Related papers: SCENIC: Stream Computation-Enhanced SmartNIC

Recent advancements in data stream processing frameworks have improved real-time data handling, however, scalability remains a significant challenge affecting throughput and latency. While studies have explored this issue on local machines…

The exponential growth of data traffic and the increasing complexity of networked applications demand effective solutions capable of passively inspecting and analysing the network traffic for monitoring and security purposes. Implementing…

Cloud services have been widely employed in IT industry and scientific research. By using Cloud services users can move computing tasks and data away from local computers to remote datacenters. By accessing Internet-based services over…

Network speeds grow quickly in the modern cloud, so SmartNICs are introduced to offload network processing tasks, even application logic. However, typical multicore SmartNICs such as BlueFiled-2 are only capable of processing control-plane…

Permissioned blockchains promise secure decentralized data management in business-to-business use-cases. In contrast to Bitcoin and similar public blockchains which rely on Proof-of-Work for consensus and are deployed on thousands of…

Early programming languages for software-defined networking (SDN) were built on top of the simple match-action paradigm offered by OpenFlow 1.0. However, emerging hardware and software switches offer much more sophisticated support for…

The data center network (DCN), wired or wireless, features large amounts of Many-to-One (M2O) sessions. Each M2O session is currently operated based on Point-to-Point (P2P) communications and Store-and-Forward (SAF) relays, and is generally…

As AI systems grow increasingly specialized and complex, managing hardware heterogeneity becomes a pressing challenge. How can we efficiently coordinate and synchronize heterogeneous hardware resources to achieve high utilization? How can…

Stream processing applications extract value from raw data through Directed Acyclic Graphs of data analysis tasks. Shared-nothing (SN) parallelism is the de-facto standard to scale stream processing applications. Given an application, SN…

Multi-Access Edge Computing (MEC) will allow implementing low-latency services that have been unfeasible so far. The European Telecommunications Standards Institute (ETSI) and the 3rd Generation Partnership Project (3GPP) are working…

The increasing prominence of AI necessitates the deployment of inference platforms for efficient and effective management of AI pipelines and compute resources. As these pipelines grow in complexity, the demand for distributed serving rises…

The explosive increase in volume, velocity, variety, and veracity of data generated by distributed and heterogeneous nodes such as IoT and other devices, continuously challenge the state of art in big data processing platforms and mining…

Processing large-scale graphs, containing billions of entities, is critical across fields like bioinformatics, high-performance computing, navigation and route planning, among others. Efficient graph partitioning, which divides a graph into…

Thanks to the advances in machine learning, data-driven analysis tools have become valuable solutions for various applications. However, there still remain essential challenges to develop effective data-driven methods because of the need to…

Scenic is an open-source JAX library with a focus on Transformer-based models for computer vision research and beyond. The goal of this toolkit is to facilitate rapid experimentation, prototyping, and research of new vision architectures…

Edge computing solutions that enable the extraction of high-level information from a variety of sensors is in increasingly high demand. This is due to the increasing number of smart devices that require sensory processing for their…

Data lakes spend a significant fraction of query execution time on scanning data from remote, disaggregated storage. Decoding alone accounts for 46% of runtime when running TPC-H directly on Parquet files. To address this bottleneck, we…

Disaggregated memory breaks the boundary of monolithic servers to enable memory provisioning on demand. Using network-attached memory to provide memory expansion for memory-intensive applications on compute nodes can improve the overall…

Stream processing applications have been widely adopted due to real-time data analytics demands, e.g., fraud detection, video analytics, IoT applications. Unfortunately, prototyping and testing these applications is still a cumbersome…

Many distributed applications implement complex data flows and need a flexible mechanism for routing data between producers and consumers. Recent advances in programmable network interface cards, or SmartNICs, represent an opportunity to…