Problem
Research questions and friction points this paper is trying to address.
Energy-performance tradeoff
Data centers
Batch service
Setup times
Queueing model
Energy-performance tradeoffs in server farms with batch services and setup times
🤖 AI Summary
This study addresses the trade-off between energy waste and performance guarantees for idle servers in data centers. To this end, it proposes a batch-service queueing model with setup times (M/M/c/SET-BATCH) that achieves energy savings by processing jobs in batches and optimizing idling strategies. The computational complexity is reduced by exploiting the model's special structure, and its validity is confirmed through steady-state probability analysis and simulations. Furthermore, dynamic batching and delayed shutdown variants are thoroughly investigated. The results reveal the mechanism by which setup times with high coefficients of variation enhance system performance. It is also demonstrated that batching strategies significantly improve performance, and that the optimal policy allows servers to remain briefly idle before being shut down.
📝 Abstract
Data centers consume a large amount of energy, much of which is wasted due to idle servers. Turning off idle servers might be an effective power-saving solution; however, there is a trade-off between energy savings and system performance. Hence, we propose a setup queueing model with a batching policy that allows servers to process a set of jobs simultaneously to minimize power consumption while maintaining acceptable performance. We consider an M/M/c/SET--BATCH queue, a multi-server batch service queue with a fixed batch size and setup times, and some variants, including systems in which idle servers delay before turning off or systems in which the batch size is dynamic. We analyze the steady-state probabilities and system performance of the M/M/c/SET--BATCH system and its variants. Our analysis of the M/M/c/SET--BATCH system with lower computational complexity is made possible by utilizing the special structure of the model. In addition, we use simulations to compare the M/M/c/SET--BATCH model with some other variants with different setup time distributions. The results suggest that the model performs better when the setup time has a larger coefficient of variation. Our results indicate that the batching policy enhances the system performance, especially when we allow servers to be idle before turning them off.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Batch service
Setup queueing model
Energy-performance tradeoff
M/M/c/SET-BATCH
Data centers
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