  • Existing memory deduplication techniques reduce memory demand via page sharing but incur online comparison overheads.

Purpose of the Study:

  • To propose a static memory deduplication (SMD) technique for cloud computing.
  • To reduce memory capacity requirements and optimize performance in virtualized environments.
  • To address the overheads associated with online memory deduplication methods.

Main Methods:

  • Developed a static memory deduplication (SMD) technique.
  • Performed page detection and comparison offline, focusing on the code segment for maximum sharing.
  • Evaluated SMD's impact on memory capacity and performance metrics.

Main Results:

  • SMD effectively reduces memory capacity requirements in cloud environments.
  • The proposed technique demonstrates performance improvements.
  • Experimental results show negligible impact on response time compared to other approaches.

Conclusions:

  • Static memory deduplication (SMD) is an efficient method for reducing memory demand in cloud computing.
  • SMD offers performance optimization with minimal overhead, particularly in response time.
  • The offline approach of SMD addresses limitations of traditional online memory deduplication techniques.