VLDB 2024

Meta Info

Homepage: https://vldb.org/2024/

Paper list

• https://vldb.org/2024/?program-schedule

• https://www.vldb.org/pvldb/volumes/17/

Papers

Resource Management

• DL training workloads

  • Saturn: An Optimized Data System for Multi-Large-Model Deep Learning Workloads [Paper] [Code]

    • UCSD

    • Saturn -> SPASE: Select a Parallelism, Allocate resources, and Schedule; formulate the joint SPASE problem as an MILP.

• Big data analytic workloads

  • Intelligent Pooling: Proactive Resource Provisioning in Large-scale Cloud Service [Paper]

    • Microsoft

    • Predict usage patterns using a hybrid ML model; optimize the pool size dynamically.

• Job scheduling

  • ResLake: Towards Minimum Job Latency and Balanced Resource Utilization in Geo-distributed Job Scheduling

    • ByteDance

• Autoscaling

  • OptScaler: A Collaborative Framework for Robust Autoscaling in the Cloud [arXiv]

    • Ant Group

• Serverless

  • Resource Management in Aurora Serverless [Paper]

    • AWS

    • Industry Paper

Model Serving

• Approximate Inference

  • Biathlon: Harnessing Model Resilience for Accelerating ML Inference Pipelines [Paper] [arXiv] [Code]

    • CUHK

      • Approximate input features to accelerate inference pipelines.

      • Trade-off between latency and accuracy.

      • Evaluation: All inference pipelines were implemented using Python and scikit-learn; run on CPU servers.

  • InferDB: In-Database Machine Learning Inference Using Indexes [Paper]

    • Hasso Plattner Institute & University of Potsdam & University of Illinois Chicago

    • Approximate ML inference pipelines using index structures available in DBMS.

    • Predictions are preserved in the embedding space; select binned features for indexing.

    • IMO: Aggressive...

• Edge Computing

  • SmartLite: A DBMS-based Serving System for DNN Inference in Resource-constrained Environments [Paper] [Code]

    • ZJU & Alibaba

    • SmartLite, a lightweight DBMS

      • Store the parameters and structural information of neural networks as database tables.

      • Implement neural network operators inside the DBMS engine.

      • Quantize model parameters as binarized values, apply neural pruning techniques to compress the models, and transform tensor manipulations into value lookup operations of the DBMS.

Notebook

• ElasticNotebook: Enabling Live Migration for Computational Notebooks [Paper] [arXiv] [Code]

  • UIUC & UMich

  • Live migration via checkpointing/restoration.

  • Reconstruct all variables from a subset of variables.

Feature Stores

• RALF: Accuracy-Aware Scheduling for Feature Store Maintenance [Paper]

  • UC Berkeley

  • Limitations of existing works

    • Naively apply a one-size-fits-all policy as to when/how to update these features.

    • Do not consider query access patterns or impacts on prediction accuracy.

  • Feature store regret: a metric for how much featurization degrades downstream accuracy.

  • Leverage downstream error feedback to minimize feature store regret.

Data Pre-processing

• FusionFlow: Accelerating Data Preprocessing for Machine Learning with CPU-GPU Cooperation [Paper] [Code]

  • UNIST

  • Cooperatively utilizes both CPUs and GPUs to accelerate the data preprocessing stage of DL training that runs the data augmentation algorithm.

  • Orchestrate data preprocessing tasks across CPUs and GPUs while minimizing interference with GPU-based model training.

Deep Learning Recommendation Model (DLRM)

• DLRover: Resource Optimization for Deep Recommendation Models Training at AntGroup [arXiv] [Code]

  • AntGroup & Sichuan University

Graph Neural Network (GNN)

• Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses [Paper] [Code]

  • UIUC & NVIDIA

  • GIDS: GPU Initiated Direct Storage Access -> A data loader to utilize all hardware resources (i.e., CPU memory, storage, and GPU memory)