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20242026
most citedIntentContinuum: Using LLMs to Support Intent-Based Computing Across the Compute Continuum

12 citations · 18 across the 11 of their papers we have counts for

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7 papers · 1 filter

cs.DC2026

PixelFlow: Token-Level Workload Management for Efficient Distributed DiT Serving

Zhexiang Zhang, Minchen Yu, Yifan Sun +3

Online image generation with Diffusion Transformers (DiTs) must meet latency service-level objectives (SLOs) while using GPU resources efficiently. Existing systems improve GPU uti…

cs.DC2026

GraphFlash: Enabling Fast and Elastic Graph Processing on Serverless Infrastructure

Chen Zhao, Parsa Poorsistani, Mohammad Goudarzi +2

Graph processing systems are essential for analyzing large-scale data with complex relationships, yet most existing frameworks rely on statically provisioned clusters, resulting in…

cs.DC2026

PipeLive: Efficient Live In-place Pipeline Parallelism Reconfiguration for Dynamic LLM Serving

Xu Bai, Muhammed Tawfiqul Islam, Chen Wang +1

Pipeline parallelism (PP) is widely used to partition layers of large language models (LLMs) across GPUs, enabling scalable inference for large models. However, existing systems re…

cs.DC2026

Multi-Objective Load Balancing for Heterogeneous Edge-Based Object Detection Systems

Daghash K. Alqahtani, Maria A. Rodriguez, Muhammad Aamir Cheema +1

The rapid proliferation of the Internet of Things (IoT) and smart applications has led to a surge in data generated by distributed sensing devices. Edge computing is a mainstream a…

cs.DC2026★ 1 cited

Service Orchestration in the Computing Continuum: Structural Challenges and Vision

Boris Sedlak, Víctor Casamayor Pujol, Ildefons Magrans de Abril +3

The Computing Continuum (CC) integrates different layers of processing infrastructure, from Edge to Cloud, to optimize service quality through ubiquitous and reliable computation.…

cs.DC2025★ 12 cited

IntentContinuum: Using LLMs to Support Intent-Based Computing Across the Compute Continuum

Negin Akbari, John Grundy, Aamir Cheema +1

The increasing proliferation of IoT devices and AI applications has created a demand for scalable and efficient computing solutions, particularly for applications requiring real-ti…

cs.DC2024★ 2 cited

TempoScale: A Cloud Workloads Prediction Approach Integrating Short-Term and Long-Term Information

Linfeng Wen, Minxian Xu, Adel N. Toosi +1

Cloud native solutions are widely applied in various fields, placing higher demands on the efficient management and utilization of resource platforms. To achieve the efficiency, lo…