CH-AL: A Consistent Hashing-Based Adaptive Load Balancing Strategy for Serverless Computing

With the rise of serverless computing, Function-as-a-Service (FaaS) has become an important paradigm for elastic, on-demand execution. However, cold start latency remains a critical issue that impacts the performance of FaaS platforms. We propose a scheduling strategy called CH-AL, which preserves function locality while ensuring global load balancing. By co-locating functions with shared container image layers, CH-AL enhances OS page cache reuse and reduces cold start overhead. To handle heterogeneous workloads, CH-AL employs a dynamic forwarding policy that redirects invocations based on real-time node load, reducing inter-node load imbalance. We implemented CH-AL on OpenWhisk and evaluated its effectiveness through a series of experiments. The results show that CH-AL improves function throughput by 26% compared to OpenWhisk’s default load balancer, while reducing average latency and inter-node load variance. Unlike prior methods that depend on runtime feedback from worker nodes, CH-AL leverages historical load statistics maintained solely by the load balancer, offering better scalability and stability.