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Publications

66 papers.

Preprints

Preprint

Ensuring Fair LLM Serving Amid Diverse Applications

Redwan Ibne Seraj Khan, Kunal Jain, Haiying Shen, Ankur Mallick, Anjaly Parayil, Anoop Kulkarni, Steve Kofsky, Pankhuri Choudhary, Renèe St. Amant, Rujia Wang, Yue Cheng, Ali R. Butt, Victor Rühle, Chetan Bansal, Saravan Rajmohan

arXiv

2027

2026

SoCC'26

Allpass: Adaptive, Cost-Efficient, Serverless Scheduling for LLM Fine-Tuning across Heterogeneous GPU Clouds

Yuqi Fu, Ruizhe Shi, Tingfeng Lan, Zhaoyuan Su, Rui Yang, Haoliang Wang, Songqing Chen, Yue Cheng

The ACM Symposium on Cloud Computing 2026 (to appear)

SoCC'26

OpRAG: A Resource-Deterministic Runtime for GPU-Backed Multi-Stage RAG Workflows

Arup Kumar Sarker, Mills Staylor, Aymen Alsaadi, Gregor von Laszewski, Yue Cheng, Shantenu Jha, Geoffrey Fox

The ACM Symposium on Cloud Computing 2026 (to appear)

SC'26

BatchFlow: Shared Batch Reuse and Adaptive Scheduling for Multi-Job Training

Patrick Watters, Xiaolong Ma, Syed Zawad, Jingyuan Zhang, Yue Cheng, Lei Yang, Feng Yan

International Conference for High Performance Computing, Networking, Storage and Analysis (to appear), 2026

2025

SDM'25

Staleness-Alleviated Distributed GNN Training via Online Dynamic-Embedding Prediction

Guangji Bai, Yu, Ziyang,, Zheng Chai, Yue Cheng, Liang Zhao

Proceedings of SIAM International Conference on Data Mining, 2025

2024

2023

VLDB'23

InfiniStore: Elastic Serverless Cloud Storage

Jingyuan Zhang, Ao Wang, Xiaolong Ma, Benjamin Carver, Nicholas John Newman, Ali Anwar, Lukas Rupprecht, Dimitrios Skourtis, Vasily Tarasov, Feng Yan, Yue Cheng

49th International Conference on Very Large Data Bases, 2023

arXivPDFCode

2022

DRBSD-8 '22

Understanding Impact of Lossy Compression on Derivative-related Metrics in Scientific Datasets

Zhaoyuan Su, Sheng Di, Ali Murat Gok, Yue Cheng, Franck Cappello

Proceedings of the 8th International Workshop on Data Analysis and Reduction for Big Scientific Data, 2022

2021

2020

HPDC'20

TiFL: A Tier-Based Federated Learning System

Zheng Chai, Ahsan Ali, Syed Zawad, Stacey Truex, Ali Anwar, Nathalie Baracaldo, Yi Zhou, Heiko Ludwig, Feng Yan, Yue Cheng

Proceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing, 2020

arXivPDF

2019

USENIX NSDI'19

HyperFaaS: A Truly Elastic Serverless Computing Framework

Jingyuan Zhang, Ao Wang, Min Li, Yuan Chen, Yue Cheng

USENIX Symposium on Networked Systems Design and Implementation, 2019

VEE'19

VCPU as a Container: Towards Accurate CPU Allocation for VMs

Li Liu, Haoliang Wang, An Wang, Mengbai Xiao, Yue Cheng, Songqing Chen

Proceedings of the 15th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments, 2019

PDF
Abstract

With our increasing reliance on cloud computing, accurate resource allocation of virtual machines (or domains) in the cloud have become more and more important. However, the current design of hypervisors (or virtual machine monitors) fails to accurately allocate resources to the domains in the virtualized environment. In this paper, we claim the root cause is that the protection scope is erroneously used as the resource scope for a domain in the current virtualization design. Such design flaw prevents the hypervisor from accurately accounting resource consumption of each domain. In this paper, using virtual CPUs as a container we propose to redefine the resource scope of a domain, so that the new resource scope is aligned with all the CPU consumption incurred by this domain. As a demonstration, we implement a novel system, called VASE (vCPU as a container), on top of the Xen hypervisor. Evaluations on our testbed have shown our proposed approach is effective in accounting system-wide CPU consumption incurred by domains, while introducing negligible overhead to the system.

2018

Book Chapter

SDN helps Big Data to optimize Storage

Ali R. Butt, Ali Anwar, Yue Cheng

Book Chapter, Big Data and Software Defined Networks, Editor: Javid Taheri. IET, ISBN 978-1-78561-304-3., 2018

2017

2016

HPDC'16

MOS: Workload-Aware Elasticity for Cloud Object Stores

Ali Anwar, Yue Cheng, Aayush Gupta, Ali R. Butt

Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing, 2016

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Abstract

The use of cloud object stores has been growing rapidly in recent years as they combine key advantages such as HTTP-based RESTful APIs, high availability, elasticity with a "pay-as-you-go" pricing model that allows applications to scale as needed. The current practice is to either use a single set of configuration parameters or rely on statically configured storage policies for a cloud object store deployment, even when the store is used to support different types of applications with evolving requirements. This crucial mismatch between the different applications requirements and capabilities of the object store is problematic and should be addressed to achieve high efficiency and performance.In this paper, we propose MOS, a Micro Object Storage architecture, which supports independently configured microstores each tuned dynamically to the needs of a particular type of workload. We also design an enhancement, MOS++, that extends MOS's capabilities through fine-grained resource management to effectively meet the tenants' SLAs while maximizing resource efficiency. We have implemented a prototype of MOS ++ in OpenStack Swift using Docker containers. Our evaluation shows that MOS ++ can effectively support heterogeneous workloads across multiple tenants. Compared to default and statically configured object store setups, for a two-tenant setup, MOS++ improves the sustained access bandwidth by up to 79% for a large-object workload, while reducing the 95th percentile latency by up to 70.2% for a small-object workload.

2015

PDSW'15

Taming the Cloud Object Storage with MOS

Ali Anwar, Yue Cheng, Aayush Gupta, Ali R. Butt

Proceedings of the 10th Parallel Data Storage Workshop, 2015

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Abstract

Cloud object stores today are deployed using a single set of configuration parameters for all different types of applications. This homogeneous setup results in all applications experiencing the same service level (e.g., data transfer throughput, etc.). However, the vast variety of applications expose extremely different latency and throughput requirements. To this end, we propose MOS, a <u>M</u>icro <u>O</u>bject <u>S</u>torage architecture with independently configured microstores each tuned dynamically for a particular type of workload. We then expose these microstores to the tenant who can then choose to place their data in the appropriate microstore according the latency and throughput requirements of their workloads. Our evaluation shows that compared with default setup, MOS can improve the performance up to 200% for small objects and 28% for large objects while providing opportunity of tradeoff between two.

HPDC'15

CAST: Tiering Storage for Data Analytics in the Cloud

Yue Cheng, M. Safdar Iqbal, Aayush Gupta, Ali R. Butt

Proceedings of the 24th International Symposium on High-Performance Parallel and Distributed Computing, 2015

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Abstract

Enterprises are increasingly moving their big data analytics to the cloud with the goal of reducing costs without sacrificing application performance. Cloud service providers offer their tenants a myriad of storage options, which while flexible, makes the choice of storage deployment non trivial. Crafting deployment scenarios to leverage these choices in a cost-effective manner - under the unique pricing models and multi-tenancy dynamics of the cloud environment - presents unique challenges in designing cloud-based data analytics frameworks.In this paper, we propose CAST, a Cloud Analytics Storage Tiering solution that cloud tenants can use to reduce monetary cost and improve performance of analytics workloads. The approach takes the first step towards providing storage tiering support for data analytics in the cloud. CAST performs offline workload profiling to construct job performance prediction models on different cloud storage services, and combines these models with workload specifications and high-level tenant goals to generate a cost-effective data placement and storage provisioning plan. Furthermore, we build CAST++ to enhance CAST's optimization model by incorporating data reuse patterns and across-jobs interdependencies common in realistic analytics workloads. Tests with production workload traces from Facebook and a 400-core Google Cloud based Hadoop cluster demonstrate that CAST++ achieves 1.21X performance and reduces deployment costs by 51.4% compared to local storage configuration.

EuroSys'15

An In-Memory Object Caching Framework with Adaptive Load Balancing

Yue Cheng, Aayush Gupta, Ali R. Butt

Proceedings of the Tenth European Conference on Computer Systems, 2015

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Abstract

The extreme latency and throughput requirements of modern web applications are driving the use of distributed in-memory object caches such as Memcached. While extant caching systems scale-out seamlessly, their use in the cloud --- with its unique cost and multi-tenancy dynamics --- presents unique opportunities and design challenges.In this paper, we propose MBal, a high-performance in-memory object caching framework with adaptive <u>M</u>ultiphase load <u>B</u>alancing, which supports not only horizontal (scale-out) but vertical (scale-up) scalability as well. MBal is able to make efficient use of available resources in the cloud through its fine-grained, partitioned, lockless design. This design also lends itself naturally to provide adaptive load balancing both within a server and across the cache cluster through an event-driven, multi-phased load balancer. While individual load balancing approaches are being lever-aged in in-memory caches, MBal goes beyond the extant systems and offers a holistic solution wherein the load balancing model tracks hotspots and applies different strategies based on imbalance severity -- key replication, server-local or cross-server coordinated data migration. Performance evaluation on an 8-core commodity server shows that compared to a state-of-the-art approach, MBal scales with number of cores and executes 2.3x and 12x more queries/second for GET and SET operations, respectively.

2013

SoCC'13

High Performance In-Memory Caching through Flexible Fine-Grained Services

Yue Cheng, Aayush Gupta, Anna Povzner, Ali R. Butt

Proceedings of the 4th Annual Symposium on Cloud Computing, 2013

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Abstract

In-memory object caches are extensively used in today's web installations [1, 6]. Most existing systems adopt monolithic storage models and engineer optimizations on specific workload characteristics [3, 6] or operations [4, 5]. Such optimizations are insufficient as large-scale cloud workloads typically exhibit both temporal and spatial shifts - requirements vary within the same deployment over time and different parts of the same workload demonstrate different access patterns. To this end, we propose a caching tier that supports differentiated services in multiple dimensions. Since there is no best "one-size-fits-all" solution for all workload requirements, we argue that a fine-grained modular design will provide both high performance and flexibility in supporting multiple services.