Scale: Deep Reinforcement Learning for Container Scheduling in Serverless Edge Computing

2026-05-15

AI Summary

This work addresses the challenge of efficient container scheduling in dynamic and heterogeneous edge environments, where balancing service-level objectives (SLOs), resource utilization, and data locality is critical. To this end, the authors propose Scale, a novel framework that, for the first time, unifies SLO constraints, end-to-end latency, and data locality into a single optimization model and leverages policy-based deep reinforcement learning to achieve joint optimization. The approach significantly enhances scheduling performance while maintaining system stability. Experimental evaluation on a large-scale real-world dataset from Huawei Cloud demonstrates that Scale achieves scheduling quality within 1.11–1.15× of the optimal solution obtained by integer linear programming, while accelerating decision-making by up to 99%.

Abstract

Serverless computing has emerged as a promising computing paradigm for edge computing. However, adopting the event driven model in highly dynamic, heterogeneous, and distributed edge systems poses significant challenges in request placement and resource management. Efficiently allocating requests to containers is therefore critical to reduce resource over provisioning and unnecessary data movement. This paper proposes Scale, a Service Level Objective aware container scheduling and resource allocation framework designed for serverless edge computing. Scale employs a policy based deep reinforcement learning algorithm to balance system stability and performance under dynamic workloads. The design jointly incorporates SLO constraints, end to end latency, and data locality into the scheduling decision process. Extensive simulations using large scale real world datasets from Huawei Cloud demonstrate that Scale achieves solutions within a factor of 1.11 to 1.15 of a state of the art Integer Linear Programming solver, while reducing decision making time by up to 99%.

Problem

• serverless edge computing
• container scheduling
• request placement
• resource management
• data locality

Innovation

• deep reinforcement learning
• serverless edge computing
• container scheduling
• SLO-aware
• data locality