Adaptive System State Based Load Balancing For Web Application Server Cluster Of Heterogeneous Performance Nodes
This research is to propose a new efficient DNS-based load balancing algorithms that can solve a sudden increase of demand for service requests when they are applied to a local Domain Name Service (DNS) server for Web based applications and selected services. It is difficult to predict such a sud...
Saved in:
| 主要作者: | |
|---|---|
| 格式: | Thesis |
| 语言: | English |
| 出版: |
2013
|
| 主题: | |
| 在线阅读: | http://ir.unimas.my/id/eprint/9423/3/Adaptive%20System%20State%20Based%20Load%20Balancing%20For%20Web%20Application%20Server%20Cluster%20of%20Heterogeneous%20Performance%20Nodes.pdf |
| 标签: |
添加标签
没有标签, 成为第一个标记此记录!
|
| 总结: | This research is to propose a new efficient DNS-based load balancing algorithms that can
solve a sudden increase of demand for service requests when they are applied to a local
Domain Name Service (DNS) server for Web based applications and selected services.
It is difficult to predict such a sudden increase in traffic and service requests which is mainly
due to an unbalanced distribution of workload and the unavailability of sufficient physical
computing resources by the service providers. If the load balancing algorithm can
accommodate computing resources of different performance level, then computing resources
from various places can be temporarily placed in the server cluster to address the sudden
increase in demand during peak traffic period.
The proposed algorithm allows an increase in efficient load distribution among multiple Web
application servers because each node may require a different level of system resources and
processing power and it has the ability to adapt with the current system in a rapidly changing
dynamic load environment particularly during the seasonal high traffic demand period.
Finally, from the analyzed and evaluated performance results it can be deduced that the
proposed ASS (Adaptive System State) DNS-based algorithms managed to improve the
overall performance in term of execution time by 78.87%. This result is obtained after
compared to the SPS, PI, COM, RR and lbmnaned algorithms under server cluster scenarios 1
to 6 (Table 4.3) which consists of servers with wide range of performance differences. |
|---|