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Cluster server intelligent dispatching method and system

A cluster server and intelligent scheduling technology, applied in the Internet field, can solve problems such as poor service cluster effect, no consideration of server processing capabilities, and large differences in processing capabilities.

Inactive Publication Date: 2011-01-05
SHENZHEN TEMOBI SCI &TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In the first and second balancing strategies, because there is no feedback information from the server, the balancing strategy cannot be adjusted according to the actual situation. Over time, the load of each server will be unbalanced. If a server suddenly fails, the system cannot learn about the failure. , causing some users to be unable to obtain services; although the third balancing strategy introduces server feedback information, it does not take into account the different processing capabilities of different servers, using absolute load as the measurement standard, and the processing capabilities of servers vary greatly The effect of the service cluster is not good. The fourth balancing strategy takes into account the different processing capabilities of different servers, assigns a static weighting coefficient to each server, and introduces relative load parameters as a measurement standard, which improves load balancing to a certain extent. This is also a balancing strategy we usually adopt; the fifth balancing strategy feeds back the dynamic load information of the server to the balancing strategy. The real-time effect of balancing is the best, but it is difficult to implement

Method used

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  • Cluster server intelligent dispatching method and system
  • Cluster server intelligent dispatching method and system
  • Cluster server intelligent dispatching method and system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0037] The load set on server 1 is 2000 and 1800 users have accessed it, the remaining load is 200, the CPU usage is 60%, and the memory usage is 71%; the load set on server 2 is 2200 and there are already 1800 users User visits, the remaining load is 400, the CPU usage is 55%, the memory usage is 69%, the load set by server 3 is 1900, 1650 users have accessed, the remaining load is 250, the CPU usage is 73%, and the memory occupancy rate is 71%; and server 4 has a load of 2,100 units and has been accessed by 1,800 users, and the remaining load is 300 units, and the CPU occupancy rate is 64%, and the memory occupancy rate is 70%.

[0038] The above data is fed back to the state database by the feedback program 1, 2, 3, and 4. Since the remaining loads of the four servers are respectively 200, 400, 250, and 300, which are all greater than the set residual load of 100, DNS will not consider For the CPU usage and memory usage data of servers 1, 2, 3, and 4, the scoring level of s...

Embodiment 2

[0040] The load set on server 1 is 2000 and 1950 users have accessed it, the remaining load is 50, the CPU usage is 60%, and the memory usage is 71%; the load set on server 2 is 2200 and there are 2140 User visits, the remaining load is 60, the CPU usage is 55%, the memory usage is 69%, the load set by server 3 is 1900, 1830 users have accessed, the remaining load is 70, the CPU usage is 73%, and the memory occupancy rate is 71%; and the server 4 has a load of 2100 units and 2020 users have accessed it, and the remaining load is 300 units, the CPU occupancy rate is 64%, and the memory occupancy rate is 70%.

[0041] The above data is fed back to the state database by feedback programs 1, 2, 3, and 4. Since the remaining loads of the four servers are 50, 60, 70, and 80 respectively, which are all greater than the set residual load of 100, DNS will not consider The remaining loads of servers 1, 2, 3, and 4 are respectively 60%, 55%, 73%, and 64% according to the CPU usage, and t...

Embodiment 3

[0043] The load set by server 1 is 2000, 1950 users have accessed it, the remaining load is 50, the CPU usage rate is 71%, and the memory usage rate is 67%; the load set by server 2 is 2200, and there are 2140 users User visits, the remaining load is 60, the CPU usage is 72%, the memory usage is 66%, the load set by server 3 is 1900, 1830 users have accessed, the remaining load is 70, the CPU usage is 73%, and the memory occupancy rate is 65%; and the server 4 has a load of 2100 units and 2020 users have accessed it, and the remaining load is 300 units, the CPU occupancy rate is 74%, and the memory occupancy rate is 64%.

[0044] The above data is fed back to the state database by the feedback program 1, 2, 3, and 4. Since the remaining loads of the four servers are 50, 60, 70, and 80 respectively, all of which are greater than the set residual load of 100, and the CPU usage They are 71%, 72%, 73%, and 74%, all of which are higher than the set value of 70%. DNS will not consid...

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Abstract

The invention relates to cluster server intelligent dispatching method and system. The cluster server intelligent dispatching system comprises a group of cluster servers, a state database, an IP (Internet Protocol) address database and a DNS (Domain Name Server) server, wherein the group of cluster servers are respectively provided with a feedback program used for collecting the real-time information of the operating states of the servers; the state database is connected with the group of cluster servers and used for recording the real-time information of the operating states of the servers according to the real-time information of the operating states of the servers, wherein the real-time information is collected by the feedback programs; the IP address database is connected with the state database and used for recording the IP address of each server, the geographic position of the IP address of the corresponding server and the information of network operators; and the DNS server is connected with the IP address database, used for distributing same domain names to the cluster servers providing same services, scoring each server in real time according to the operating states inside the state database and the IP information of accessors to obtain the scoring grades of the servers and providing network services for users by selecting an optimum server IP.

Description

technical field [0001] The invention relates to the field of the Internet, in particular to a method and system for intelligent scheduling of cluster servers. Background technique [0002] When there is a bottleneck in the performance of a single server, the cluster system can use a certain mechanism to distribute all requests to multiple node servers through the load balancer, which greatly improves the system's business processing capability. In addition, the load balancer can also monitor the availability of server nodes. At present, the load balancing strategies adopted by the load balancing scheduler are as follows: 1. Round-robin balancing: each processing request for network data traffic is assigned to the internal server in turn, From 1 to N and then start again, this balancing algorithm is suitable for all servers in the server group have the same software and hardware configuration and the average service request is relatively balanced; 2. Random balancing: use a r...

Claims

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Application Information

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IPC IPC(8): H04L29/08H04L29/12H04L12/24
CPCH04L67/16H04L29/12066H04L61/1511H04L67/22H04L67/18H04L61/4511H04L67/51H04L67/535H04L67/52
Inventor 汤敏
Owner SHENZHEN TEMOBI SCI &TECH
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