Intelligent distributed calculation management system and method based on DAG (Directed Acyclic Graph)
A technology of distributed computing and management methods, applied in the field of distributed computing management, can solve problems such as inability to proceed, waste of system resources, unsuitable for small file storage, etc., to achieve flexible scheduling strategies, improvement of various indicators, and flexible scheduling strategies Effect
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Embodiment 1
[0052] combine figure 2 , the DAG-based intelligent distributed computing management system provided in this embodiment, the system has a master-slave distribution structure, including: a root node, at least one core node that is connected to the root node, and a core node that is connected to each core node operating node;
[0053] The operation node is a leaf node or a core node, and the leaf node is the basic task execution unit in the system;
[0054] Wherein, each core node corresponds to a running pool, and the running pool has a DAG graph structure; each running pool has a parent running pool and / or a child running pool.
[0055]Further, the leaf nodes are functions, modules or files.
[0056] The DAG-based intelligent distributed computing management system provided by the embodiment of the present invention is a distributed software architecture. Based on this architecture, job tasks can be subdivided and divided into execution units with smaller granularity for ta...
Embodiment 2
[0062] combine image 3 The DAG-based intelligent distributed computing management method provided in the embodiment of the present invention is applied to the system provided in Embodiment 1, and the method includes:
[0063] Step S1, obtaining job tasks received by the root node;
[0064] Step S2, splitting the job tasks into job DAG graphs;
[0065] Step S3, adding the job DAG graph into the historical running pool, and merging according to the set rules to form the current running pool;
[0066] In step S4, task allocation is performed according to the vertices of the current running pool, so as to implement granular processing of job tasks.
[0067] The DAG-based intelligent distributed computing management method provided by the embodiment of the present invention is applied to the system provided in the first embodiment, wherein the system is a distributed software architecture, based on this architecture, it is possible to subdivide job tasks and disassemble Divide ...
Embodiment 3
[0100] This embodiment is a specific exemplary description, and specifically, the system in Embodiment 1 or Embodiment 2 is used to establish a machine learning platform. The established platform needs to meet the following functions: support multi-users, be able to support various databases, and keep the overall utilization rate of the system at a high level.
[0101] A complete machine learning task usually requires data integration, data preprocessing, training, evaluation and comparison. The task of data integration is to collect the data that needs to be trained from different storage media, so this process is usually mainly transmission and retrieval operations; data preprocessing usually includes data cleaning, missing value filling, etc.; training It is a work that requires iterative calculations, and is usually the most time-consuming in the entire machine learning process.
[0102] According to the process of machine learning, we divide this platform into three kern...
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