Performance of scalable resource allocation using load prediction schemes for different traffic scenarios

Previous work was done to compare the static resource allocation (StRA) scheme and scalable resource allocation (ScRA) scheme within an integrated GSM and GPRS network (see Tang, S. and Tafazolli, R., Vehicular Technology Conference - Spring, 2003). In order to maintain the QoS for both, the ScRA scheme assumed that the load history database was a credible estimate of the present state of traffic intensity in the cell area. In cases when there is a sudden change in traffic conditions, the ScRA scheme may not be able to perform well. Hence, in order to cope with sudden changes of traffic in the network, we propose to incorporate prediction schemes into the ScRA algorithm. For comparison purposes, we present and evaluate three prediction strategies, namely linear regression prediction, exponential regression prediction, and current value prediction, in six various traffic conditions. In addition, resource utilisation gains between the StRA scheme and the proposed ScRA schemes are also studied. The findings show that ScRA exponential regression prediction scheme can cope best in all the tested traffic scenarios and experiences an average of 0.5 percent of resource utilisation gain over the StRA scheme.