Ashraf B. El-Sisi

Cloud task scheduling based on ant colony optimization

Cloud computing is the development of distributed computing, parallel computing and grid computing, or defined as the commercial implementation of these computer science concepts. One of the fundamental issues in this environment is related to task scheduling. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it. A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. In this paper a cloud task scheduling policy based on ant colony optimization algorithm compared with different scheduling algorithms FCFS and round-robin, has been presented. The main goal of these algorithms is minimizing the makespan of a given tasks set. Ant colony optimization is random optimization search approach that will be used for allocating the incoming jobs to the virtual machines. Algorithms have been simulated using Cloudsim toolkit package. Experimental results showed that the ant colony optimization outperformed FCFS and round-robin algorithms.

Virtual Machine Placement Based on Ant Colony Optimization for Minimizing Resource Wastage

Cloud computing is concept of computing technology in which user uses remote server for maintain their data and application. Resources in cloud computing are demand driven utilized in forms of virtual machines to facilitate the execution of complicated tasks. Virtual machine placement is the process of mapping virtual machines to physical machines. This is an active research topic and different strategies have been adopted in literature for this problem. In this paper, the problem of virtual machine placement is formulated as a multi-objective optimization problem aiming to simultaneously optimize total processing resource wastage and total memory resource wastage. After that ant colony optimization algorithm is proposed for solving the formulated problem. The main goal of the proposed algorithm is to search the solution space more efficiently and obtain a set of non-dominated solutions called the Pareto set. The proposed algorithm has been compared with the well-known algorithms for virtual machine placement problem existing in the literature. The comparison results elucidate that the proposed algorithm is more efficient and significantly outperforms the compared methods on the basis of CPU resource wastage and memory resource wastage.

Argumentation based negotiation in multiagent system

Negotiation is a process of reaching an agreement on the terms of a transaction such as price, quantity, for two or more parties in multi-agent systems such as E-Commerce. It tries to maximize the benefits for all parties. Argumentation-based negotiation has been proposed as an alternative to proposalbased approaches such as game theory and heuristic. The main advantage is that it allows agents to exchange additional information rather than just simple proposals. This property of argumentation protocols can lead to beneficial agreements when used for complex multi agent negotiation. This paper presents an empirical comparison of argumentation-based negotiation to proposal-based negotiation in a strategic two-player scenario. It is implemented through a well established FIPA-complaint Agent Toolkit, JADE. Our experiments show that the argumentation-based approach outperforms the proposal-based approach with respect to the quality of the agreements found and the quantity of unsuccessful negotiations.

Enhanced semantic arabic Question Answering system based on Khoja stemmer and AWN

Due to the great amount of information available on the web, Question/Answering systems have become a focus for researchers and users as well. This paper introduces a proposed design for an Arabic Question Answering system based on Query Expansion ontology and an Arabic Stemmer. A set of factoid CLEF and TREC questions used to evaluate the system. Improved results obtained using AWN as a semantic Query Expansion and Khoja stemmer as a stemming system. Two experiments conducted using AWN the first using one level of expansion and the second using two level of expansion. Three measures are performed: Accuracy, Mean Reciprocal Rank, and Answered Questions, and the obtained results are 35.5%, 20.2%, and 65.33% respectively when using one level of expansion. But when using two level of expansion we get 38.77%, 16.2%, and 65.55% respectively.

A Multilateral Agent-Based Service Level Agreement Negotiation Framework

Negotiation gets more interesting With the increasing demand for discovering web services. Negotiation requires that the non-functional consumer requirements have to meet with providers. Conducting a negotiation between participants is the key issue for reaching an agreement between them. Service Level Agreements (SLA) plays an important role in service-based systems. Different researchers conduct different bilateral negotiation frameworks. Multilateral negotiation helps the consumer to get the best suitable provider not to get an agreement between agreed participants. This paper presents a multilateral SLA negotiation framework for non-functional requirements using three different functions with time-based strategy. That is for getting the best suitable provider for a consumer. Through the proposed framework, a model is defined to map attributes of participants to a main parameter used in decision-making model. A prototype of the proposed framework is implemented with conducting a multilateral negotiation scenario. The proposed framework reached the best-suitable provider, not an acceptable one.

Clustering of chemical data sets for drug discovery

Chemoinformatics clustering algorithms are important issues for drug discovery process. So, there are many clustering algorithms that are available for analyzing large chemical data sets of medium and high dimensionality. The quality of these algorithms depends on the nature of data sets and the accuracy needed by the application. The applications of clustering algorithms in the drug discovery process are compound selection, virtual library generation, High-Throughput Screening (HTS), Quantitative Structure-Activity Relationship (QSAR) analysis and Absorption, Distribution, Metabolism, Elimination and Toxicity (ADMET) prediction. Based on Structure-Activity Relationship (SAR) model, compounds with similar structure have similar biological activities. So, clustering algorithms must group more similar compounds in one cluster. K-Means, bisecting K-Means and Ward clustering algorithms are the most popular clustering algorithms that have a wide range of applications in chemoinformatics. In this paper, a comparative study between these algorithms is presented. These algorithms are applied over homogeneous and heterogeneous chemical data sets. The results are compared to determine which algorithms are more suitable depending on the nature of data sets, computation time and accuracy of produced clusters. Accuracy is evaluated using standard deviation metric. Experimental results show that K-Means algorithm is preferable for small number of clusters for homogeneous and heterogeneous data sets in terms of time and standard deviation. Bisecting K-Means and Ward algorithms are preferable for large number of clusters for homogeneous and heterogeneous data sets in term of standard deviation, but bisecting K-Means algorithm is preferable in term of time.

Design and implementation of course timetabling system based on genetic algorithm

University course timetabling is one of the most important and time-consuming problem which takes place frequently in all the educational institutes. This paper proposes design and implementation system to generate timetable based on genetic algorithm using different combinations selection algorithm and mutation types. Two cases small problem and big problem are studied. The results show that two cases tournament selection is giving solutions better than roulette wheel Selection. The worst pair is roulette wheel selection and mutation. Mutation error method helps to reach to the best solution faster. In case of conflicts and no solution, our system generates a report, containing conflict constraints that must be remove or modified.

Syntactic open domain Arabic question/answering system for factoid questions

Information Retrieval systems have become a crucial part of any search engine, taking into consideration the increased number of pages and documents added to the World Wide Web every day. Question/Answering systems are specific to retrieve the most accurate answer among several documents; this paper will present an improved methodology and implementation of an open domain Arabic Question Answering System for factoid questions. The proposed system is based on Query Expansion ontology and an Arabic Stemmer. Basically the system mainly depends on AWN as a semantic Query Expansion and Khoja stemmer as a stemming system, to evaluate the system we used 56 translated CLEF and TREC questions, four types of test scenarios are performed. Four evaluation attributes were considered: Accuracy, Mean Reciprocal Rank, Answered Questions and recall; and encouraging results were reached with the proposed system.

Image compression ratio enhancement based on conformal mapping

The main goal of compression is to reduce the amount of data required to represent a digital image. Conformal mapping has long been an important topic in complex analysis. In this paper, the proposed image compression technique based on conformal mapping transformation is introduced. The most recent standard compression technique, JPEG2000 compression algorithm, is used. The proposed technique is tested with various images types. Two categories of image compression techniques (lossless and lossy) and with/without conformal mapping are studied. The experimental results show that the compression ratio improves by 14% in average, and in case lossy image compression using JPEG2000 image quality gains over 2 dB in average.

A novel artificial immune clonal selection classification and rule mining with swarm learning model

Metaheuristic optimisation algorithms have become popular choice for solving complex problems. By integrating Artificial Immune clonal selection algorithm (CSA) and particle swarm optimisation (PSO) algorithm, a novel hybrid Clonal Selection Classification and Rule Mining with Swarm Learning Algorithm (CS2) is proposed. The main goal of the approach is to exploit and explore the parallel computation merit of Clonal Selection and the speed and self-organisation merits of Particle Swarm by sharing information between clonal selection population and particle swarm. Hence, we employed the advantages of PSO to improve the mutation mechanism of the artificial immune CSA and to mine classification rules within datasets. Consequently, our proposed algorithm required less training time and memory cells in comparison to other AIS algorithms. In this paper, classification rule mining has been modelled as a miltiobjective optimisation problem with predictive accuracy. The multiobjective approach is intended to allow the PSO algorithm to return an approximation to the accuracy and comprehensibility border, containing solutions that are spread across the border. We compared our proposed algorithm classification accuracy CS2 with five commonly used CSAs, namely: AIRS1, AIRS2, AIRS-Parallel, CLONALG, and CSCA using eight benchmark datasets. We also compared our proposed algorithm classification accuracy CS2 with other five methods, namely: Naïve Bayes, SVM, MLP, CART, and RFB. The results show that the proposed algorithm is comparable to the 10 studied algorithms. As a result, the hybridisation, built of CSA and PSO, can develop respective merit, compensate opponent defect, and make search-optimal effect and speed better.