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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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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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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.
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A scalable scheduling and resource management framework for cloud-native B2B applications.

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Summary

This study introduces an Optimized Job Scheduling and Resource Scaling (OJSRS) algorithm to reduce cloud computing queue times and improve resource management. OJSRS enhances job execution efficiency and supports elastic resource provisioning for business applications.

Keywords:
Automated resource scalingBusiness-to-Business applicationsCloud computingCloud native platformDecent work and economic growthJob schedulingLoad balancing

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Area of Science:

  • Cloud Computing
  • Algorithm Design
  • Resource Management

Background:

  • Dynamic workloads in cloud environments necessitate on-demand resource provisioning.
  • Job arrival rate fluctuations cause queue delays, impacting system performance.
  • Existing algorithms often neglect queue delays and flexible resource needs for critical applications.

Purpose of the Study:

  • Propose a novel Optimized Job Scheduling and Resource Scaling (OJSRS) algorithm.
  • Enhance job execution efficiency in cloud environments.
  • Support elastic resource management for dynamic workloads.

Main Methods:

  • Developed the OJSRS algorithm, integrating Tree-based Job Scheduling (TJS) and Automated Resource Scaling and Scheduling (ARSS).
  • TJS maps jobs to suitable Virtual Machines (VMs) hierarchically to minimize queue delays.
  • ARSS dynamically adjusts resource allocation based on workload and policies for adaptive provisioning.

Main Results:

  • OJSRS increased resource utilization by 5-10%.
  • Accelerated job completion times through proactive resource scaling.
  • Demonstrated significant performance improvements for cloud-native applications.

Conclusions:

  • The OJSRS algorithm effectively addresses queue delays and resource provisioning challenges.
  • OJSRS offers a scalable and efficient solution for cloud environments.
  • Improves performance for business-critical cloud applications requiring efficiency and scalability.