activity
20182024
most citedSage: Leveraging ML to Diagnose Unpredictable Performance in Cloud Microservices

15 citations · 45 across the 18 of their papers we have counts for

collaborators
Showing 2018Show all

5 papers · 1 filter

cs.DC2018

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…

cs.DC2018

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…

cs.DC2018

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…

cs.DC2018

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…

cs.PF2018

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…