Association for Computing Machinery
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 recalibrate 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, the authors propose DejaVu - a framework that minimizes the resource management overhead by identifying a small set of workload classes for which it needs to evaluate resource allocation decisions.