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Related Concept Videos

Scale-Up Processes01:14

Scale-Up Processes

The scale-up of microbial fermentation processes is essential in industrial biotechnology, allowing the transition from laboratory-scale experiments to commercial-scale production while aiming to maintain product yield and quality. This process requires meticulous adjustment of equipment design, process parameters, and contamination control strategies to accommodate increasing culture volumes.At the laboratory scale, cultures are typically maintained in 1 to 10-liter glass or autoclavable...
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Updated: May 16, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Scalable and responsive event processing in the cloud.

Visalakshmi Suresh1, Paul Ezhilchelvan, Paul Watson

  • 1School of Computing Science, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|December 12, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel queueing-theory approach for optimizing event processing response times in cloud environments. It efficiently scales cloud resources to meet performance targets with fluctuating event rates.

Related Experiment Videos

Last Updated: May 16, 2026

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

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Published on: May 15, 2020

Area of Science:

  • Computer Science
  • Cloud Computing
  • Performance Optimization

Background:

  • Event processing requires continuous query evaluation over data streams.
  • Traditional response-time optimization relies on fixed resources and low-level metrics.
  • Cloud computing offers elastic resource provisioning.

Purpose of the Study:

  • To propose a queueing-theory-based method for response-time optimization in cloud event processing.
  • To leverage cloud flexibility for dynamic resource allocation.
  • To meet specified response-time targets under variable event arrival rates.

Main Methods:

  • Modeling query processing engines as atomic units for response-time prediction.
  • Applying M/G/1 queueing systems to model multiple query units on a single node.
  • Using model-based predictions to dynamically acquire and release cloud computing resources.

Main Results:

  • Elimination of intrusive, low-level performance measurements.
  • Achieved portability and scalability of the event processing system.
  • Efficient utilization of cloud resources to meet response-time targets.

Conclusions:

  • The proposed queueing-theory approach effectively optimizes response times in cloud event processing.
  • Dynamic resource scaling based on model predictions ensures performance targets are met.
  • The method offers a scalable and portable solution for fluctuating event streams.