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System for detecting abnormity of proxy cache cluster based on automatic computing

A proxy caching and autonomous computing technology, applied in computing, transmission systems, digital transmission systems, etc., can solve problems such as the large scale of proxy cluster systems and the inability to detect exceptions in time.

Active Publication Date: 2013-12-11
HARBIN INST OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem that the existing proxy cluster system has a large scale and can not detect in time when an abnormality occurs, causing serious consequences, the present invention provides an autonomous computing-based proxy cache cluster abnormality detection system

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  • System for detecting abnormity of proxy cache cluster based on automatic computing
  • System for detecting abnormity of proxy cache cluster based on automatic computing
  • System for detecting abnormity of proxy cache cluster based on automatic computing

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specific Embodiment approach 1

[0020] Specific implementation mode 1. Combination figure 1 This specific embodiment will be described. Anomaly detection system for proxy cache cluster based on autonomous computing, which includes:

[0021] The state detection module is used to monitor the state of the distributed agent cluster and obtain detailed data when the distributed agent cluster is running;

[0022] The state self-perception module is used to receive the state data provided by the state detection module, analyze the state data, and identify the current running state of the distributed agent cluster;

[0023] The state self-recovery module is used to judge the parameters that need to be adjusted and the degree of adjustment according to the operating state results obtained by the state self-aware module, and send a parameter adjustment command to the algorithm execution module;

[0024] The algorithm execution module is used to execute the parameter adjustment command sent by the self-recovery modul...

specific Embodiment approach 2

[0029] Embodiment 2. This embodiment is different from Embodiment 1 in that the monitoring items of the state detection module are hardware resource monitoring items, network resource monitoring items and service resource monitoring items;

[0030] Hardware resource monitoring items, used to monitor CPU usage C, memory usage M and disk I / O usage D;

[0031] The network resource monitoring item is used to monitor the utilization rate P of the number of connections and the utilization rate B of the network bandwidth;

[0032] The service resource monitoring item is used to monitor the cache URL request frequency F.

[0033] The status monitoring module is the basis of the entire self-decision framework. It regularly collects the system information of the HTTP cache server, obtains the monitoring data value, and performs normalization processing. Considering comprehensively, according to the resource attributes used by the HTTP cache server in the distributed proxy cache system,...

specific Embodiment approach 3

[0035] Specific Embodiment Three. The difference between this specific embodiment and specific embodiment two is that the hardware resource monitoring item is used to monitor CPU usage C, memory usage M and disk I / O usage D as follows:

[0036] Ⅰ. The calculation method of CPU usage C is:

[0037] The monitoring module collects the total time T of the CPU usage twice tot and CPU idle time T idle , the interval t between two data collections is 5s, and the CPU usage C is obtained by making a difference respectively and dividing by the interval time:

[0038] C=1-(T idle (t i+1 )-T idle (t i )) / ((T tot (t i+1 )-T tot (t i ))*t)

[0039] t i Indicates the time of the last collection of information;

[0040] When monitoring CPU utilization information, check the / proc / stat file. The multiple CPU usage information of the HTTP cache server is stored in this file, and the summary information of the CPU operation is located in the cpu field line. This line records the ti...

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Abstract

The invention belongs to the field of optics and discloses a system for detecting abnormity of a proxy cache cluster based on automatic computing, for solving the problem that the existing proxy cluster system is huge in scale and serious results are caused if abnormity cannot be detected in time. The system comprises a state detection module, a state self-sensing module, a state self-recovery module and an algorithm execution module, wherein the state detection module is used for monitoring the state of distributed proxy clusters so as to acquire detailed data when the distributed proxy clusters runs; the state self-sensing module is used for receiving state data provided by the state detection module, analyzing the state data and identifying the running state of the current distributed proxy clusters; the state self-recovery module is used for judging parameters to be adjusted and adjustment degrees according to the running state results acquired by the state self-sensing module, and transmitting a parameter adjustment command to the algorithm execution module; the algorithm execution module is used for executing the parameter adjustment command transmitted by the state self-recovery module and dynamically changing running parameters. The system is applied to a proxy cluster system.

Description

technical field [0001] The invention relates to an abnormal detection system of a proxy cache cluster. Background technique [0002] The core of autonomic computing is self-management. Just like the entire nervous system of the human body, it senses the changes of certain factors in its own internal and external environments, so as to adjust and change the state autonomously to adapt to new changes. Unlike other management models in the past, the entire perception and repair process of autonomic computing does not require human intervention. At present, the research related to autonomic computing is mainly concentrated in IBM, which analyzes various problems to be faced when designing autonomous computing systems and understanding the behavior of autonomous computing systems. Jann et al. study dynamic configuration methods for autonomic computing. Hariri of the University of Arizona developed AUTONOMIA, an autonomous computing environment. In China, the Institute of Compu...

Claims

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Application Information

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IPC IPC(8): H04L12/26H04L29/08G06F19/00
Inventor 何慧张伟哲李乔王冬王健范国涛秦泓洋
Owner HARBIN INST OF TECH
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