Cloud Capacity Planning Based on Simulation and Genetic Algorithms

The maturity of heterogeneous and hybrid public Cloud environments enables service providers to deploy there their complex IT services trusting these large and complex infrastructures. At the same time, evaluating the impact of changes at service configuration before and at the runtime is still a very challenging and difficult task. Moreover, a comprehensive performance evaluation of IT service configurations should not be limited just to costs for IT resource acquisition, but also include risk related elements such as Service Level Agreement (SLA) violation penalties and other intangibles. To support IT service providers in this difficult task, we developed Business-Driven Management as a Service Plus (BDMaaS+), a novel decision support tool that can evaluate IT service configuration through simulation with realistic service and network models. By allowing service providers to define expanded operational parameters, BDMaaS+ also enables what-if scenario analysis, thereby opening interesting possibilities at the planning level. Experimental results, collected from our thorough evaluations, demonstrate how a service provider can leverage BDMaaS+ to explore the potential of high-level business SLA changes and data center additions.

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Cloud Capacity Planning Based on Simulation and Genetic Algorithms

Cited by 2 publications

References 12 publications

Multi-Objective Emperor Penguin Optimizer for Tuning the Quality-of-Service in Cloud Computing

Multi-Objective Emperor Penguin Optimizer for Tuning the Quality-of-Service in Cloud Computing

BDMaaS+: Business-Driven and Simulation-Based Optimization of IT Services in the Hybrid Cloud

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