Published on Fri Feb 05 2016

Daleel: Simplifying Cloud Instance Selection Using Machine Learning

Faiza Samreen, Yehia Elkhatib, Matthew Rowe, Gordon S. Blair

Machine learning can help address some of the complicated decisions by carrying out customer-specific analytics. We employ machine learning techniques to develop an adaptive deployment policy, providing an optimal match between the customer demands and the available cloud service offerings.

0
0
0
Abstract

Decision making in cloud environments is quite challenging due to the diversity in service offerings and pricing models, especially considering that the cloud market is an incredibly fast moving one. In addition, there are no hard and fast rules, each customer has a specific set of constraints (e.g. budget) and application requirements (e.g. minimum computational resources). Machine learning can help address some of the complicated decisions by carrying out customer-specific analytics to determine the most suitable instance type(s) and the most opportune time for starting or migrating instances. We employ machine learning techniques to develop an adaptive deployment policy, providing an optimal match between the customer demands and the available cloud service offerings. We provide an experimental study based on extensive set of job executions over a major public cloud infrastructure.

Mon Nov 09 2020
Machine Learning
TrimTuner: Efficient Optimization of Machine Learning Jobs in the Cloud via Sub-Sampling
TrimTuner is the first system for optimizing machine learning jobs in the cloud to exploit sub-sampling techniques. It can reduce the cost of the optimization process by up to 50x.
0
0
0
Sun May 09 2021
Machine Learning
Machine Learning (ML)-Centric Resource Management in Cloud Computing: A Review and Future Directions
0
0
0
Wed Jun 09 2021
Machine Learning
Cocktail: Leveraging Ensemble Learning for Optimized Model Serving in Public Cloud
Cocktail is a costeffective ensembling-based model serving framework. The RM framework leveragestransient virtual machine instances to reduce the de-ployment cost in a public cloud. Cocktail can reduce deployment cost by 1.45x, while providing 2xreduction in latency.
1
0
0
Tue May 07 2019
Machine Learning
Transferable Knowledge for Low-cost Decision Making in Cloud Environments
Cloud computing is increasingly overwhelmed with the wide range of providers and services offered by each provider. An emerging alternative is to use a decision support system (DSS), which relies on gaining insights from observational data. The primary activity of such systems is the generation of a prediction model, which requires a significantly large amount of training
0
0
0
Sat Oct 12 2019
Machine Learning
ClassyTune: A Performance Auto-Tuner for Systems in the Cloud
Performance tuning can improve the system performance and thus enable the reduction of cloud computing resources needed to support an application. ClassyTune exploits the machine learning model of classification for auto-tuning.
0
0
0
Tue Jun 09 2020
Machine Learning
MLModelCI: An Automatic Cloud Platform for Efficient MLaaS
MLModelCI provides multimedia researchers and developers with a one-stop platform for efficient machine learning (ML) services. The system leverages DevOps techniques to optimize, test, and manage models. It also containerizes these optimized and validated models as cloud services (MLaaS)
0
0
0