Evidence of communication traffic complexity reveals correlation in a within-queue and heterogeneity among queues. We show how a random-effect model can be used to accommodate these kinds of phenomena. We apply a Pareto distribution for arrival (service) time of individual queue for given arrival (service) rate. For modelling potential correlation in arrival (service) times within a queue and heterogeneity of the arrival (service) rates among queues, we use an inverse gamma distribution. This modelling approach is then applied to the cache access log data processed through an Internet server. We believe that our approach is potentially useful in the area of network resource management.
Bibliographical noteFunding Information:
Identification of a proper distribution for arrival (service) times in a queuing system is a crucial part of effective resource allocation, since it is needed in finding the average time a customer spends in a system as well as the average number of customers in the system. Consider the sanitized cache-access log data for the two servers (bo1 and bo2) provided by NLANR (National Laboratory for Applied Network Research) and the National Science Foundation (grants NCR-9616602 and NCR-9521745). Each line of the log file contains various fields such as timestamp, elapsed time, content, and file size.
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Statistics, Probability and Uncertainty