相关论文: Putting Data Science Pipelines on the Edge

The pipeline leakage problem is a very challenging and critical issue. Solving this problem will save the nation a lot of money, resources and more importantly, it will save the environment. This paper discusses the state-of-the-art of leak…

Currently, a variety of pipeline tools are available for use in data engineering. Data scientists can use these tools to resolve data wrangling issues associated with data and accomplish some data engineering tasks from data ingestion…

Pipelining is a well understood and often used implementation technique for increasing the performance of a hardware system. We develop several SystemC/C++ modeling techniques that allow us to quickly model, simulate, and evaluate…

The "IMP Science Gateway Portal" (http://scigate.imp.kiev.ua) for complex workflow management and integration of distributed computing resources (like clusters, service grids, desktop grids, clouds) is presented. It is created on the basis…

Much of the alignment tuning literature is organized around optimization objectives, while the construction of alignment data is often treated implicitly. In this survey, we adopt a data centric perspective and reframe alignment tuning as a…

The Digital Twin (DT) offers a novel approach to the management of critical infrastructures, including energy, water, traffic, public health, and communication systems, which are indispensable for the functioning of modern societies. The…

Pulsar data analysis pipelines have historically been comprised of bespoke software systems, supporting the off-line analysis of data. However modern data acquisition systems are making off-line analyses impractical. They often output…

The growing adoption of federated data spaces, such as in the GAIA-X and the International Data Spaces (IDS) initiative, promises secure and sovereign data sharing across organizational boundaries in Industry 4.0. In manufacturing…

Under several emerging application scenarios, such as in smart cities, operational monitoring of large infrastructure, wearable assistance, and Internet of Things, continuous data streams must be processed under very short delays. Several…

Constructing behavioral-level chiplet models (e.g., SystemC) is crucial for early-stage heterogeneous architecture exploration. Traditional manual modeling is notoriously time-consuming and error-prone. Recently, Large Language Models…

Implementing concurrent data structures is challenging and requires a deep understanding of concurrency concepts and careful design to ensure correctness, performance, and scalability. Further, composing operations on two or more concurrent…

Many organizations routinely analyze large datasets using systems for distributed data-parallel processing and clusters of commodity resources. Yet, users need to configure adequate resources for their data processing jobs. This requires…

Data processing frameworks such as Apache Beam and Apache Spark are used for a wide range of applications, from logs analysis to data preparation for DNN training. It is thus unsurprising that there has been a large amount of work on…

The energy demands of data centers are increasing and are expected to grow exponentially. Reducing the energy consumption of data centers decreases operational expenses, as well as their carbon footprint. We design techniques to reduce data…

With the growth of real-time applications and IoT devices, computation is moving from cloud-based services to the low latency edge, creating a computing continuum. This continuum includes diverse cloud, edge, and endpoint devices, posing…

The dispersed node locations and complex topologies of edge networks, combined with intricate dynamic microservice dependencies, render traditional centralized microservice architectures (MSAs) unsuitable. In this paper, we propose a…

The systems that operate the infrastructure of cities have evolved in a fragmented fashion across several generations of technology, causing city utilities and services to operate sub-optimally and limiting the creation of new value-added…

A composable infrastructure is defined as resources, such as compute, storage, accelerators and networking, that are shared in a pool and that can be grouped in various configurations to meet application requirements. This freedom to 'mix…

Advances in technology and computing hardware are enabling scientists from all areas of science to produce massive amounts of data using large-scale simulations or observational facilities. In this era of data deluge, effective coordination…

This paper evaluates the optimal scale of datacentre (DC) resource disaggregation for composable DC infrastructures and investigates the impact of present day silicon photonics technologies on the energy efficiency of different composable…