Related papers: Should I Hide My Duck in the Lake?
Deep Neural Networks (DNNs) have become an essential component in many application domains including web-based services. A variety of these services require high throughput and (close to) real-time features, for instance, to respond or…
With the increasing artificial intelligence application, deep neural network (DNN) has become an emerging task. However, to train a good deep learning model will suffer from enormous computation cost and energy consumption. Recently,…
Secure multi-party computation (MPC) facilitates privacy-preserving computation between multiple parties without leaking private information. While most secure deep learning techniques utilize MPC operations to achieve feasible…
Presto is an open-source distributed SQL query engine for OLAP, aiming for "SQL on everything". Since open-sourced in 2013, Presto has been consistently gaining popularity in large-scale data analytics and attracting adoption from a wide…
To process data more efficiently, big data frameworks provide data abstractions to developers. However, due to the abstraction, there may be many challenges for developers to understand and debug the data processing code. To uncover the…
Sideways information passing is a well-known technique for mitigating the impact of large build sides in a database query plan. As currently implemented in production systems, sideways information passing enables only a uni-directional…
In this paper we show that the data plane of commodity programmable (Network Interface Cards) NICs can run neural network inference tasks required by packet monitoring applications, with low overhead. This is particularly important as the…
Enterprise data lakes often suffer from substantial amounts of duplicate and redundant data, with data volumes ranging from terabytes to petabytes. This leads to both increased storage costs and unnecessarily high maintenance costs for…
Data lakes hold a growing amount of cold data that is infrequently accessed, yet require interactive response times. Serverless functions are seen as a way to address this use case since they offer an appealing alternative to maintaining…
In this paper, we present a detailed performance analysis of QUIC instant ACK, a standard-compliant approach to reduce waiting times during the QUIC connection setup in common CDN deployments. To understand the root causes of the…
Due to the pervasive diffusion of personal mobile and IoT devices, many ``smart environments'' (e.g., smart cities and smart factories) will be, among others, generators of huge amounts of data. Currently, this is typically achieved through…
Today's Cloud applications are dominated by composite applications comprising multiple computing and data components with strong communication correlations among them. Although Cloud providers are deploying large number of computing and…
Main memory column-stores have proven to be efficient for processing analytical queries. Still, there has been much less work in the context of clusters. Using only a single machine poses several restrictions: Processing power and data…
The increasing performance requirements of modern applications place a significant burden on software-based packet processing. Most of today's software input/output accelerations achieve high performance at the expense of reserving CPU…
Cloud interactive data-driven applications generate swarms of small TCP flows that compete for the small buffer space in data-center switches. Such applications require a short flow completion time (FCT) to perform their jobs effectively.…
Scientific applications in HPC environment are more com-plex and more data-intensive nowadays. Scientists usually rely on workflow system to manage the complexity: simply define multiple processing steps into a single script and let the…
Many data analytic systems have adopted a newly emerging compute resource, serverless (SL), to handle data analytics queries in a timely and cost-efficient manner, i.e., serverless data analytics. While these systems can start processing…
Modern storage systems often combine fast cache with slower backend devices to accelerate I/O. As performance gaps narrow, concurrently accessing both devices, rather than relying solely on cache hits, can improve throughput. However, in…
The increasing complexity of deep neural networks (DNNs) has made it challenging to exploit existing large-scale data processing pipelines for handling massive data and parameters involved in DNN training. Distributed computing platforms…
Shared memory multiprocessors come back to popularity thanks to rapid spreading of commodity multi-core architectures. As ever, shared memory programs are fairly easy to write and quite hard to optimise; providing multi-core programmers…