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Performance-Cost Trade-Off in Auto-Scaling Mechanisms for Cloud Computing.

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Summary

This study models cloud auto-scaling using stochastic Petri nets and GRASP optimization to balance performance and cost. It helps find optimal configurations for service level agreements (SLAs) and budgets.

Keywords:
auto-scalingcloud computingcost evaluationoptimizationperformance evaluationstochastic Petri net

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Area of Science:

  • Computer Science
  • Cloud Computing
  • Performance Optimization

Background:

  • Cloud computing offers utility-based resource consumption, with elasticity via auto-scaling being a key feature.
  • Critical systems require adherence to Service Level Agreements (SLAs), such as response time limits.
  • Optimizing cloud configurations for cost and SLA compliance is complex due to numerous variables.

Purpose of the Study:

  • To propose a method for modeling cloud auto-scaling mechanisms.
  • To discover trade-offs between cloud service performance and cost.
  • To identify economic configurations that meet SLA and budget constraints.

Main Methods:

  • Modeling auto-scaling mechanisms using stochastic Petri nets (SPN).
  • Employing the GRASP (Generalized הרב-search) adaptive search metaheuristic for optimization.
  • Validating the SPN model against a real-world test-bed scenario.

Main Results:

  • SPN models allow estimation of cloud service metrics (configuration, cost, response time, throughput) against SLAs.
  • The auto-scaling SPN model achieved 95% confidence validation against a real test-bed.
  • The GRASP algorithm successfully identified economic system configurations meeting SLA and budget constraints.

Conclusions:

  • The proposed SPN modeling and GRASP optimization effectively address the complexity of cloud auto-scaling.
  • This approach aids in finding optimized-quality solutions for cloud service operational management.
  • It enables practical identification of cost-effective cloud service configurations.