In this paper, we show that service demands provide significant benefits for implementing modern auto-scalers. In a system under load, due to measurement interference, service demands normally cannot be measured directly, however, a number of estimation approaches exist based on high-level performance metrics. %X In the context of performance models, service demands are key model parameters capturing the average time individual requests of different workload classes are actively processed.
%T On the Value of Service Demand Estimation for Auto-Scaling %B Proceedings of 19th International GI/ITG Conference on Measurement, Modelling and Evaluation of Computing Systems (MMB 2018) Our results encourage further research on the application of service demand estimates for resource management in data = , Our results show that the service demand-based auto-scaler outperforms the CPU utilization-based one in all scenarios. This is shown by testing two approaches in three different scenarios. We demonstrate that even a simple auto-scaler that leverages information about service demands significantly outperforms auto-scalers solely based on CPU utilization measurements. Auto-scaling describes the process of dynamically adjusting the number of allocated virtual resources (e.g., virtual machines) in a data center according to the incoming workload.
In the context of performance models, service demands are key model parameters capturing the average time individual requests of different workload classes are actively processed. Chameleon - Chair of Computer Science II - Software Engineering You no longer have to worry about Windows updates or that shutting down your computer crashed. designed for Windows and works with VMware Workstation Pro 16 and VMware Player 16. Top Software Keywords Show more Show less
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