Serverless is an emerging computing paradigm that greatly simplifies the development,
deployment, and maintenance of cloud applications. However, due to potential cost issues …

A recent trend in stream processing is offloading the computation of decomposable
aggregation functions (DAF) from cloud nodes to geo-distributed fog/edge devices to …

Streaming workloads deal with data that is generated in real-time. This data is often
unpredictable and changes rapidly in volume. To deal with these fluctuations, current …

Large language models (LLMs) have significantly advanced the field of natural language
processing, with GPT models at the forefront. While their remarkable performance spans a …

Proactive streaming analytics continuously extract real-time business value from massive
data that stream in data centers or clouds. This requires (a) to process the data while they …

Managing the limited energy on mobile platforms executing long-running, resource intensive
streaming applications requires adapting an application's operators in response to their …

With the growth of multi-cloud computing across a heterogeneous substrate of public cloud,
edge, and on-premise sites, observability has been gaining importance in compre-hending …

Massive amounts of data are generated by sensor networks, edge computers, IoT devices,
and enterprise networks. To process this volume of data requires (1) a scalable …

To provide high-quality streaming services to end users, streaming analytics applications
need to process massive volumes of data promptly. Such applications suffer from high …

This research presents JARVIS, an innovative desktop assistant tool designed to
revolutionize user interaction within the realm of robotics. Leveraging the powerful …