most citedResilience Evaluation of Kubernetes in Cloud-Edge Environments via Failure Injection

1 citations · 2 across the 4 of their papers we have counts for

collaborators

6 papers

cs.AI2026

Zero-Shot Forecasting of In-Situ Soil Moisture and Temperature with a Time-Series Foundation Model

Mohammad Talaei, Mohammad Goudarzi, Maille Todd +2

Soil moisture and temperature govern plant-water availability and microbial activity, making them key indicators for evaluating land-restoration efforts on degraded landscapes. How…

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

LLM-Driven Intent-Based Privacy-Aware Orchestration Across the Cloud-Edge Continuum

Zijie Su, Muhammed Tawfiqul Islam, Mohammad Goudarzi +1

With the rapid advancement of large language models (LLMs), efficiently serving LLM inference under limited GPU resources has become a critical challenge. Recently, an increasing n…

cs.DC2025★ 1 cited

Resilience Evaluation of Kubernetes in Cloud-Edge Environments via Failure Injection

Zihao Chen, Mohammad Goudarzi, Adel Nadjaran Toosi

Kubernetes has emerged as an essential platform for deploying containerised applications across cloud and edge infrastructures. As Kubernetes gains increasing adoption for mission-…

cs.DC2025★ 1 cited

Efficient Routing of Inference Requests across LLM Instances in Cloud-Edge Computing

Shibo Yu, Mohammad Goudarzi, Adel Nadjaran Toosi

The rising demand for Large Language Model (LLM) inference services has intensified pressure on computational resources, resulting in latency and cost challenges. This paper introd…

cs.DC2025

A Multi-Armed Bandit-Based Participant Selection Method for Federated Recommendation Systems

Jintao Liu, Mohammad Goudarzi, Adel Nadjaran Toosi

Federated Recommendation Systems (FRS) enable privacy-preserving model training by keeping user data on edge devices. However, the practical deployment of FRS in Edge-Cloud environ…