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Quota-based cluster fuzzy control capacity planning method

A capacity planning and fuzzy control technology, applied in program control design, database distribution/replication, resource allocation, etc., can solve the problems of prominent operation and maintenance cost, non-fault tolerance, and complicated operation.

Pending Publication Date: 2020-08-28
ZHEJIANG UNIV OF TECH
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AI Technical Summary

Problems solved by technology

The algorithm will fill up the free space of a node in the cluster at one time, and often there is an index with a large amount of data on a node. This situation is very unfriendly to later operation and maintenance, and the operation is complicated when disk alarms and data relocation are involved. , takes a long time;
[0005] (2) Non-fault tolerance
However, with the development of business requirements, other aspects of O&M costs brought about by adaptive algorithms have become increasingly prominent, reducing the stability of the cluster to a certain extent

Method used

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  • Quota-based cluster fuzzy control capacity planning method
  • Quota-based cluster fuzzy control capacity planning method
  • Quota-based cluster fuzzy control capacity planning method

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Embodiment Construction

[0128] The present invention will be further described below in conjunction with the accompanying drawings.

[0129] refer to Figure 1 to Figure 5 , a Quota-based cluster fuzzy control capacity planning method, including the following steps:

[0130] Step 1: Initialize the template data Quota, and convert it into Quota according to the storage period and daily data increment provided by the user;

[0131] Quota=(user apply *day) / cap node

[0132] Among them, user apply The resource requested by the user, day is the number of storage days, cap node It is the disk specification of each machine node, for example: the total amount of data is 200G, and the disk of each machine is 3T, then Quota=200 / (3* 1024); if the storage period is 3 days, the daily data increment is 100G, Quota=(3*100) / (3*1024);

[0133] Step 2: Create a cluster region. In the traditional capacity planning method, the role level is limited to a single physical node node in the cluster, and the same clust...

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Abstract

The invention discloses a Quota-based cluster fuzzy control capacity planning method, which comprises the following steps: 1, converting daily data increment into Quota according to a storage period provided by a user side; 2, creating a cluster region, and designing and abstracting concepts rack and region of two layers of logics to plan a cluster; 3, reserving a free-rack buffer pool, and positioning the free-rack buffer pool at 10% of the free-rack buffer pool, wherein all the clusters share a free pool of one elastic cloud; 4, calculating the adjustment amount count of each region according to a fuzzy control capacity planning strategy; 5, carrying out duplicate removal operation; and 6, querying the task of each region, and sequentially checking the capacity expansion and shrinkage task and the capacity shrinkage task which are being executed by each region. The cluster resource utilization rate is improved.

Description

technical field [0001] The invention relates to the problem of cluster resource planning in the big data component Elasticsearch, and in particular to the field of improving the capacity planning algorithm in the Elasticsearch cluster of index templates based on Quota (quota) control. Background technique [0002] In Internet enterprises, with the optimization and iteration process of the search and storage platform based on the big data Elasticsearch engine, various adaptive planning algorithms emerge in an endless stream, striving to efficiently and reasonably manage and control the index data created by users, and improve the utilization rate of cluster resources. It also greatly reduces the cost of manual operation and maintenance. However, with the application of adaptive capacity planning algorithms, the cost of operation and maintenance has become increasingly prominent, and there is the possibility of a single node failure in the cluster. With the application of vari...

Claims

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Application Information

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IPC IPC(8): G06F16/27H04L29/08G06F9/50G06F16/22G06F16/2455
CPCG06F16/27H04L67/10G06F9/5027G06F9/5044G06F16/22G06F16/2455G06F16/24552G06F2209/5011H04L67/568
Inventor 李胜林煜南
Owner ZHEJIANG UNIV OF TECH
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