Export Citations
Save this search
Please login to be able to save your searches and receive alerts for new content matching your search criteria.
Flame: A Centralized Cache Controller for Serverless Computing
ASPLOS '23: Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 4March 2023, Pages 153–168https://doi.org/10.1145/3623278.3624769Caching function is a promising way to mitigate coldstart overhead in serverless computing. However, as caching also increases the resource cost significantly, how to make caching decisions is still challenging. We find that the prior "local cache ...
λFS: A Scalable and Elastic Distributed File System Metadata Service using Serverless Functions
ASPLOS '23: Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 4March 2023, Pages 394–411https://doi.org/10.1145/3623278.3624765The metadata service (MDS) sits on the critical path for distributed file system (DFS) operations, and therefore it is key to the overall performance of a large-scale DFS. Common "serverful" MDS architectures, such as a single server or cluster of ...
DataFlower: Exploiting the Data-flow Paradigm for Serverless Workflow Orchestration
ASPLOS '23: Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 4March 2023, Pages 57–72https://doi.org/10.1145/3623278.3624755Serverless computing that runs functions with auto-scaling is a popular task execution pattern in the cloud-native era. By connecting serverless functions into workflows, tenants can achieve complex functionality. Prior research adopts the control-flow ...
- research-articleFebruary 2024
DREAM: A Dynamic Scheduler for Dynamic Real-time Multi-model ML Workloads
ASPLOS '23: Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 4March 2023, Pages 73–86https://doi.org/10.1145/3623278.3624753Emerging real-time multi-model ML (RTMM) workloads such as AR/VR and drone control involve dynamic behaviors in various granularity; task, model, and layers within a model. Such dynamic behaviors introduce new challenges to the system software in an ML ...