Model for Partially replicated in-memory Transactional cloud stores. PROMPT combines white box Analytical Modelling and Machine Learning techniques, with the goal of achieving the best of the two methodologies: low training times, high extrapolation power, and portability across heterogeneous cloud infrastructures. We validate PROMPT via an extensive experimental study based on a popular open-source transactional in-memory data store (Red Hat’s Infinispan), industry-standard benchmarks, and de-ployments on both public and private cloud infrastructures. I
Storage device performance prediction is a key element of self-managed storage systems. This work ex...
Abstract—In this paper we focus on the problem of self-tuning distributed transactional cloud data s...
ABSTRACT: This paper presents a novel evaluation study of various strategies for modeling and simula...
In-memory (transactional) data stores, also referred to as data grids, are recognized as a first-cla...
In this article, we introduce TAS (Transactional Auto Scaler), a system for automating the elastic s...
In-memory transactional data grids have revealed extremely suited for cloud based environments, give...
In this paper we introduce TAS (Transactional Auto Scaler), a system for automating elastic-scaling ...
Due to the cost of sampling system performance, it is expensive to obtain performance characteristic...
Cloud-based solutions are increasingly being used to implement large-scale dynamic data driven appli...
Managing data over cloud infrastructures raises novel challenges with respect to existing and well s...
In this article we exploit a combination of analytical and Machine Learning (ML) techniques in order...
Managing data over cloud infrastructures raises novel challenges with respect to existing and well s...
International audienceOne of the cornerstones of the cloud provider business is to reduce hardware r...
Abstract We propose simple models to predict the perfor-mance degradation of disk requests due to st...
Managing data over cloud infrastructures raises novel challenges with respect to existing and well s...
Storage device performance prediction is a key element of self-managed storage systems. This work ex...
Abstract—In this paper we focus on the problem of self-tuning distributed transactional cloud data s...
ABSTRACT: This paper presents a novel evaluation study of various strategies for modeling and simula...
In-memory (transactional) data stores, also referred to as data grids, are recognized as a first-cla...
In this article, we introduce TAS (Transactional Auto Scaler), a system for automating the elastic s...
In-memory transactional data grids have revealed extremely suited for cloud based environments, give...
In this paper we introduce TAS (Transactional Auto Scaler), a system for automating elastic-scaling ...
Due to the cost of sampling system performance, it is expensive to obtain performance characteristic...
Cloud-based solutions are increasingly being used to implement large-scale dynamic data driven appli...
Managing data over cloud infrastructures raises novel challenges with respect to existing and well s...
In this article we exploit a combination of analytical and Machine Learning (ML) techniques in order...
Managing data over cloud infrastructures raises novel challenges with respect to existing and well s...
International audienceOne of the cornerstones of the cloud provider business is to reduce hardware r...
Abstract We propose simple models to predict the perfor-mance degradation of disk requests due to st...
Managing data over cloud infrastructures raises novel challenges with respect to existing and well s...
Storage device performance prediction is a key element of self-managed storage systems. This work ex...
Abstract—In this paper we focus on the problem of self-tuning distributed transactional cloud data s...
ABSTRACT: This paper presents a novel evaluation study of various strategies for modeling and simula...