Mosaab Daoud

Monte Carlo simulation-based algorithms for estimating the reliability of mobile agent-based systems

In this paper, we propose two algorithms for estimating the task route reliability of mobile agent-based systems (MABS), which are based on the conditions of the underlying computer network. In addition, we propose a third algorithm for generating a random static planning strategy for mobile agents. The complexity of mobile agent network systems makes it hard to obtain the task route reliability of the MABS theoretically; instead we estimate it using Monte Carlo simulation. In this paper, we assume that the MABS consist of a number of independent mobile agents operating simultaneously. The results we have achieved demonstrate the robustness of the proposed algorithms.

Reliability estimation of mobile agent systems using the Monte Carlo approach

In this paper two algorithms are proposed for estimating the reliability of mobile agent systems, which are based on the conditions of the underlying computer network. A third algorithm is proposed for generating a random static planning strategy for mobile agents. The complexity of the mobile agent system network makes it hard to obtain the reliability of the system theoretically; instead we estimate it using the Monte Carlo simulation approach. In this paper, we assume that the system consists of a number of independent mobile agents operating simultaneously. The results we have achieved demonstrate the robustness of the proposed algorithms.

A new distance distribution paradigm to detect the variability of the influenza-A virus in high dimensional spaces

The influenza-A virus is a rapidly mutating virus. Detecting the variability of the influenza-A virus is a key point in understanding the changeability in the genetic information of this virus and can help in designing or rebuilding the annual vaccine. In this paper, we propose a novel distance distribution paradigm to detect influenza-virus variability in high dimensional spaces. We conduct an experiment and the results show the robustness of the proposed paradigm in detecting this variability.

Neural and Statistical Classification to Families of Bio-sequences

In this paper we present a novel technique to compute feature vectors for use with artificial neural networks and other pattern recognition techniques that is designed for classifying families of biological sequences. Such sequences present unique challenges due to the fact that they vary in length and often consist of many symbols relative to the number of exemplars available. The latter property presents a specific challenge with respect to avoiding over generalization. We explore a novel approach involving computing the entropy of pair-wise correlations between co-occurring symbols in the strings to generate feature vectors which are of fixed size, much smaller than the original string lengths, and still effective at discerning differences between classes of strings. We apply the technique and show its effectiveness on an RNA family classification problem.

A hybrid approach to reliability estimation of mobile agent-based systems

In this paper we analytically analyze the reliability of mobile agent-based systems. We define the reliability of a mobile agent in terms of the processor execution time required to perform agents tasks at each processing node and the transmission (communication) time needed to transfer the agent from one processing node to another via a network to complete the mission. The failure of entire components causes failure of the agent. Simulation experiments have been conducted using stochastic Petri Nets modeling. We investigate the effect of different component reliability and availability models (processors and links) on the system throughput. The results we have obtained demonstrate the impact of different reliability and availability models on the system throughput

Reliability analysis of mobile agent-based systems

In this paper we propose an algorithm for estimating the reliability of mobile agent systems, based on the conditions of the underlying computer network. We use the concept of restricted random walks in graph theory for generating a random static route strategy for mobile agents. The complexity of the mobile agent networks makes it hard to obtain the reliability of the system theoretically; instead we estimate it using the Monte Carlo approach. We assume that the system consists of a number of independent agents operating simultaneously. The results we have achieved demonstrate the robustness of the proposed algorithm.

AN ANALYTICAL APPROACH TO RELIABILITY ESTIMATION OF MOBILE AGENT-BASED SYSTEMS

Reliability estimation of mobile agent-based systems remains a difficult task due to the characteristics of mobile agents that include distributed and asynchronous execution, autonomy, and mobility. In this paper we present an analytical approach to estimate the reliability of mobile agent-based systems. We define the reliability of a mobile agent in terms of the processor execution time required to perform agents tasks at each processing node and the transmission or communication time needed to transfer the agent from one processing node to another via a network to complete its mission. The failure of entire components causes failure of the agent. Simulation experiments have been conducted using stochastic Petri Nets modeling. We investigate the effect of different component reliability and availability models (processors and links) on the system throughput. The results we have obtained demonstrate the impact of different reliability and availability models on the system throughput.

Estimating the task route reliability of mobile agent-based systems using Monte Carlo simulation

In this paper we propose two algorithms for estimating the task route reliability of mobile agent-based systems (MABS), which are based on the conditions of the underlying computer network. In addition, we propose a third algorithm for generating a random static planning strategy for mobile agents. The complexity of mobile agent network systems makes it hard to obtain the task route reliability of the MABS theoretically; instead we estimate it using Monte Carlo simulation. In this paper, we assume that the MABS consists of a number of independent mobile agents operating simultaneously. The results we have achieved demonstrate the robustness of the proposed algorithms.

Maximizing the route reliability of mobile agents in unreliable environments

Autonomous mobility of an agent is the most important feature of mobile agent-based systems. In this paper, we study the reliability of mobility (movement) of an agent in a faulty network environment with invariant (constant) latencies between its nodes. To achieve a reliable agent, we used the integer linear programming model to maximize the route reliability to complete a given task. The suggested criteria could be used as part of the planning module in the planning system of mobile agents. Moreover, we propose a bi-objective planning strategy when the network latencies are highly varied. This leads to a new concept which we call it a mixed planning strategy; it is locally static within each subnetwork but globally dynamic over all connected subnetworks.

A fuzzy approach to reliability estimation of mobile agent-based systems

In this paper we explore the reliability of mobile agent-based systems under the assumption of uncertainty regarding the systems environment (network). The lack of information about the failure rates of processors and communications links make it hard to estimate the required reliability using the conventional approach. To solve this problem, a fuzzy approach is suggested and experiments are conducted through Monte Carlo simulation. The results show the robustness of the proposed approach.