Throughput analysis of TCP congestion control algorithms in a cloud based collaborative virtual environment

Collaborative Virtual Environment (CVE) has become popular in the last few years, this is because CVE is designed to allow geographically distributed users to work together over the network. In CVE the state of the virtual objects is witnessing unprecedentant change. When a user performs an action in CVE, the information of the action needs to be transmitted to other users to maintain consistency in the cooperative work. TCP is the most widely used protocol in the design of CVE, and its throughput deteriorates in the network with large delay. Gital et al, 2014 proposes a cloud based architectural model for improving scalability and consistency in CVE. Therefore, this paper aim at evaluating and comparing the performance of different TCP variant (Tahoe, Reno, New Reno, Vegas, SACK, Fack and Linux) with the cloud based CVE architecture to determine the suitability of each TCP variant for CVE. A comparative analysis between the different TCP variants is presented in terms of throughput verses elapse time, with increasing number of users in the system. TCP with the cloud based model was found to be effective, promising and robust for achieving consistency requirement in CVE system

Performance evaluation of TCP congestion control algorithms throughput for CVE based on cloud computing model

Collaborative Virtual Environment (CVE) is becoming popular in the last few years; this is because CVE is designed to allow geographically distributed users to work together over the network. Currently, in the development of CVE Systems, Client server architectures with multiple servers are used with TCP as update transmitting transport protocol because of its reliability. With the increasing number of collaborators, the transport protocol is inadequate to meet the system requirements in terms of timely data transmission. The transport protocol (TCP) throughput deteriorates in the network with large delay which leads to unsatisfactory consistency requirement of the CVE systems.We proposed a cloud based architectural model for improving scalability and consistency in CVE in an earlier study. The current paper aims at evaluating and comparing the performance of different TCP variants (Tahoe, Reno, New Reno, Vegas, SACK, Fack and Linux) with the cloud based CVE architecture to determine the suitability of each TCP variant for CVE. A comparative analysis between the different TCP variants is presented in terms of throughput verses elapse time, with increasing number of users in the system. TCP Vegas with the cloud based model was found to be effective for CVE systems based on Cloud Computin

Investigating the dynamics of watermark features in audio streams

The dynamics of watermark features after embedded into audio streams through digital watermarking techniques are unstable. The audio streams exits as a series of waveform amplitude of sound over the range of information it contains. Within this range, there are variations of the presentation of the stream taken per second and given in hertz. The precision of the stream representations is measured by the number of bits per stream. The fact that the streams bits are high is a sign for data already existing which means that within empty streams additional information can be embedded. In general, added information is described as noise and these audio streams are considered as noise tolerant. Watermarks are embedded into a spatial or transformed domain with the effect that the presentation of some bit streams will be affected. This paper investigates the dynamics of watermarks embedded in an audio stream, the contained file being noise intolerant. The watermark file is embedded in several positions within the audio signal stream by spread spectrum techniques. The most suitable positions for embedding the watermark is identified which ensures a strong and robust watermark as a result

Intelligent system for predicting the price of natural gas based on non-oil commodities

We present a preliminary investigation into a novel approach to natural gas prediction. Experimental data were extracted from the Energy Information Administration of the US Department of Energy. The datasets were pre-processed and used to build a feed-forward neural network intelligent system for predicting natural gas prices based on gold, silver, soy and copper. The validation of the intelligent system indicated a Regression (R) = 0.79972 when the reserved datasets were tested on the intelligent system. Natural gas prices can be predicted using non-oil commodities as independent variables. With little additional information, the proposed design can be used to construct intelligent decision support systems to support decision making in the government and private sector

Text normalization algorithm for facebook chats in Hausa language

The rapid increase in using non-standard words (NSWs) in communication through the social media is causing difficulties in understanding contents of the text messages. In addition, it affects the performance of several natural language processing (NLP) task such as machine translation, information retrievals, summarization and etc. In this study, we present an automatic text normalization system on Facebook chatting based on Hausa language. The proposed algorithm manually developed dictionary that employ normalization of each non-standard word with its equivalent standard word. This is accomplished through modification of the technique employed by [1] to fit Hausa NSWs' formation. It was found that our proposed algorithm was able to normalized Hausa NSWs with an accuracy of 100%. The results of this research can facilitate comprehensive communication via Facebook using Hausa language

Cloud computing platforms for delivering computer science and mathematics instructional course content to learners

The cloud computing can be considered as a major driver in innovations and transformations in teaching and learning. The research on cloud computing in education is on the increase and is attracting the attention of researchers. In this paper, we designed and developed an integrated cloud computing learning platform based on Unified Modelling Language for delivering course contents of computer science and mathematics courses to learners. The propose cloud learning framework has the potentials for improving ubiquitous and learner centred learning activities. Educators can find the propose cloud learning platform useful for improving learning outcomes and motivatio

A Support Vector Machine Classification of Computational Capabilities of 3D Map on Mobile Device for Navigation Aid

3D map for mobile devices provide more realistic view of an environment and serves as better navigation aid. Previous research studies shows differences in 3D maps effect on acquiring of spatial knowledge. This is attributed to the differences in mobile device computational capabilities. Crucial to this, is the time it takes for 3D map dataset to be rendered for a required complete navigation task. Different findings suggest different approach on solving the problem of time require for both in-core (inside mobile) and out-core (remote) rendering of 3D dataset. Unfortunately, studies on analytical techniques required to shows the impact of computational resources required for the use of 3D map on mobile device were neglected by the research communities. This paper uses Support Vector Machine (SVM) to analytically classify mobile device computational capabilities required for 3D map that will be suitable for use as navigation aid. Fifty different Smart phones were categorized on the bases of their Graphical Processing Unit (GPU), display resolution, memory and size. The result of the proposed classification shows high accuracy</jats:p