April 16, 2024, 4:45 a.m. | Zibo Wang, Pinghe Li, Chieh-Jan Mike Liang, Feng Wu, Francis Y. Yan

cs.LG updates on arXiv.org arxiv.org

arXiv:2212.12180v5 Announce Type: replace-cross
Abstract: Achieving resource efficiency while preserving end-user experience is non-trivial for cloud application operators. As cloud applications progressively adopt microservices, resource managers are faced with two distinct levels of system behavior: end-to-end application latency and per-service resource usage. Translating between the two levels, however, is challenging because user requests traverse heterogeneous services that collectively (but unevenly) contribute to the end-to-end latency. We present Autothrottle, a bi-level resource management framework for microservices with latency SLOs (service-level objectives). …

abstract application applications arxiv behavior cloud cloud applications cs.dc cs.lg efficiency experience however latency management managers microservices operators per practical resource efficiency resource management service type usage

Software Engineer for AI Training Data (School Specific)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Python)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Tier 2)

@ G2i Inc | Remote

Data Engineer

@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania

Artificial Intelligence – Bioinformatic Expert

@ University of Texas Medical Branch | Galveston, TX

Lead Developer (AI)

@ Cere Network | San Francisco, US