Energy-aware server provisioning and load dispatching for connection-intensive internet services
NSDI'08 Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation
The cost of a cloud: research problems in data center networks
ACM SIGCOMM Computer Communication Review
Optimality analysis of energy-performance trade-off for server farm management
Performance Evaluation
Power management of online data-intensive services
Proceedings of the 38th annual international symposium on Computer architecture
Defragmenting the cloud using demand-based resource allocation
Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems
On applying stochastic network calculus
Frontiers of Computer Science: Selected Publications from Chinese Universities
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Energy consumption imposes a significant cost for data centers; yet much of that energy is used to maintain excess service capacity during periods of predictably low load. Resultantly, there has recently been interest in developing designs that allow the service capacity to be dynamically resized to match the current workload. However, there is still much debate about the value of such approaches in real settings. In this paper, we show that the value of dynamic resizing is highly dependent on statistics of the workload process. In particular, both slow time-scale non-stationarities of the workload (e.g., the peak-to-mean ratio) and the fast time-scale stochasticity (e.g., the burstiness of arrivals) play key roles. To illustrate the impact of these factors, we combine optimization-based modeling of the slow time-scale with stochastic modeling of the fast time scale. Within this framework, we provide both analytic and numerical results characterizing when dynamic resizing does (and does not) provide benefits.