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As the computing power of large-scale HPC clusters approaches the Exascale, the gap between compute capabilities and storage systems is ever widening. In particular, the popular High Performance Computing (HPC) application, the Weather…
In-flight Internet connectivity is a necessity for aircraft passengers as well as aircraft systems. It is challenging to satisfy required quality of service (QoS) levels for flows within aircraft due to the large number of users and the…
With rapid advances in network hardware, far memory has gained a great deal of traction due to its ability to break the memory capacity wall. Existing far memory systems fall into one of two data paths: one that uses the kernel's paging…
While cluster computing frameworks are continuously evolving to provide real-time data analysis capabilities, Apache Spark has managed to be at the forefront of big data analytics for being a unified framework for both, batch and stream…
Data intensive applications often involve the analysis of large datasets that require large amounts of compute and storage resources. While dedicated compute and/or storage farms offer good task/data throughput, they suffer low resource…
The need for scalable and efficient stream analysis has led to the development of many open-source streaming data processing systems (SDPSs) with highly diverging capabilities and performance characteristics. While first initiatives try to…
Analyzing the increasingly large volumes of data that are available today, possibly including the application of custom machine learning models, requires the utilization of distributed frameworks. This can result in serious productivity…
Distributed data processing platforms for cloud computing are important tools for large-scale data analytics. Apache Hadoop MapReduce has become the de facto standard in this space, though its programming interface is relatively low-level,…
With the exponential growth of data and evolving use cases, petabyte-scale OLAP data platforms are increasingly adopting a model that decouples compute from storage. This shift, evident in organizations like Uber and Meta, introduces…
Big data processing is a hot topic in today's computer science world. There is a significant demand for analysing big data to satisfy many requirements of many industries. Emergence of the Kappa architecture created a strong requirement for…
Recent neural networks (NNs) with self-attention exhibit competitiveness across different AI domains, but the essential attention mechanism brings massive computation and memory demands. To this end, various sparsity patterns are introduced…
Apache Kafka has become a foundational platform for high throughput event streaming, enabling real time analytics, financial transaction processing, industrial telemetry, and large scale data driven systems. Despite its maturity and…
High throughput and low latency data processing is essential for systems requiring live decision making, control, and machine learning-optimized data reduction. We focus on two distinct use cases for in-flight streaming data processing for…
Advancement in Processor technology has made it easy to handle data-intensive workloads, but limiting main memory advances has created performance bottlenecks. In DRAM, there have been improvements in DRAM access latency as well as…
Communication between robots and the server is a major problem for cloud robotic systems. In this paper, we address the problem caused by data loss during such communications, and propose an efficient buffering algorithm, called AFR, to…
RDMA is an exciting technology that enables a host to access the memory of a remote host without involving the remote CPU. Prior work shows how to use RDMA to improve the performance of distributed in-memory storage systems. However, RDMA…
Serverless computing that runs functions with auto-scaling is a popular task execution pattern in the cloud-native era. By connecting serverless functions into workflows, tenants can achieve complex functionality. Prior researches adopt the…
In recent years, data-intensive applications have been increasingly deployed on cloud systems. Such applications utilize significant compute, memory, and I/O resources to process large volumes of data. Optimizing the performance and…
Emerging deep neural network (DNN) applications require high-performance multi-core hardware acceleration with large data bursts. Classical network-on-chips (NoCs) use serial packet-based protocols suffering from significant protocol…
As the sheer amount of computer generated data continues to grow exponentially, new bottlenecks are unveiled that require rethinking our traditional software and hardware architectures. In this paper we present five algorithms and data…