most citedSplitwise: Collaborative Edge-Cloud Inference for LLMs via Lyapunov-Assisted DRL

8 citations · 8 across the 3 of their papers we have counts for

collaborators

5 papers

cs.LG20258 cited

Splitwise: Collaborative Edge-Cloud Inference for LLMs via Lyapunov-Assisted DRL

Abolfazl Younesi, Abbas Shabrang Maryan, Elyas Oustad +3

Deploying large language models (LLMs) on edge devices is challenging due to their limited memory and power resources. Cloud-only inference reduces device burden but introduces hig…

cs.AI2025

AutoStreamPipe: LLM Assisted Automatic Generation of Data Stream Processing Pipelines

Abolfazl Younesi, Zahra Najafabadi Samani, Thomas Fahringer

Data pipelines are essential in stream processing as they enable the efficient collection, processing, and delivery of real-time data, supporting rapid data analysis. In this paper…

cs.DC2025

Collaborative State Machines: A Better Programming Model for the Cloud-Edge-IoT Continuum

Marlon Etheredge, Thomas Fahringer, Felix Erlacher +5

The development of Cloud-Edge-IoT applications requires robust programming models. Existing models often struggle to manage the dynamic and stateful nature of these applications ef…

cs.DC2025

A Terminology for Scientific Workflow Systems

Frédéric Suter, Tainã Coleman, İlkay Altintaş +23

The term scientific workflow has evolved over the last two decades to encompass a broad range of compositions of interdependent compute tasks and data movements. It has also become…

cs.DC2024

Dynamic Resource Manager for Automating Deployments in the Computing Continuum

Zahra Najafabadi Samani, Matthias Gassner, Thomas Fahringer +2

With the growth of real-time applications and IoT devices, computation is moving from cloud-based services to the low latency edge, creating a computing continuum. This continuum i…