TransScale: Combined-Approach Elasticity for Stream Processing in Fog Environments

Real-time data processing is a standard requirement in Fog Computing. Dynamically adapting data stream processing frameworks is an essential functionality to handle time-varying workloads efficiently and to optimize resource consumption. However, horizontal scaling alone, by adapting the parallelism and number of provisioned nodes, faces limits when available compute resources are scarce. We propose TransScale, a combined-approach auto-scaler that combines horizontal scaling to approximation computing, controlling it through transprecision computing. We design TransScale to make the approximation method transparent to the system and support context-specific requirements through QoS-driven re-configuration decisions. Based on the policy’s objective, we show that it can reduce re-configuration occurrences, optimize resource utilization and sustain high workloads in resource-constrained environments.