Effective resource management of virtualized environments is a challenging task. State-of-the-art management systems either rely on analytical models or evaluate resource allocations by running actual experiments. However, both approaches incur a significant overhead once the workload changes. The former needs to re-calibrate and re-validate models, whereas the latter has to run a new set of experiments to select a new resource allocation. During the adaptation period, the system may run with an inefficient configuration. In this paper, we propose DejaVu - a framework that (1) minimizes the resource management overhead by identifying a small set of workload classes for which it needs to evaluate resource allocation decisions, (2) quickly adapts to workload changes by classifying workloads using signatures and caching their preferred resource allocations at runtime, and (3) deals with interference by estimating an "interference index". We evaluate DejaVu by running representative network services on Amazon EC2. DejaVu achieves more than 10x speedup in adaptation time for each workload change relative to the state-of-the-art. By enabling quick adaptation, DejaVu saves up to 60% of the service provisioning cost. Finally, DejaVu is easily deployable as it does not require any extensive instrumentation or human intervention.

The power consumption of the Internet and datacenter networks is already significant, and threatens to shortly hit the power delivery limits while the hardware is trying to sustain ever-increasing traffic requirements. Existing energy-reduction approaches in this domain advocate recomputing network configuration with each substantial change in demand. Unfortunately, computing the minimum network subset is computationally hard and does not scale. Thus, the network is forced to operate with diminished performance during the recomputation periods. In this paper, we propose REsPoNse, a framework which overcomes the optimality-scalability trade-off. The insight in REsPoNse is to identify a few energy-critical paths off-line, install them into network elements, and use a simple online element to redirect the traffic in a way that enables large parts of the network to enter a low-power state. We evaluate REsPoNse with real network data and demonstrate that it achieves the same energy savings as the existing approaches, with marginal impact on network scalability and application performance.