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Elastic scaling method based on load prediction and system

An elastic scaling and load forecasting technology, applied in the field of cloud computing, can solve problems such as insufficient response to business load fluctuations and reduced availability of elastic services, so as to achieve timely and effective response to application load fluctuations, overcome service delays, and avoid unnecessary resources The effect of consumption

Active Publication Date: 2018-04-13
GUANGDONG YOUMAI INFORMATION COMM TECH
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this method relies on the real-time effectiveness of the monitoring system on the one hand, and the response to business load fluctuations is not timely enough; on the other hand, the load data of all cloud server instances in the scaling group needs to be collected. reduce

Method used

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  • Elastic scaling method based on load prediction and system
  • Elastic scaling method based on load prediction and system
  • Elastic scaling method based on load prediction and system

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Embodiment Construction

[0058] Below in conjunction with accompanying drawing and specific embodiment, the present invention will be further described:

[0059] See figure 1 , the present invention relates to an elastic scaling method based on load forecasting, and its preferred implementation includes the following steps, starting

[0060] In step S101, current service request data is determined according to application load data within a preset historical time range and a first preset rule.

[0061] In general, the system load balancer can be used to collect the traffic data of the cloud server instances in the scaling group, and analyze the collected traffic data to obtain application load data for storage. Based on the historical data analyzed above, that is, in the preset The application load data in the historical time range, and the number of service requests of the system are determined by using the first preset rule. For example, the first preset rule in the present invention may be to obt...

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Abstract

The invention relates to an elastic scaling method based on load prediction and a system. The elastic scaling method comprises the steps of determining the current service request data according to application load data within a preset historical time range and a first preset rule; generating a corresponding scaling rule when the current service request data meets preset scaling requirements so asto trigger a scaling activity request; creating a scaling activity according to the scaling activity request; and executing the scaling activity so as to add or delete a cloud server instance of a scaling group. The elastic scaling method can provide elastic services timely and effectively, realizes on-demand supply of resources and can better adapt to application scenarios of a large-scale cluster.

Description

technical field [0001] The present invention relates to the field of cloud computing, in particular to an elastic scaling method and system based on load forecasting. Background technique [0002] Cloud computing is the growth, usage, and delivery model of Internet-based related services, usually involving the provision of dynamically scalable and often virtualized resources over the Internet. Load balancing is a collection of servers composed of multiple servers in a symmetrical manner. Each server has an equivalent status and can provide services independently without the assistance of other servers; load balancing can evenly distribute customer requests to the server array , so as to provide quick access to important data and solve a large number of concurrent access service problems. The elastic scaling service is a management service that automatically adjusts its elastic computing resources according to the user's business needs and strategies; it can automatically in...

Claims

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Application Information

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IPC IPC(8): H04L29/08
CPCH04L67/1008H04L67/1031H04L67/60
Inventor 陈强王武侠郑均强
Owner GUANGDONG YOUMAI INFORMATION COMM TECH
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