Multi-Agent System Approach for Trustworthy Cloud Service Discovery

Accessing the advantages of cloud computing requires that a prospective user has proper access to trustworthy cloud services. It is a strenuous and laborious task to find resources and services in a heterogeneous network such as cloud environment. The cloud computing paradigm being a form of distributed system with a complex collection of computing resources from different domains with different regulatory policies but having a lot of values could enhance the mode of computing. However, a monolithic approach to cloud service discovery cannot help the necessities of cloud environment efficiently. This study put forward a distributive approach for finding sincere cloud services with the use of Multi-Agents System for ensuring intelligent cloud service discovery from trusted providers. Experiments were carried out in the study using CloudAnalyst and the results indicated that extending the frontiers MAS approach into cloud service discovery by way of integrating trust into the process improves the quality of service in respect of response time and scalability. A further comparative analysis of the Multi-Agents System approach for cloud service discovery to monolithic approach showed that Multi-Agents System approach is highly efficient, and highly flexible for trustworthy cloud service discovery

A Prey-Predator Defence Mechanism For Ad Hoc On-Demand Distance Vector Routing Protocol

This study proposes a nature-based system survivability model. The model was simulated, and its performance was evaluated for the mobile ad hoc wireless networks. The survivability model was used to enable mobile wireless distributed systems to keep on delivering packets during their stated missions in a timely manner in the presence of attacks. A prey-predator communal defence algorithm was developed and fused with the Ad hoc On-demand Distance Vector (AODV) protocol. The mathematical equations for the proposed model were formulated using the Lotka-Volterra theory of ecology. The model deployed a security mechanism for intrusion detection in three vulnerable sections of the AODV protocol. The model simulation was performed using MATLAB for the mathematical model evaluation and using OMNET++ for protocol performance testing. The MATLAB simulation results, which used empirical and field data, have established that the adapted Lotka-Volterra-based equations adequately represent network defense using the communal algorithm. Using the number of active nodes as a measure of throughput after attack (with a maximum throughput of 250 units), the proposed model had a throughput of 230 units while under attack and the intrusion was nullified within 2 seconds. The OMNET++ results for protocol simulation that use throughput, delivery ratio, network delay, and load as performance metrics with the OMNET++ embedded datasets showed good performance of the model, which was better than the existing conventional survivability systems. The comparison of the proposed model with the existing model is also presented. The study concludes that the proposed communal defence model was effective in protecting the entire routing layer (layer 2) of the AODV protocol when exposed to diverse forms of intrusion attacks

A low cost course information syndication system

This study presents a cost-effective, reliable, and convenient mobile web-based system to facilitate the dissemination of course information to students, to support interaction that goes beyond the classroom. The system employed the Really Simple Syndication (RSS) technology and was developed using Rapid Application Development (RAD) methodology. The design of the system was modelled using Unified Modeling Language (UML) diagrams, while its implementation was done using Java Micro Edition (JME) and “PHP: Hypertext Preprocessor” (PHP).A simulation technique was used to evaluate the proposed system performance by comparing the approach used in its design to one adopted in a similar study, using response time and bandwidthconsumption as metrics. The results obtained revealed that the performance of the proposed syndication system was better. Similarly, an experiment to investigate the students’ perception of the system was conducted, with students’ responses revealing a tremendous success of this project

Lightweight Agents, Intelligent Mobile Agent and RPC Schemes: A Comparative Analysis

This paper presents the performance comparison of Lightweight Agents, Single Mobile Intelligent Agents and Remote Procedure Call which are tools for implementing communication in a distributed computing environment. Routing algorithms for each scheme is modeled based on TSP. The performance comparison among the three schemes is based on bandwidth overhead with retransmission, system throughput and system latency. The mathematical model for each performance metric is presented, from which mathematical model is derived for each scheme for comparison. The simulation results show that the LWAs has better performance than the other two schemes in terms of small bandwidth retransmission overhead, high system throughput and low system latency. The Bernoulli random variable is used to model the failure rate of the simulated network which is assumed to have probability of success p = 85% and the probability of failure q = 15%. The network availability is realized by multiplicative pseudorandom number generator during the simulation. The results of simulation are presented

An Octopus-Inspired Intrusion Deterrence Model in Distributed Computing System

The study formulated and evaluated a model for effective management of maliciousnodes in mobile Ad-hoc network based on Ad-Hoc on- demand distancevector routing protocol. A collaborative injection model called CollaborativeInjection Deterrence Model (CIDM) was formulated using stochastic theory.The definition of the model was presented using graph theory. CIDM wassimulated using three different scenarios. The three scenarios were then comparedusing packets delivery ratio (PDR), routing load, throughput and delayas performance metrics. The simulation result showed that CIDM reduce considerablythe rate of packets dropped caused by malicious nodes in MANETnetwork. CIDM did not introduce additional load to the network and yet withproduce higher throughput. Lastly, the access delay with CIDM is minimalcompared with convectional OADV. The study developed a model to mete outa punitive measure to rogue nodes as a form of intrusion deterrence withoutdegrading the overall performance of the network. The well known CRAWDADdataset was used in the simulation

Development and testing of a graphical FORTRAN learning tool for novice programmers

To address the difficulties associated with computer programming, this article first looks at some reasons why students, especially engineering students, find programming such a daunting prospect, and it proposes a programming learning tool managed by a Deterministic Finite Automaton (DFA). The DFA machine used a graphical environment provided by Simulink to teach the FOR-mula TRANslator (FORTRAN) programming language to science students. The proposed programming learning tool and the traditional method of teaching were compared and evaluated. The results of evaluation indicated that there was an improvement in learning effectiveness of the proposed learning tool

Network resources management in a multi-agent system: A simulative approach

Multi-agent systems (i.e. systems comprising many agents) have been proposed for many Internet and distributed applications. The proposed systems have little or no consideration of the effects of this multi-agent approach on network resources. In this paper, we presented a simulation assessment of the effect of multi-agent systems on network resources. The routing scheme of the agents was formulated based on the travelling salesman problem. Lightweight agent (LWA) controller was modelled using a fuzzy logic toolbox in the MATLAB environment. The performance metrics of bandwidth usage, response time and throughput were used to compare the network resources usage by different groups of LWAs (10 LWAs, 40 LWAs, 100 LWAs, 150 LWAs) during their computational task on the network. Java programs were written for the implementation of lightweight agents in the simulation. The inputs to the system were realised by multiplicative pseudorandom number generation during the simulation. The simulation result analysis was carried out based on the performance metrics stated above for the four groups of agents. Increasing the number of LWAs in a simulated multi-agent system decreased the response time but increased the throughput and the bandwidth usage. All these performance measures should be considered for developing countries with bandwidth shortages, because having too many agents in a multi-agent system could result in bandwidth wastages

A fuzzy logic based multi-agents controller

This paper presents a fuzzy logic based controller (Multi-Agents System Controller (MASC)) which regulates the number of agents released to the network on a Multi-Agents Systems (MASs). A fuzzy logic (FL) model for the controller is as presented. The controller is a two-inputs-one-output system. The controllability is based on the network size (NTZ) and the available bandwidth (ABD) which are the inputs to the controller, the controller's output is number of agents (ANG). The model was simulated using SIMULINK software. The simulation result is presented and it shows that ABD is the major constraint for the number of agents released to the network. © 2010 Elsevier Ltd. All rights reserved

An ensemble deep learning approach for predicting cocoa yield

One important aspect of agriculture is crop yield prediction. This aspect allows decision-makers and farmers to make adequate planning and policies. Before now, various statistical models have been used for crop yield prediction but this approach experienced some hiccups such as time wastage, inaccurate prediction, and difficulties in model usage. Recently, a new trend of deep learning and machine learning are now adopted for crop yield prediction. Deep learning can extract patterns from a large volume of the dataset, thus, they are suitable for prediction. The research work aims to propose an efficient deep-learning technique in the field of cocoa yield prediction. This research presents a deep learning approach for cocoa yield prediction using a Convolutional Neural Network and Recurrent Neural Network (CNN-RNN) with Long Short Term Memory (LSTM). The ensemble approach was adopted because of the nature of the dataset used. Two different sets of the dataset were used, namely; the climatic dataset and the cocoa yield dataset. CNN-RNN with LSTM has some salient features, where CNN was used to handle the climatic dataset, and RNN was employed to handle the cocoa yield prediction in southwest Nigeria. Two major problems generated by the CNN-RNN model are vanishing and exploding gradients and this was handled by LSTM. The proposed model was benchmarked with other machine learning algorithms based on Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). CNN-RNN with LSTM gave the least mean of absolute error as compared to the other machine learning algorithms which shows the efficiency of the model

A Mobile Based Pharmacy Store Location-aware System

This paper presents a formulated mobile-based location-awareness model that was implemented into a Location-awareness System (L-aS hereafter) for finding the pharmacy location where prescribed drugs and their prices are available for sale. The scenario that inspired the model formulation was formalized using the unified modeling language. The model was implemented within the android studio integrated development environment with the L-aS database created through SQL lite database. The system was tested using user experience based testing technique. Based on core system performance testing, the system demonstrated a normal response time, resource utilization (i.e. storage and memory usage), and data use potentials of 414.6ms, 4.964mb and 1.9116 kb/secs, and 3.0296mb respectively. Therefore, the system performed well under ordinary conditions as an android application running on small memory devices. The study concluded that the developed mobile based pharmacy store location-aware system was useful to provide information to purchase prescribed drugs especially in perplexed situation(s)