DejaVu: Accelerating Resource Allocation in Virtualized Environments

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. Dur-ing the adaptation period, the system may run with an inefficient configuration. In this paper, we propose DejaVu – a framework that (1) mini-mizes the resource management overhead by identifying a small set of workload classes for which it needs to evaluate resource alloca-tion decisions, (2) quickly adapts to workload changes by classify-ing 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 represen-tative network services on Amazon EC2. DejaVu achieves more than 10x speedup in adaptation time for each workload change rel-ative 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 instrumen-tation or human intervention