# Demeter: Resource-Efficient Distributed Stream Processing under Dynamic Loads with Multi-Configuration Optimization

**Authors:** Morgan Geldenhuys, Dominik Scheinert, Odej Kao, Lauritz Thamsen  
**arXiv:** [2403.02129](https://arxiv.org/abs/2403.02129)  
**Date:** 2024-03-04  
**Summary page:** https://www.summarizepaper.com/en/paper/2403.02129v1/

## Key points

- In Distributed Stream Processing (DSP), the focus is on real-time processing of vast streams of unbounded data.
- DSP systems can dynamically scale across a cluster of commodity nodes to ensure consistent Quality of Service.
- Optimizing scaleout configurations for resource utilization is a persistent challenge, especially in environments with evolving workload dynamics and inevitable node failures.
- Configuration parameters like memory allocation and checkpointing intervals significantly impact performance and resource usage.
- Suboptimal configurations can lead to high operational costs, subpar performance, or service disruptions.
- Demeter is a novel method introduced to dynamically optimize key DSP system configuration parameters for enhanced resource efficiency.
- Demeter leverages Time Series Forecasting and Multi-Objective Bayesian Optimization to predict future workloads and model runtime behaviors for parameter settings and workload rates.
- These advanced techniques enable proactive adjustment of system configuration parameters based on predicted workload rates to ensure optimized performance and resource utilization.
- Experimental trials with Apache Flink have shown that Demeter significantly enhances operational efficiency in handling dynamic workloads in distributed computing environments.

## Summary

In the realm of Distributed Stream Processing (DSP), the focus is on real-time processing of vast streams of unbounded data. DSP systems have the capability to dynamically scale across a cluster of commodity nodes, ensuring consistent Quality of Service even with fluctuating workloads. However, optimizing scaleout configurations to maximize resource utilization remains a persistent challenge, especially in environments where workload dynamics evolve over time and node failures are inevitable. Configuration parameters such as memory allocation and checkpointing intervals play a crucial role in influencing performance and resource usage. Suboptimal configurations can result in high operational costs, subpar performance, or even unacceptable service disruptions. To address these challenges, a novel method named Demeter has been introduced for dynamically optimizing key DSP system configuration parameters to enhance resource efficiency. Demeter leverages Time Series Forecasting to predict future workloads and Multi-Objective Bayesian Optimization to model runtime behaviors concerning parameter settings and workload rates. These advanced techniques enable the system to assess whether adequate information is available about predicted workload rates to proactively initiate short-lived parallel profiling runs for data collection purposes. Once trained, the models guide the adjustment of multiple interdependent system configuration parameters to ensure optimized performance and resource utilization in response to changing workload rates. Experimental trials conducted on a commodity cluster utilizing Apache Flink have demonstrated that Demeter significantly enhances the operational efficiency of long-running benchmark jobs. Authored by Morgan Geldenhuys, Dominik Scheinert, Odej Kao, and Lauritz Thamsen, this research paper titled "Demeter: Resource-Efficient Distributed Stream Processing under Dynamic Loads with Multi-Configuration Optimization" delves into cutting-edge methodologies aimed at revolutionizing DSP systems' adaptability and efficiency in handling dynamic workloads within distributed computing environments.