Chengzhong Xu

Online Fake Drug Detection System in Heterogeneous Platforms Using Big Data Analysis

2016

The widespread use of internet provides extensive heterogeneous platforms for drug sales. The internet has greatly facilitated the development of merchandise sales, meanwhile, many fake drug sellers that has been strongly restricted in the market …

Prometheus

2017

Modern in-memory distributed computation frameworks like Spark adequately leverage memory resources to cache intermediate data across multi-stage tasks in pre-allocated worker processes, so as to speedup executions. They rely on a cluster resource manager like …

FIFL: A Fair Incentive Mechanism for Federated Learning

2021

Federated learning is a novel machine learning framework that enables multiple devices to collaboratively train high-performance models while preserving data privacy. Federated learning is a kind of crowdsourcing computing, where a task publisher shares profit …

Prepartition: Load Balancing Approach for Virtual Machine Reservations in a Cloud Data Center

2021 · arXiv (Cornell University)

Load balancing is vital for the efficient and long-term operation of cloud data centers. With virtualization, post (reactive) migration of virtual machines after allocation is the traditional way for load balancing and consolidation. However, reactive …

Holmes

2022

Co-location of latency-critical services with best-effort batch jobs is commonly adopted in production systems to increase resource utilization. Although memory and CPU isolation have been extensively studied, we find Simultaneous Multi-Threading (SMT) technology imposes non-trivial …

Integrated Sensing and Communication for Edge Inference with End-to-End Multi-View Fusion

2024 · arXiv (Cornell University)

Integrated sensing and communication (ISAC) is a promising solution to accelerate edge inference via the dual use of wireless signals. However, this paradigm needs to minimize the inference error and latency under ISAC co-functionality interference, …

Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Training

2024 · arXiv (Cornell University)

This paper presents ProFL, a new framework that effectively addresses the memory constraints in FL. Rather than updating the full model during local training, ProFL partitions the model into blocks based on its original architecture …