Distributed ML Workload Scaling With Slice-Based Container Execution
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Solution Overview
Problem
Data scientists and data engineers face complexities in scaling big data sets for data processing and machine learning, requiring in-depth technical knowledge of distributed systems, compatibility with existing data and services, strict access controls, and effective team communication.
Innovation Solution
A framework that streamlines deployment and scaling of data processing and machine learning workloads using a user-friendly configuration interface, intelligent data partitioning, and real-time monitoring, enabling users with limited expertise to manage large-scale datasets efficiently, with features like slice key data partitioning, SPMD execution, and robust error handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed systems are used to process big data sets, then processing capacity and scalability are improved, but system complexity and difficulty of operation increase
Solution Approach 1:
The patent introduces a framework layer as an intermediary between users and distributed computing infrastructure. This framework abstracts the complexity of cluster management, resource allocation, and data distribution, allowing users to focus on data processing logic while the framework handles the distributed system complexities.
Solution Approach 2:
The framework segments the distributed data processing system into distinct modular components: data ingestion layer, processing layer, and output layer. Each component can be independently configured, deployed, and scaled, reducing overall system complexity while maintaining processing capacity.
2Adaptability or versatility
If users manage distributed systems directly, then control and customization are improved, but ease of operation deteriorates due to required technical expertise
Solution Approach 1:
The framework serves as a mediator that translates high-level user requirements into detailed distributed system configurations. Users interact with simplified APIs and configuration files rather than directly managing cluster nodes, resource allocation, and data partitioning strategies.
Solution Approach 2:
The framework implements automatic resource provisioning, dynamic load balancing, and self-healing capabilities. The system automatically adjusts to failures and optimizes resource utilization without requiring user intervention, maintaining adaptability while improving ease of operation.
3Reliability
If strict access controls are implemented to protect sensitive data, then data security is improved, but system complexity and operational difficulty increase
Solution Approach 1:
The framework implements access controls and security policies at the configuration stage rather than requiring runtime enforcement. Users define access policies, data classification rules, and authentication mechanisms in advance through configuration files, simplifying operational complexity while maintaining strong security.
4Reliability
If code version compatibility is ensured with existing data and services, then system reliability is improved, but deployment time and complexity increase
Solution Approach 1:
The framework performs compatibility validation and dependency resolution during the configuration and deployment preparation phase. It automatically checks code versions, data schema compatibility, and service dependencies before deployment, preventing runtime errors while streamlining the deployment process.
Data Source
AI summary
Systems and techniques may generally be used for streamlining a deployment and scaling of data processing and machine learning workloads on a distributed system. An example method may include receiving, from a user at a user interface, a plugin command, an input including a name key and a query, and a driver code including a code package. The method may include filtering the query based on a scope of the plugin command, obtaining input data by querying an input measure group, and slicing the obtained input data into one or more slices based on the slicing key. The method may include determining a number of containers to be used, and assigning at least one slice for each container. The method may include executing at each container the respective assigned at least one slice, generating an output for each input, and storing each output at a respective output measure group.