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Lynceus: Tuning and Provisioning Data Analytic Jobs on a Budget
Lynceus implements a new budget-aware approach that builds the performance model of the target job by profiling the job on the best set of cloud/parameter configurations possible given constraints of both quality of service and monetary nature, and can consistently identify better (i.e., less expensive) configurations. Expand
Lynceus: Cost-efficient Tuning and Provisioning of Data Analytic Jobs
Lynceus is introduced, a new approach for the optimization of cloud-based data analytic jobs that improves over state-of-the-art approaches by enabling significant cost savings both in terms of the final recommended configuration and of the optimization process used to recommend configurations. Expand
A Quest for Inspiration: How Users Create and Reuse PINs
Personal Identification Numbers (PINs), required to authenticate on a multitude of devices, are ubiquitous nowadays. To increase the security and safety of their assets, users are advised to createExpand
Self-tuning of cloud systems
Over the past years, there has been an increase in the use of Cloud Computing for delivery of services. These services range from remote storage systems to Virtual Machines (VMs), available forExpand
TrimTuner: Efficient Optimization of Machine Learning Jobs in the Cloud via Sub-Sampling
It is shown that TrimTuner can reduce the cost of the optimization process by up to 50 x and speeds-up the recommendation process by 65 x with respect to state of the art techniques for hyperparameter optimization that use sub-sampling techniques. Expand
Lynceus: Long-Sighted, Budget-Aware Online Tuning of Cloud Applications
Over the past years, modern cloud providers have widely incremented the heterogeneity of the products they offer, be it virtual machines or remote storage systems. With such a huge variety ofExpand