Justin: Integration of Heterogeneous CPU/memory Scaling in Apache Flink and Its Kubernetes Support

Distributed Stream Processing (DSP) systems execute continuous queries over data streams. To handle workload variations, DSP systems run on clusters of machines and rely on auto-scalers. DSP auto-scalers dynamically adjust the number of parallel instances of operators to support a target rate. State-of-the-art auto-scalers couple CPU and memory scaling, allocating resources as one-size-fits-all packages. This contrasts with operators’ high diversity of requirements for CPU and memory. Justin is an auto-scaler that enables hybrid CPU and memory scaling for DSP operators. Justin monitors both CPU usage and the performance of operators’ storage operations. Its mechanisms enable fine-grain memory allocation for tasks upon a query reconfiguration. The Justin policy identifies individual operators’ memory pressure and decides between adjusting parallelism and/or memory allocation for these operators. Justin is implemented in Apache Flink, extending the Flink Kubernetes Operator and its CPU-only auto-scaler. The artifact includes the code of Justin, the extended Flink Kubernetes Operator, and the evaluation scripts, allowing to reproduce the results of the original publication at IFIP DAIS 2025.