3 citations · 6 across the 8 of their papers we have counts for
8 papers
Hestia: Hyperthread-Level Scheduling for Cloud Microservices with Interference-Aware Attention
Dingyu Yang, Fanyong Kong, Jie Dai +5
Modern cloud servers routinely co-locate multiple latency-sensitive microservice instances to improve resource efficiency. However, the diversity of microservice behaviors, coupled…
EDGC: Entropy-driven Dynamic Gradient Compression for Efficient LLM Training
Qingao Yi, Jiaang Duan, Jun Zhang +11
Training large language models (LLMs) at scale incurs substantial communication overhead, while static gradient compression cannot adapt to gradient evolution and may degrade model…
GFS: A Preemption-aware Scheduling Framework for GPU Clusters with Predictive Spot Instance Management
Jiaang Duan, Shenglin Xu, Shiyou Qian +15
The surge in large language models (LLMs) has fundamentally reshaped the landscape of GPU usage patterns, creating an urgent need for more efficient management strategies. While cl…
Atys: An Efficient Profiling Framework for Identifying Hotspot Functions in Large-scale Cloud Microservices
Jiaqi Sun, Dingyu Yang, Shiyou Qian +2
To handle the high volume of requests, large-scale services are comprised of thousands of instances deployed in clouds. These services utilize diverse programming languages and are…
LKD-KGC: Domain-Specific KG Construction via LLM-driven Knowledge Dependency Parsing
Jiaqi Sun, Shiyou Qian, Zhangchi Han +5
Knowledge Graphs (KGs) structure real-world entities and their relationships into triples, enhancing machine reasoning for various tasks. While domain-specific KGs offer substantia…
Mitigating Interference of Microservices with a Scoring Mechanism in Large-scale Clusters
Dingyu Yang, Kangpeng Zheng, Shiyou Qian +2
Co-locating latency-critical services (LCSs) and best-effort jobs (BEJs) constitute the principal approach for enhancing resource utilization in production. Nevertheless, the co-lo…
Humas: A Heterogeneity- and Upgrade-aware Microservice Auto-scaling Framework in Large-scale Data Centers
Qin Hua, Dingyu Yang, Shiyou Qian +3
An effective auto-scaling framework is essential for microservices to ensure performance stability and resource efficiency under dynamic workloads. As revealed by many prior studie…
MOPAR: A Model Partitioning Framework for Deep Learning Inference Services on Serverless Platforms
Jiaang Duan, Shiyou Qian, Dingyu Yang +3
With its elastic power and a pay-as-you-go cost model, the deployment of deep learning inference services (DLISs) on serverless platforms is emerging as a prevalent trend. However,…