Trace-Based Runtime Prediction of Reoccurring Data-Parallel Processing Jobs

Table of Contents

1. Introduction

1.1 Motivation

1.2 Problem Description

1.3 Contribution

1.4 Framework and Structure

2. Background

2.1 Use Case Description

2.2 Data Parallel Processing Workloads

2.2.1 Cloud Computing Scheduler

2.2.2 Task Dependencies

2.2.3 Recurrent Jobs

2.3 Graph Theory

2.3.1 Directed Acyclic Graphs

2.3.2 Definitions

2.4 Graph Embedding

2.4.1 GNN

2.5 Data Clustering and Classification

2.5.1 Rule-Based Classification

2.5.2 K-Means

2.5.3 DBSCAN

2.5.4 OPTICS

2.5.5 Clustering Evaluation

2.6 Linear Regression

2.7 Evaluation Metrics in Linear Models

3. Approach

3.1 Overview

3.2 Data Cleaning and Integration

3.2.1 Data Cleaning

3.2.2 DAGs Extraction

3.3 Graph Embedding and Clustering

3.3.1 Graph Embedding

3.3.2 Clustering

3.3.3 Runtime Prediction

4. Implementation

4.1 DAGs Extraction

4.1.1 Data Preprocessing

4.1.2 DAGs Objects

4.1.3 Target Variables Definition

4.2 Graph Embedding

4.2.1 Model Structure

4.2.2 Model Training

4.3 Data Clustering Techniques

4.3.1 Preprocessing

4.3.2 DBSCAN

4.3.3 OPTICS

4.4 Runtime Prediction

5. Evaluation and Discussion

5.1 Dataset

5.1.1 Exceptions Handling

5.1.2 Data Characteristics

5.2 Rule-Based Classification

5.2.1 Recurring Jobs Detection

5.2.2 Rule-Based Classification

5.2.3 Runtime Prediction

5.2.4 Evaluation

5.3 GNN Evaluation

5.4 OPTICS Clustering

5.5 K-means Clustering

5.5.1 Model Setup

5.5.2 Evaluation

5.6 DBSCAN Clustering

5.6.1 Runtime Prediction

5.6.2 Comparison with the Baseline

5.7 Limitations

5.8 Discussions

6. State of the Art

6.1 Graph Representation Learning

6.2 Runtime and Resource Usage Prediction

6.3 Reoccurring Jobs

6.4 Alibaba Cluster Dataset

7. Conclusion

7.1 Provision of the Code

7.2 Provision of the Datasets

Objectives and Research Themes

This thesis aims to develop a framework for predicting the runtime of incoming data-parallel jobs in cloud environments. By leveraging the structural similarities of reoccurring jobs through Graph Neural Networks (GNN) and clustering techniques, the research seeks to improve prediction accuracy and enable better resource allocation.

• Graph representation learning using GNNs to embed Directed Acyclic Graphs (DAGs).
• Application of clustering algorithms (OPTICS, DBSCAN, K-means) to identify meaningful groups of reoccurring jobs.
• Runtime estimation via linear regression models tailored to specific job clusters.
• Evaluation using large-scale production trace data to validate the predictive value of job dependency structures.

Book Excerpt

Summary of Chapters

1. Introduction: This chapter provides an overview of the motivation behind runtime prediction in cloud environments, defines the problem, and outlines the thesis structure.

2. Background: This chapter establishes the necessary domain knowledge, covering parallelism, graph theory, graph embedding (GNN), and clustering/classification techniques.

3. Approach: This chapter introduces the methodology for data cleaning, DAG extraction, GNN-based embedding, and the clustering-based runtime prediction framework.

4. Implementation: This chapter details the technical execution of the proposed approach, including data preprocessing, feature selection, GNN architecture, and clustering setup.

5. Evaluation and Discussion: This chapter presents the statistical analysis of the dataset, evaluates the performance of the proposed methods against baselines, and discusses the results and limitations.

6. State of the Art: This chapter reviews existing literature related to graph representation learning, cloud resource prediction, and the analysis of the Alibaba cluster dataset.

7. Conclusion: This chapter summarizes the research findings, contributions, and potential areas for future work regarding job dependency embeddings.

Keywords

Cloud Computing, Data-Parallel Jobs, Runtime Prediction, Directed Acyclic Graphs (DAGs), Graph Neural Networks (GNN), Graph Embedding, Data Clustering, DBSCAN, OPTICS, Resource Allocation, Alibaba Cluster Trace, Job Dependencies, Machine Learning, Runtime Estimation, Feature Selection

Frequently Asked Questions

What is the core focus of this research?

The thesis focuses on predicting the runtime of data-parallel jobs in cloud environments by identifying and clustering reoccurring job patterns based on their structural dependencies.

What are the primary themes addressed?

The central themes are cloud workload management, graph representation learning, unsupervised clustering techniques, and empirical evaluation using large-scale production traces.

What is the main objective of this work?

The primary goal is to improve the accuracy of job runtime predictions by analyzing the Directed Acyclic Graph (DAG) structure of tasks, thereby assisting cloud providers in efficient resource scheduling.

Which scientific methods are utilized?

The study employs Graph Neural Networks (GNN) for embedding DAGs, clustering algorithms such as DBSCAN and OPTICS to identify job groups, and linear regression models to predict runtime per cluster.

What content is covered in the main body of the text?

The main body covers the extraction of DAGs from raw trace data, the design of a GNN model to create fixed-size vector representations, and a comparative study of different clustering and prediction strategies.

Which keywords characterize this thesis?

Key terms include Cloud Computing, Graph Neural Networks, Runtime Prediction, DAGs, Data Clustering, and Resource Utilization.

How does the author define recurring jobs in this context?

Recurring jobs are identified as tasks sharing isomorphic structural dependencies (DAGs), allowing the framework to group similar operations together even if the input datasets vary.

What impact does the research have on existing prediction baselines?

The proposed framework demonstrates a significant improvement in accuracy, specifically reducing the mean absolute error of baseline predictions by 27%.

How is the Alibaba cluster dataset used?

The dataset is used as a real-world benchmark with over 200,000 jobs to evaluate the robustness and reliability of the proposed dependency-based prediction approach.