COSTREAM: Learned Cost Models for Operator Placement in Edge-Cloud Environments

Kernekoncepter

COSTREAM is a novel learned cost model that accurately predicts the execution costs of streaming queries in edge-cloud environments, enabling optimal operator placement.

Resumé

The article introduces COSTREAM, a learned cost model for Distributed Stream Processing Systems (DSPS) that focuses on accurate predictions of execution costs for streaming queries in edge-cloud environments. The core idea is to find an initial placement of operators across heterogeneous hardware to optimize query performance. The article highlights the importance of initial operator placement in IoT scenarios and the challenges posed by heterogeneous hardware. It discusses the limitations of existing approaches and presents COSTREAM as a solution that does not rely on runtime information, enabling an initial placement selection. The article details the novel model architecture based on Graph Neural Networks (GNN) and transferable features used for generalization to unseen queries and hardware. Experimental evaluation includes accuracy assessments, generalization tests, and ablation studies.

Introduction

• DSPS crucial for high-performance applications.
• Importance of efficient operator placement in IoT scenarios.
• Challenges with heterogeneous hardware.

Existing Approaches Limitations

• Emphasis on online reconfiguration neglecting initial placement.
• Gap in addressing hardware and network heterogeneity.
• Time-consuming monitoring approaches causing overheads.

Novel Approach with COSTREAM

• Introduction of COSTREAM as a learned cost model.
• Predicting expected performance before query execution.
• Importance of transferable features for generalization.

Model Architecture and Training Procedure

• Novel GNN-based model architecture.
• Transferable features selection for prediction accuracy.

Benchmark Creation

• Development of a new benchmark dataset with diverse queries and hardware configurations.

Experimental Evaluation

• Assessment through various experiments to evaluate prediction accuracy, generalization capabilities, and impact analysis.

Statistik

COSTREAMは、既存のコストモデルベースのアプローチに比べて、初期オペレータ配置の問題を解決するために高い精度で予測します。

Citater

"Placing a stream processing operator on weak hardware resources can lead to delays or even crashes."
"COSTREAM enables optimal operator placement without relying on runtime information."

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COSTREAM

by Roman Heinri... kl. arxiv.org 03-14-2024

https://arxiv.org/pdf/2403.08444.pdf

Dybere Forespørgsler

How can COSTREAM be applied beyond edge-cloud environments

COSTREAM can be applied beyond edge-cloud environments by leveraging its learned cost model for operator placement in various distributed stream processing systems. The key lies in the transferable features and generalizability of the model, allowing it to adapt to different hardware configurations and query patterns. For instance, COSTREAM's ability to predict costs accurately for unseen workloads and hardware makes it versatile enough to be utilized in a range of settings beyond just edge-cloud environments. By training the model on diverse datasets that encompass a wide array of scenarios, COSTREAM can effectively optimize operator placements in different distributed systems.

What are potential drawbacks or criticisms of using a cost-based approach like COSTREAM

One potential drawback or criticism of using a cost-based approach like COSTREAM is the reliance on accurate predictions based on available features. If there are limitations or inaccuracies in the feature selection process, it could lead to suboptimal placement decisions. Additionally, as with any machine learning model, there is always a risk of overfitting or underfitting if not properly validated and tested across various scenarios. Another criticism could be related to scalability issues when dealing with large-scale deployments where real-time adjustments are required frequently.

How might advancements in hardware technology impact the effectiveness of models like COSTREAM

Advancements in hardware technology can significantly impact the effectiveness of models like COSTREAM by influencing the accuracy and efficiency of cost predictions for operator placements. For example, improvements in CPU performance, RAM capacity, network bandwidth, and latency can alter how operators interact with hardware resources during execution. As hardware becomes more powerful and efficient, models like COSTREAM may need updates or recalibration to account for these changes accurately. Moreover, advancements such as specialized accelerators (e.g., GPUs) or new networking technologies could introduce additional factors that need consideration within the cost estimation process.