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Skadi: Building a Distributed Runtime for Data Systems in Disaggregated Data Centers

Publications associées (32)

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Nowadays our daily life is not only powered by water, electricity, gas and telephony but by "cloud" as well. Big cloud vendors such as Amazon, Microsoft and Google have built large-scale centralized data centers to achieve economies of scale, on-demand res ...

EPFL2017

The last 2 decades have witnessed a dramatic increase in the use of patent citation data in social science research. Facilitated by digitization of the patent data and increasing computing power, a community of practice has grown up that has developed meth ...

Wiley2017

Toward timely, predictable and cost-effective data analytics

Renata Borovica-Gajic

Modern industrial, government, and academic organizations are collecting massive amounts of data at an unprecedented scale and pace. The ability to perform timely, predictable and cost-effective analytical processing of such large data sets in order to ext ...

EPFL2016

Runtime Prediction for Scale-Out Data Analytics

Adrian Daniel Popescu

Many analytics applications generate mixed workloads, i.e., workloads comprised of analytical tasks with different processing characteristics including data pre-processing, SQL, and iterative machine learning algorithms. Examples of such mixed workloads ca ...

EPFL2015

Adaptation, Learning, and Optimization over Networks

Ali H. Sayed

This work deals with the topic of information processing over graphs. The presentation is largely self-contained and covers results that relate to the analysis and design of multi-agent networks for the distributed solution of optimization, adaptation, and ...

NOW Publishers2014

Low Power and Scalable Many-Core Architecture for Big-Data Stream Computing

David Atienza Alonso, Karim Kanoun, Martino Ruggiero

In the last years the process of examining large amounts of different types of data, or Big-Data, in an effort to uncover hidden patterns or unknown correlations has become a major need in our society. In this context, stream mining applications are now wi ...

IEEE Press2014

DiNoDB: Efficient Large-Scale Raw Data Analytics

Anastasia Ailamaki, Marko Vukolic, Ioannis Alagiannis, Erietta Liarou, Yongchao Tian

Modern big data workflows, found in e.g., machine learning use cases, often involve iterations of cycles of batch analytics and interactive analytics on temporary data. Whereas batch analytics solutions for large volumes of raw data are well established (e ...

2014

Distributed Decision-Making Over Adaptive Networks

Ali H. Sayed

In distributed processing, agents generally collect data generated by the same underlying unknown model (represented by a vector of parameters) and then solve an estimation or inference task cooperatively. In this paper, we consider the situation in which ...

2014

System and Method for Optimizing Data Storage in a Distributed Data Storage Environment

Karl Aberer, Nicolas Bonvin, Athanasios Papaioannou

A growing amount of data is produced daily resulting in a growing demand for storage solutions. While cloud storage providers offer a virtually infinite storage capacity, data owners seek geographical and provider diversity in data placement, in order to a ...

2014

Application of large-scale computing infrastructure for diverse environmental research applications using GC3Pie

Michael Lehning, Sofiane Sarni, Mathias Thierry Pierre Bavay

The Swiss Experiment platform (SwissEx: http://www.swiss-experiment.ch) provides a distributed storage and processing infrastructure for environmental research experiments. The aim of the second phase project (the Open Support Platform for Environmental Re ...

2013