15 citations · 64 across the 23 of their papers we have counts for
23 papers
Analytically-Driven Resource Management for Cloud-Native Microservices
Yanqi Zhang, Zhuangzhuang Zhou, Sameh Elnikety +1
Resource management for cloud-native microservices has attracted a lot of recent attention. Previous work has shown that machine learning (ML)-driven approaches outperform traditio…
Towards Fast, Adaptive, and Hardware-Assisted User-Space Scheduling
Lisa, Li, Nikita Lazarev +7
Modern datacenter applications are prone to high tail latencies since their requests typically follow highly-dispersive distributions. Delivering fast interrupts is essential to re…
Mystique: Enabling Accurate and Scalable Generation of Production AI Benchmarks
Mingyu Liang, Wenyin Fu, Louis Feng +5
Building large AI fleets to support the rapidly growing DL workloads is an active research topic for modern cloud providers. Generating accurate benchmarks plays an essential role…
QoS-Aware Resource Management for Multi-phase Serverless Workflows with Aquatope
Zhuangzhuang Zhou, Yanqi Zhang, Christina Delimitrou
Multi-stage serverless applications, i.e., workflows with many computation and I/O stages, are becoming increasingly representative of FaaS platforms. Despite their advantages in t…
End-to-End Application Cloning for Distributed Cloud Microservices with Ditto
Mingyu Liang, Yu Gan, Yueying Li +4
We present Ditto, an automated framework for cloning end-to-end cloud applications, both monolithic and microservices, which captures I/O and network activity, as well as kernel op…
A Hardware-Software Stack for Serverless Edge Swarms
Liam Patterson, David Pigorovsky, Brian Dempsey +6
Swarms of autonomous devices are increasing in ubiquity and size, making the need for rethinking their hardware-software system stack critical. We present HiveMind, the first swarm…
Sage: Leveraging ML to Diagnose Unpredictable Performance in Cloud Microservices
Yu Gan, Mingyu Liang, Sundar Dev +2
Cloud applications are increasingly shifting from large monolithic services, to complex graphs of loosely-coupled microservices. Despite their advantages, microservices also introd…
Sinan: Data Driven Resource Management for Cloud Microservices
Yanqi Zhang, Weizhe Hua, Zhuangzhuang Zhou +2
Cloud applications are increasingly shifting to interactive and loosely-coupled microservices. Despite their advantages, microservices complicate resource management, due to inter-…
Dagger: Accelerating RPCs in Cloud Microservices Through Tightly-Coupled Reconfigurable NICs
Nikita Lazarev, Shaojie Xiang, Neil Adit +2
The ongoing shift of cloud services from monolithic designs to microservices creates high demand for efficient and high performance datacenter networking stacks, optimized for fine…
Sinan: Data-Driven, QoS-Aware Cluster Management for Microservices
Yanqi Zhang, Weizhe Hua, Zhuangzhuang Zhou +2
Cloud applications are increasingly shifting from large monolithic services, to large numbers of loosely-coupled, specialized microservices. Despite their advantages in terms of fa…
Sage: Using Unsupervised Learning for Scalable Performance Debugging in Microservices
Yu Gan, Mingyu Liang, Sundar Dev +2
Cloud applications are increasingly shifting from large monolithic services to complex graphs of loosely-coupled microservices. Despite the advantages of modularity and elasticity…
CuttleSys: Data-Driven Resource Management forInteractive Applications on Reconfigurable Multicores
Neeraj Kulkarni, Gonzalo Gonzalez-Pumariega, Amulya Khurana +3
Multi-tenancy for latency-critical applications leads to re-source interference and unpredictable performance. Core reconfiguration opens up more opportunities for colocation,as it…
Dagger: Towards Efficient RPCs in Cloud Microservices with Near-Memory Reconfigurable NICs
Nikita Lazarev, Neil Adit, Shaojie Xiang +2
Cloud applications are increasingly relying on hundreds of loosely-coupled microservices to complete user requests that meet an applications end-to-end QoS requirements. Communicat…
HiveMind: A Scalable and Serverless Coordination Control Platform for UAV Swarms
Justin Hu, Ariana Bruno, Brian Ritchken +4
Swarms of autonomous devices are increasing in ubiquity and size. There are two main trains of thought for controlling devices in such swarms; centralized and distributed control.…
Ripple: A Practical Declarative Programming Framework for Serverless Compute
Shannon Joyner, Michael MacCoss, Christina Delimitrou +1
Serverless computing has emerged as a promising alternative to infrastructure- (IaaS) and platform-as-a-service (PaaS)cloud platforms for applications with ample parallelism and in…
uqSim: Scalable and Validated Simulation of Cloud Microservices
Yanqi Zhang, Yu Gan, Christina Delimitrou
Current cloud services are moving away from monolithic designs and towards graphs of many loosely-coupled, single-concerned microservices. Microservices have several advantages, in…
An Open-Source Benchmark Suite for Cloud and IoT Microservices
Yu Gan, Yanqi Zhang, Dailun Cheng +21
Cloud services have recently started undergoing a major shift from monolithic applications, to graphs of hundreds of loosely-coupled microservices. Microservices fundamentally chan…
Leveraging Deep Learning to Improve the Performance Predictability of Cloud Microservices
Yu Gan, Yanqi Zhang, Kelvin Hu +4
Performance unpredictability is a major roadblock towards cloud adoption, and has performance, cost, and revenue ramifications. Predictable performance is even more critical as clo…
The Architectural Implications of Microservices in the Cloud
Yu Gan, Christina Delimitrou
Cloud services have recently undergone a shift from monolithic applications to microservices, with hundreds or thousands of loosely-coupled microservices comprising the end-to-end…
To Centralize or Not to Centralize: A Tale of Swarm Coordination
Justin Hu, Ariana Bruno, Drew Zagieboylo +7
Large swarms of autonomous devices are increasing in size and importance. When it comes to controlling the devices of large-scale swarms there are two main lines of thought. Centra…
Seer: Leveraging Big Data to Navigate the Increasing Complexity of Cloud Debugging
Yu Gan, Meghna Pancholi, Dailun Cheng +3
Performance unpredictability in cloud services leads to poor user experience, degraded availability, and has revenue ramifications. Detecting performance degradation a posteriori h…
Mage: Online Interference-Aware Scheduling in Multi-Scale Heterogeneous Systems
Francisco Romero, Christina Delimitrou
Heterogeneity has grown in popularity both at the core and server level as a way to improve both performance and energy efficiency. However, despite these benefits, scheduling appl…
Pliant: Leveraging Approximation to Improve Datacenter Resource Efficiency
Neeraj Kulkarni, Feng Qi, Christina Delimitrou
Cloud multi-tenancy is typically constrained to a single interactive service colocated with one or more batch, low-priority services, whose performance can be sacrificed when deeme…