International Journal of Computer Networks and Applications

93 - 104

Authors: Vincent O. Nyangaresi, Silvance O. Abeka, Solomon. O. Ogara

In the recent years, many organizations have turned to cloud technology to support their information technology services. The cloud servers are therefore increasingly holding huge and sensitive information belonging to diverse groups of individuals and companies. Additionally, some organizations employ the cloud to provide them with online backup services. One of the most outstanding requirements for cloud customers is availability – the customers must be able to access their information and other resources stored in the cloud any time and from anywhere on the globe. This means that there should be efficient network design such that any delays are averted. The connection between the customer and the cloud can therefore be regarded as delay senstive. Network congestions often lead to delays and packet losses. Transmission control protocol employs four congestion control algorithms – slow start, congestion avoidance, fast retransmit and fast recovery, all of which fail to meet the requirements of delay intolerance. Transmission control protocol pacing has been suggested as a possible solution to delays and packet dropping in computer networks. However, the conventional pacing is static in nature, meaning that constant pauses are introduced between packet transmissions to prevent bursty transmissions which can lead to delays at the receiver buffers. This paper therefore presents a congestion aware packet routing where the delay period is hinged on the prevailing network conditions. This dynamic pacing algorithm was designed and implemented in Spyder using Python programming language. It employed probe signals to gather network intelligence such as the applicable round trip times of the network. Thereafter, this network intelligence was employed to tailor the paces to these network conditions. The results obtained showed that this algorithm introduced longer paces when more packets are transmitted and shorter paces when few packets are transmitted. In so doing, this new algorithm gives enough time for large packets to be delivered and smaller paces when few packets are sent. The analysis was done in terms of bandwidth utilization efficiency, round trip times and congestion window size adjustments. The congestion window – time graphs and throughput – time graphs showed that the developed dynamic pacing algorithm adjusted quickly to network congestions hence ensuring that the network is efficiently utilized by averting delays.

113 - 120

Authors: S. Thirumurugan, Jasmine Beulah Gnanadurai

The wired mode of communication will no longer serve the purpose of our present scenario network where the expectations in communications system have been skyrocketed. Thus, wireless network with ad hoc nature will suit the present and forthcoming real world applications. This ad hoc wireless network will bind the entire globe and will reign the communication system tomorrow. But, ad hoc network has been limited by the growth factor. In order to address this issue, the clustering mechanism has been incorporated into this ad hoc network to get better results in terms of the network functionality. The ad hoc network efficiency has been improved when the clusters have been devised properly. There are various parameters which identify the efficacy level of clusters in networks. Existing cluster formation algorithms give more weight to any one of the parameters (degree, distance, energy, mobility) when selecting the cluster head of the cluster. This selection of parameters should be corresponding to the application to which the clusters are devised and cluster heads are selected. There is a desperate need for identifying the appropriate cluster head based on the importance given to parameters at various scenarios. Thus, this paper proposes NNPAC (Neural Network based Partitioning Around Cluster head), a generic method of cluster head election to fit for different applications. Implementation of this work has been carried out with the help of MATLAB simulator.