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

An ensemble CRT, RVFLN, SVM method for estimating Propane Spot Price

In this paper, we propose an ensemble of the CRT-RVFLN-SVM (Classification and Regression Tree (CRT), Random Variable Functional Link Neural Network (RVFLN), and Support Vector Machine (SVM)) to improve robustness and effectiveness in estimating propane spot price. The propane spot price data which are collected from the Energy Information Administration of the US Department of Energy and Barchart were used to build an ensemble CRT-RVFLN-SVM model for the estimating of propane spot price. For the purpose of evaluation, the constituted intelligent computing technologies of the proposed ensemble methodology in addition to Multilayer Back-Propagation Neural Network (MBPNN) were also applied to estimate the propane spot price. Experimental results show that the proposed ensemble CRT-RVFLN-SVM model has improved the performance of CRT, RVFLN, SVM, and MBPNN. The can help to reduce the level of future uncertainty of the propane spot price. Propane investors can use our model as an alternative investment tool for generating more revenue because accurate estimations of future propane price implies generating more profit

Intelligent Decision Support Systems for Oil Price Forecasting

This research studies the application of hybrid algorithms for predicting the prices of crude oil. Brent crude oil price data and hybrid intelligent algorithm (time delay neural network, probabilistic neural network, and fuzzy logic) were used to build intelligent decision support systems for predicting crude oil prices. The proposed model was able to predict future crude oil prices from August 2013 to July 2014. Future prices can guide decision makers in economic planning and taking effective measures to tackle the negative impact of crude oil price volatility. Energy demand and supply projection can effectively be tackled with accurate forecasts of crude oil prices, which in turn can create stability in the oil market. The future crude oil prices predict by the intelligent decision support systems can be used by both government and international organizations related to crude oil such as organization of petroleum exporting countries (OPEC) for policy formulation in the next one year.  DOR: 98.1000/1726-8125.2015.0.47.0.0.73.10

Energy-efficient Virtual Machine Allocation Technique Using Flower Pollination Algorithm in Cloud Datacenter: A Panacea to Green Computing

Cloud computing has attracted significant interest due to the increasing service demands from organizations offloading computationally intensive tasks to datacenters. Meanwhile, datacenter infrastructure comprises hardware resources that consume high amount of energy and give out carbon emissions at hazardous levels. In cloud datacenter, Virtual Machines (VMs) need to be allocated on various Physical Machines (PMs) in order to minimize resource wastage and increase energy efficiency. Resource allocation problem is NP-hard. Hence finding an exact solution is complicated especially for large-scale datacenters. In this context, this paper proposes an Energy-oriented Flower Pollination Algorithm (E-FPA) for VM allocation in cloud datacenter environments. A system framework for the scheme was developed to enable energy-oriented allocation of various VMs on a PM. The allocation uses a strategy called Dynamic Switching Probability (DSP). The framework finds a near optimal solution quickly and balances the exploration of the global search and exploitation of the local search. It considers a processor, storage, and memory constraints of a PM while prioritizing energy-oriented allocation for a set of VMs. Simulations performed on MultiRecCloudSim utilizing planet workload show that the E-FPA outperforms the Genetic Algorithm for Power-Aware (GAPA) by 21.8%, Order of Exchange Migration (OEM) ant colony system by 21.5%, and First Fit Decreasing (FFD) by 24.9%. Therefore, E-FPA significantly improves datacenter performance and thus, enhances environmental sustainability

Energy-efficient Nature-Inspired techniques in Cloud computing datacenters

Cloud computing is a systematic delivery of computing resources as services to the consumers via the Internet. Infrastructure as a Service (IaaS) is the capability provided to the consumer by enabling smarter access to the processing, storage, networks, and other fundamental computing resources, where the consumer can deploy and run arbitrary software including operating systems and applications. The resources are sometimes available in the form of Virtual Machines (VMs). Cloud services are provided to the consumers based on the demand, and are billed accordingly. Usually, the VMs run on various datacenters, which comprise of several computing resources consuming lots of energy resulting in hazardous level of carbon emissions into the atmosphere. Several researchers have proposed various energy-efficient methods for reducing the energy consumption in datacenters. One such solutions are the Nature-Inspired algorithms. Towards this end, this paper presents a comprehensive review of the state-of-the-art Nature-Inspired algorithms suggested for solving the energy issues in the Cloud datacenters. A taxonomy is followed focusing on three key dimension in the literature including virtualization, consolidation, and energy-awareness. A qualitative review of each techniques is carried out considering key goal, method, advantages, and limitations. The Nature-Inspired algorithms are compared based on their features to indicate their utilization of resources and their level of energy-efficiency. Finally, potential research directions are identified in energy optimization in data centers. This review enable the researchers and professionals in Cloud computing datacenters in understanding literature evolution towards to exploring better energy-efficient methods for Cloud computing datacenters

Advances in teaching and learning on Facebook in higher institutions

Facebook has now become the most popular and extensively used social networking site among students of institutions of higher education. This makes it a widespread tool for communication and exchange of ideas. Notable to that is an active research in determining the utility of Facebook as a complementary tool in teaching and learning. The uses of the social networking sites especially, Facebook has been reported in a wide variety of results with respect to factors, such as students\u27 learning performance, involvement, and acceptance, have been reported in the literature. This paper presents a comprehensive review of recent studies that employ Facebook as a tool for teaching and learning in institutions of higher education. We analyze the use of Facebook as a teaching and learning tool for various courses. Thereafter, its impacts on enhancing student learning outcomes as well as its negative impact on students\u27 performance are evaluated. We also highlight the main limitations of the existing and previous studies. Future research directions for incorporating Facebook into the teaching and learning at institutions of higher education are suggested. This review is helpful to educators who plan to integrate Facebook into their teaching as well as to the researchers for further exploration of Facebook as a tool in teaching and learning

AN alternative design of Collaborative Virtual Environment architecture based on cloud computing

Collaborative Virtual Environment (CVE) systems allow the sharing of virtual space, and each participant is represented by an entity in the CVE. Resources are consumed when interaction among several users occurs. The resources are consumed as a result of updating its own state, and the communication resources required to distribute the update of counterpart users in the CVE. The Peer-to-Peer (p2p) and Client Server (CS) architecture in CVE has the limitation of scaling to large number of users while maintaining consistency in the virtual world. This paper identifies the basic requirements of CVE systems, proposed a model based on cloud computing to improve the performance of the traditional CVE systems to evade the limitations of the p2p and CS. The design of the cloud computing-based CVE architecture proposes in our research consist of three layers namely, infrastructure, platform, and application. The potential benefits of the CVE architecture presented in this paper indicated a performance improvement over the traditional CVE. The proposed architecture with little modification can easily be implemented in real life applications to improve the performance of collaborative virtual application

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

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

TCP Skudai: A high performance TCP variant for Collaborative Virtual Environment systems

In Collaborative Virtual Environment (CVE) systems, one of the main research topics in is how to efficiently transmit message to minimized delay, provide scalability and reliability, and also the choice of transport protocol. TCP is the most widely used protocol in the design of CVE systems, and its throughput deteriorates in the network with large delay due to it conservative congestion control. TCP removes network congestion by adjusting sending rate according to available bandwidth. Researchers have shown that TCP underutilizes bandwidth in a high-speed network and in a long distance network. With the increasing number of concurrent distant collaborators in CVE systems, TCP may not provide the level of consistency and scalability required in the systems. Many researchers have proposed different modifications of TCP, but rarely addressed the issues in CVE systems. In this paper, we proposed a novel TCP variant conceptual model called TCP Skudai, aimed at achieving a high performance congestion control algorithm for CVE systems. The methods used in the conceptualizations lies within adding constraints to the phases of transmission: slow start, congestion avoidance, fast retransmit, and fast recovery based on principle of packet conservation