Help Center/ Cloud Adoption Framework / Cloud Adoption Framework and Practices/ Solution Design/ Designing a Big Data Architecture/ Big Data Task Scheduling Platform Design
Updated on 2025-06-19 GMT+08:00
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Big Data Task Scheduling Platform Design

When designing the deployment architecture for the big data task scheduling platform on the cloud, you are advised to comply with the following principles:

  • Big data cloud services preferred: If the source is a self-built big data task scheduling platform with required components, and there are equivalent cloud services available on the destination cloud platform which fulfill all functional, performance, and compatibility criteria with minimal assessed reconstruction workloads, you are advised to preferentially use big data cloud services when designing the deployment architecture. Use your existing setup if the destination cloud platform does not have the corresponding big data task scheduling components or if the destination cloud platform's big data task scheduling components have limited compatibility and require significant rework.
  • Minimum reconstruction: If there is no special service requirement, avoid large-scale reconstruction. Design all components for the big data task scheduling platform in 1:1 benchmarking mode. Keep their versions consistent. Before a version upgrade, assess the adaptive reconstruction workloads.
  • Elasticity and scalability: When deploying the big data task scheduling platform on the cloud, pay attention to the elasticity and scalability of the platform. Cloud environments offer flexible compute and storage resources that scale automatically to meet varying demands. Ensure that the task scheduling platform can quickly process increased workloads and support horizontal expansion to meet service requirements.
  • High availability and fault tolerance: Ensure that the task scheduling platform on the cloud is highly available and fault-tolerant. Use the redundancy design and automatic fault recovery mechanism to ensure continuous system availability. For example, use multiple scheduling nodes and backup policies to prevent single points of failure (SPOFs) and ensure that tasks are not interrupted due to node failures.
  • Security and data protection: The task scheduling platform on the cloud must have security and data protection mechanisms. Ensure that proper access control and encryption measures are provided for sensitive data and system components to prevent unauthorized access and data leakage.
  • Performance optimization: When deploying the task scheduling platform on the cloud, pay attention to performance optimization. Optimize resource configurations, task scheduling algorithms, and data distribution policies to improve task execution efficiency and speed. You can also use services and functions provided by the cloud platform, such as cache and data prefetching, to optimize task execution performance.