Scrub: Online TroubleShooting for Large Mission-Critical Applications

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We propose an incentive mechanism for the sponsored content provider (CP) market in which the communication of users can be represented by a graph, and the private information of the users is assumed to have a continuous distribution function. The CP stipu ...

The landscape of computing is changing, thanks to the advent of modern networking equipment that allows machines to exchange information in as little as one microsecond. Such advancement has enabled microsecond-scale distributed computing, where entire dis ...

Online services have become ubiquitous in technological society, the global demand for which has driven enterprises to construct gigantic datacenters that run their software. Such facilities have also recently become a substrate for third-party organizatio ...

In the era of "big data" finding solutions for data storage alternative to those based on silicon or magnetic tapes is an urgent need for our society. The development of polymers that can store information at the molecular level has opened up new opportuni ...

The booming popularity of online services has led to a major evolution in the way these services are built and deployed. To cope with such online data-intensive services, service providers deploy several massive-scale datacenters, also referred to as wareh ...

This chapter discusses some of the opportunities offered by Big Data to understand mobility practices. However, beyond its promises, it has to be considered as human and social constructions. Therefore, the author underlines the importance of describing al ...

Much of the Internet economy relies on online advertising for monetizing digital content: Users are expected to accept the presence of online advertisements in exchange for content being free. However, online advertisements have become a serious problem fo ...

Current online applications, such as search engines, social networks, or file sharing services, execute across a distributed network of machines. They provide non-stop services to their users despite failures in the underlying network. To achieve such a hi ...

Database systems access memory either sequentially or randomly. Contrary to sequential access and despite the extensive efforts of computer architects, compiler writers, and system builders, random access to data larger than the processor cache has been s ...

Service-oriented architecture (SOA) paradigm for orchestrating large-scale distributed applications offers significant cost savings by reusing existing services. However, the high irregularity of client requests and the distributed nature of the approach m ...

Remote Procedure Calls are widely used to connect datacenter applications with strict tail-latency service level objectives in the scale of µs. Existing solutions utilize streaming or datagram-based transport protocols for RPCs that impose overheads and li ...

Distributed systems are difficult to design and develop. The difficulties arise both in basic safety correctness properties, and in achieving high performance. As a result of this complexity, the implementation of a distributed system often contains the ba ...

Modern asynchronous runtime systems allow the re-thinking of large-scale scientific applications. With the example of a simulator of morphologically detailed neural networks, we show how detaching from the commonly used bulk-synchronous parallel (BSP) exec ...

With the explosion of broadband Internet connections in the late 80s, new types of applications, called distributed applications, came to light. These applications typically seek to provide a user with remote services that appear to that user as if they we ...

The upsurge in the number of web users over the last two decades has resulted in a significant growth of online information. Recommenders are machine learning approach and are becoming one of the main ways to navigate the Internet. They recommend appropria ...

In recent years time series data has become ubiquitous thanks to affordable sensors and advances in embedded technology. Large amount of time-series data are continuously produced in a wide spectrum of applications, such as sensor networks, medical monitor ...

Big-Data streaming applications are used in several domains such as social media analysis, financial analysis, video annotation, surveillance, medical services and traffic prediction. These applications, running on different types of platforms from mobile ...

As radio telescope data is now increasingly high-dimensional, data reduction has become essential to reduce computational load while preserving accurate signal reconstruction. Gridding the continuous Fourier visibilities represents the standard approach to ...

Learning about users’ utilities from preference, discrete choice or implicit feedback data is of integral importance in e-commerce, targeted advertising and web search. Due to the sparsity and diffuse nature of data, Bayesian approaches hold much promise, ...

Scrub: Online TroubleShooting for Large Mission-Critical Applications

Chattez avec Graph Search

Incentive Mechanism in the Sponsored Content Market With Network Effects

Recognizing And Utilizing Novel Research Opportunities With Artificial Intelligence

Reliable Microsecond-Scale Distributed Computing

Hardware and Software Support for RPC-Centric Server Architecture

Bacterial nanopores open the future of data storage

Hardware-Software Co-Design of an RPC Processor

Digital approaches and mobilities in the Big Data era

Ad-blocking Games: Monetizing Online Content Under the Threat of Ad Avoidance

System Support for Efficient Replication in Distributed Systems

Improving Main-memory Database System Performance through Cooperative Multitasking

An economic approach for scalable and highly-available distributed applications

R2P2: Making RPCs first-class datacenter citizens

Simplifying Distributed System Development

Fully-Asynchronous Cache-Efficient Simulation of Detailed Neural Networks

Type-based event dissemination

Privacy in Recommender Systems

Distributed Time Series Analytics

Energy-Efficient Co-Design Optimization of Many-Core Platforms for Big-Data Streaming Applications

Dimension embedding for big data in radio interferometry

Scalable Collaborative Bayesian Preference Learning