activity
20172019
most citedLow-Level Augmented Bayesian Optimization for Finding the Best Cloud VM

7 citations · 13 across the 2 of their papers we have counts for

collaborators

7 papers

cs.SE2019

Whence to Learn? Transferring Knowledge in Configurable Systems using BEETLE

Rahul Krishna, Vivek Nair, Pooyan Jamshidi +1

As software systems grow in complexity and the space of possible configurations increases exponentially, finding the near-optimal configuration of a software system becomes challen…

cs.AI2018

Is One Hyperparameter Optimizer Enough?

Huy Tu, Vivek Nair

Hyperparameter tuning is the black art of automatically finding a good combination of control parameters for a data miner. While widely applied in empirical Software Engineering, t…

cs.DC2018

Micky: A Cheaper Alternative for Selecting Cloud Instances

Chin-Jung Hsu, Vivek Nair, Tim Menzies +1

Most cloud computing optimizers explore and improve one workload at a time. When optimizing many workloads, the single-optimizer approach can be prohibitively expensive. Accordingl…

cs.SE2018

Transfer Learning with Bellwethers to find Good Configurations

Vivek Nair, Rahul Krishna, Tim Menzies +1

As software systems grow in complexity, the space of possible configurations grows exponentially. Within this increasing complexity, developers, maintainers, and users cannot keep…

cs.DC2018

Scout: An Experienced Guide to Find the Best Cloud Configuration

Chin-Jung Hsu, Vivek Nair, Tim Menzies +1

Finding the right cloud configuration for workloads is an essential step to ensure good performance and contain running costs. A poor choice of cloud configuration decreases applic…

cs.DC20177 cited

Low-Level Augmented Bayesian Optimization for Finding the Best Cloud VM

Chin-Jung Hsu, Vivek Nair, Vincent W. Freeh +1

With the advent of big data applications, which tends to have longer execution time, choosing the right cloud VM to run these applications has significant performance as well as ec…