Analyzing the blocking probability of queuing model for mixed traffic with different distributions | |
| Author | Menama, Menama Rallage Ruchika Samindika Bandara |
| Call Number | AIT Caps. Proj. no.TC-15-06 |
| Subject(s) | Computer networks Queuing theory Telecommunication |
| Note | A capstone project submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Engineering Telecommunications Engineering, School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Series Statement | Caps. Proj. ; no. TC-15-06 |
| Abstract | The Telecommunication sector is a fast developing and most important industry in the world today. Day by day new companies establish and the competition between the companies have increased. As the telecommunication market get mature, the demand for the service from the subscribers increase day by day. Subscribers gather to the networks with best service and fast growing technologies. Companies face many problems in festival times and other occasions because of the traffic creates in the network. They use many strategies to decrease the blocking probability in the network. According NCC recommended standards , it says that a good network’s blocking probability is less than 2 percent. If the blocking probability increased the quality of service of the network decrease. When engineers design a network the main important thing or they give main priority to blocking probability. Generally blocking probability increase in busy hours and festival seasons like when new year day etc. We can decrease the blocking probability by increasing the number of servers in the system. not only servers there are more other parameters which affect the blocking probability. In this project we analyse a multi server queuing model with a mixed traffic. we are going to conclude how the blocking probability vary with the data or packet type and the arrival rate. In this project we are considering how the blocking probability vary when the arrival rate of different data types get vary and also when the arrival rate is same for all the data in the system. We consider four scenarios to compare the blocking probability. First one is injecting the all three data types with same arrival rate as 20 packets per second and compare the blocking probability effect on each data type. In second third and fourth scenarios arrival rate of one distribution change to 30 packets per second while other two kept same as 20 packets per second. |
| Year | 2015 |
| Corresponding Series Added Entry | Asian Institute of Technology. Caps. Proj. ; no. TC-15-06 |
| Type | Capstone Project |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Telecommunications (TC) |
| Chairperson(s) | Teerapat Sanguankotchakorn; |
| Examination Committee(s) | Attaphongse Taparugssanagorn; |
| Degree | Capstone Project (B. Sc.) - Asian Institute of Technology, 2015 |