Section: Overall Objectives

Context: the need for scalable data management

We are witnessing a rapidly increasing number of application areas generating and processing very large volumes of data on a regular basis. Such applications are called data-intensive. Governmental and commercial statistics, climate modeling, cosmology, genetics, bio-informatics, high-energy physics are just a few examples in the scientific area. In addition, rapidly growing amounts of data from social networks and commercial applications are now routinely processed.

In all these examples, the overall application performance is highly dependent on the properties of the underlying data management service. It becomes crucial to store and manipulate massive data efficiently. However, these data are typically shared at a large scale and concurrently accessed at a high degree. With the emergence of recent infrastructures such as cloud computing platforms and post-Petascale high-performance computing (HPC) systems, achieving highly scalable data management under such conditions has become a major challenge.

Our objective

The KerData project-team is namely focusing on designing innovative architectures and systems for scalable data storage and processing. We target two types of infrastructures: clouds and post-Petascale high-performance supercomputers, according to the current needs and requirements of data-intensive applications.

We are especially concerned by the applications of major international and industrial players in cloud computing and extreme-scale high-performance computing (HPC), which shape the long-term agenda of the cloud computing  [26], [23] and Exascale HPC  [25] research communities. The Big Data area, emphasized the challenges related to Volume, Velocity and Variety. This is yet another element of context that further highlights the primary importance of designing data management systems that are efficient at a very large scale.

Alignment with Inria's scientific strategy

Data-intensive applications exhibit several common requirements with respect to the need for data storage and I/O processing. We focus on some core challenges related to data management, resulted from these requirements. Our choice is perfectly in line with Inria's strategic plan  [30], which acknowledges as critical the challenges of storing, exchanging, organizing, utilizing, handling and analyzing the huge volumes of data generated by an increasing number of sources. This topic is also stated as a scientific priority of Inria's research center of Rennes  [29]: Storage and utilization of distributed big data.

Challenges and goals related to cloud data storage and processing

In the area of cloud data processing, a significant milestone is the emergence of the Map-Reduce  [35] parallel programming paradigm. It is currently used on most cloud platforms, following the trend set up by Amazon  [22]. At the core of Map-Reduce frameworks lies the storage system, a key component which must meet a series of specific requirements that are not fully met yet by existing solutions: the ability to provide efficient fine-grain access to the files, while sustaining a high throughput in spite of heavy access concurrency; the need to provide a high resilience to failures; the need to take energy-efficiency issues into account.

More recently, it becomes clear that data-intensive processing needs to go beyond the frontiers of single datacenters. In this perspective, extra challenges arise, related to the efficiency of metadata management. This efficiency has a major impact on the access to very large sets of small objects by Big Data processing workflows running on large-scale infrastructures.

Challenges and goals related to data-intensive HPC applications

Key research fields such as climate modeling, solid Earth sciences or astrophysics rely on very large-scale simulations running on post-Petascale supercomputers. Such applications exhibit requirements clearly identified by international panels of experts like IESP  [28], EESI  [24], ETP4HPC  [25]. A jump of one order of magnitude in the size of numerical simulations is required to address some of the fundamental questions in several communities in this context. In particular, the lack of data-intensive infrastructures and methodologies to analyze the huge results of such simulations is a major limiting factor.

The challenge we have been addressing is to find new ways to store, visualize and analyze massive outputs of data during and after the simulations. Our main initial goal was to do it without impacting the overall performance, avoiding the jitter generated by I/O interference as much as possible. Recently, we started to focus specifically on in situ processing approaches and we explored approaches to model and predict I/O phase occurrences and to reduce intra-application and cross-application I/O interference.

Our approach

KerData's global approach consists in studying, designing, implementing and evaluating distributed algorithms and software architectures for scalable data storage and I/O management for efficient, large-scale data processing. We target two main execution infrastructures: cloud platforms and post-Petascale HPC supercomputers.

Platforms and Methodology

The highly experimental nature of our research validation methodology should be emphasized. To validate our proposed algorithms and architectures, we build software prototypes, then validate them at a large scale on real testbeds and experimental platforms.

We strongly rely on the Grid'5000 platform. Moreover, thanks to our projects and partnerships, we have access to reference software and physical infrastructures. In the cloud area, we use the Microsoft Azure and Amazon cloud platforms. In the post-Petascale HPC area, we are running our experiments on systems including some top-ranked supercomputers, such as Titan, Jaguar, Kraken or Blue Waters. This provides us with excellent opportunities to validate our results on advanced realistic platforms.

Collaboration strategy

Our collaboration portfolio includes international teams that are active in the areas of data management for clouds and HPC systems, both in Academia and Industry.

Our academic collaborating partners include Argonne National Lab, University of Illinois at Urbana-Champaign, Universidad Politécnica de Madrid, Barcelona Supercomputing Center, University Politehnica of Bucharest. In industry, we are currently collaborating with Huawei and Total.

Moreover, the consortiums of our collaborative projects include application partners in the area of climate simulations (e.g., the Department of Earth and Atmospheric Sciences of the University of Michigan, within our collaboration inside JLESC  [31]). This is an additional asset, which enables us to take into account application requirements in the early design phase of our solutions, and to validate those solutions with real applications... and real users!