Related papers: MetaFlow: a Scalable Metadata Lookup Service for D…
A Content Delivery Network (CDN) is a powerful system of distributed caching servers that aims to accelerate content delivery, like high-definition video, IoT applications, and ultra-low-latency services, efficiently and with fast velocity.…
Transformers are central to advances in artificial intelligence (AI), excelling in fields ranging from computer vision to natural language processing. Despite their success, their large parameter count and computational demands challenge…
Software Defined Networking (SDN) not only enables agility through the realization of part of the network functionality in software but also facilitates offering advanced features at the network layer. Hence, SDN can support a wide range of…
The data engineering and data science community has embraced the idea of using Python & R dataframes for regular applications. Driven by the big data revolution and artificial intelligence, these applications are now essential in order to…
Big Data is defined as high volume of variety of data with an exponential data growth rate. Data are amalgamated to generate revenue, which results a large data silo. Data are the oils of modern IT industries. Therefore, the data are…
OpenFlow switches are fundamental components of software defined networking, where the key operation is to look up flow tables to determine which flow an incoming packet belongs to. This needs to address the same multi-field rule-matching…
As the most competitive solution for next-generation network, software-defined network (SDN) and its dominant implementation OpenFlow, are attracting more and more interests. But besides convenience and flexibility, SDN/OpenFlow also…
Scanning and filtering over multi-dimensional tables are key operations in modern analytical database engines. To optimize the performance of these operations, databases often create clustered indexes over a single dimension or…
Typically, serverless functions rely on remote storage services for managing state, which can result in increased latency and network communication overhead. In a dynamic environment such as the 3D (Edge-Cloud-Space) Compute Continuum,…
Parallel dataflow systems are a central part of most analytic pipelines for big data. The iterative nature of many analysis and machine learning algorithms, however, is still a challenge for current systems. While certain types of bulk…
Extract-Transform-Load (ETL) handles large amount of data and manages workload through dataflows. ETL dataflows are widely regarded as complex and expensive operations in terms of time and system resources. In order to minimize the time and…
We present DataFlow, a computational framework for building, testing, and deploying high-performance machine learning systems on unbounded time-series data. Traditional data science workflows assume finite datasets and require substantial…
Task scheduling is a well-studied problem in the context of optimizing the Quality of Service (QoS) of cloud computing environments. In order to sustain the rapid growth of computational demands, one of the most important QoS metrics for…
Software-Defined Networking (SDN) introduces a centralized network control and management by separating the data plane from the control plane which facilitates traffic flow monitoring, security analysis and policy formulation. However, it…
With the advent of internet services, data started growing faster than it can be processed. To personalize user experience, this enormous data has to be processed in real time, in interactive fashion. In order to achieve faster data…
Metadata represents the information about data to be stored in Data Warehouses.It is a mandatory element of Data Warehouse to build an efficient Data Warehouse.Metadata helps in data integration,lineage,data quality and populating…
As the landscape of deep neural networks evolves, heterogeneous dataflow accelerators, in the form of multi-core architectures or chiplet-based designs, promise more flexibility and higher inference performance through scalability. So far,…
Despite recent advancements in large language models (LLMs), their performance on complex reasoning problems requiring multi-step thinking and combining various skills is still limited. To address this, we propose a novel framework HDFlow…
Cloud has been a computational and storage solution for many data centric organizations. The problem today those organizations are facing from the cloud is in data searching in an efficient manner. A framework is required to distribute the…
Reusable data/code and reproducible analyses are foundational to quality research. This aspect, however, is often overlooked when designing interactive stream analysis workflows for time-series data (e.g., eye-tracking data). A mechanism to…