Database analytics acceleration using FPGAs
Bharat Sukhwani, Hong Min, et al.
PACT 2012
Cloud computing offers the flexibility to dynamically size the infrastructure in response to changes in workload demand. While both horizontal scaling and vertical scaling of infrastructure are supported by major cloud providers, these scaling options differ significantly in terms of their cost, provisioning time, and their impact on workload performance. Importantly, the efficacy of horizontal and vertical scaling critically depends on the workload characteristics, such as the workload’s parallelizability and its core scalability. In today’s cloud systems, the scaling decision is left to the users, requiring them to fully understand the trade-offs associated with the different scaling options. In this paper, we present our solution for optimizing the resource scaling of cloud deployments via implementation in OpenStack. The key component of our solution is the modeling engine that characterizes the workload and then quantitatively evaluates different scaling options for that workload. Our modeling engine leverages Amdahl’s Law to model service timescaling in scale-up environments and queueing-theoretic concepts to model performance scaling in scale-out environments. We further employ Kalman filtering to account for inaccuracies in the model-based methodology and to dynamically track changes in the workload and cloud environment.
Bharat Sukhwani, Hong Min, et al.
PACT 2012
Andrzej Kochut, Alexei Karve, et al.
IM 2015
Anshul Gandhi, Parijat Dube, et al.
SBAC-PAD 2014
Anshul Gandhi, Sidhartha Thota, et al.
IC2E 2016