All news

Mix-or-Split schedules LLM inference across devices, edge and cloud using a fine-grained latency model.

A framework that detects and reduces QoS violations caused by noisy neighbors in multi-tenant public clouds.

Deep reinforcement learning task scheduling for mobile edge computing that accounts for providers overselling capacity.

Uses curiosity-driven reinforcement learning to schedule requests collaboratively across mobile edge and cloud servers.

Schedules dependent tasks and manages container image layers together across device, edge and cloud tiers.

CARSS combines online arrival forecasting with co-simulation to adapt job scheduling.

A network flow scheduler for AI clusters that penalizes contention so latency-sensitive flows meet deadlines alongside bulk training traffic.

An online scheduler that uses pre-warmed zygote containers to cut cold-start latency for serverless functions.