dc.description.abstracten |
Load balancing algorithms play a crucial role in enhancing system scalability, op-
timizing resource utilization, and improving overall performance. However, the
effectiveness of load balancing depends on selecting the appropriate strategy that
aligns with the specific requirements of the system. With a wide range of avail-
able options, it is essential to consider various parameters and system needs when
making the choice. By carefully evaluating these factors, an optimal load balancing
strategy can be selected to achieve the desired outcomes.The goal of this research is
to compare the performance of various load balancing algorithms in different met-
rics. The test results are able to highlight specific performance metrics in which each
algorithm outperformed others, as well as identify the traffic parameters that affect
these metrics. The test environment was carefully designed to ensure consistent and
reliable performance data in each test case. The test system consisted of one load
balancing node and three servers responsible for handling requests, along with a
traffic loader(also a performance testing node). The majority of the data was gener-
ated by a traffic loader implemented using the Locust load testing framework. The
testing environment was developed using the Python programming language and
employed the RSocket transport protocol.GitHub Repository. |
uk |