Fathy E. Eassa

AIDA: a dynamic analyzer for Ada programs

This article presents a dynamic analyzer for Ada programs called AIDA. In software engineering, previous dynamic analyzers have often incorporated first-order logic assertion languages. For dynamic testing of both sequential and concurrent programs, however, temporal logic may be advantageous because it deals with the development of situations over time. AIDA investigates the applicability of temporal logic in building a dynamic analyzer for Ada programs. AIDA is designed to test, debug, and specify programs written in the Ada language. It affects the instrumentation of programs as well as collecting, organizing, and reporting of results of the instrumented program. The instrumentation approach is based on the idea that the intended function of a program can often be specified in terms of assertions or values that must be assumed by variables at certain strategic points in the program. This article describes the design, implementation, and experimental evaluation of AIDA. The goal of this work is to apply AIDA as a comprehensive dynamic analyzer for Ada programs. AIDA can handle sequential processes and concurrent tasks, and it can understand fully all Ada statements.

Multiple phases-based classifications for cloud services

The current problem in cloud services discovery is the lack of standardisation in the naming convention and the heterogeneous type of its features. Therefore, to accurately retrieve the appropriate services, an intelligent service discovery is required. To do that, the cloud services attributes should be extracted from the heterogeneous formats and represented it in a uniform manner such as ontology to increase the accuracy of discovery. The extraction process can be done by classifying the cloud services into different types. In this paper, single and multiple phases-based classifications are performed using support vector machine (SVM) and naive Bayes as classifiers. The Cloud Armors dataset used which represents four classes of cloud services. Topic modelling using MALLET tool is used for dataset pre-processing. The experimental results showed that the classification accuracy for the two phases-based and single phase-based classifications reached 87.90% and 92.78% respectively.

Agent-based Managing for Grid Cloud System — Design and Prototypal Implementation

Here, we present the design and architecture of an Agent-based Manager for Grid Cloud Systems (AMGCS) using software agents to ensure independency and scalability when the number of resources and jobs increase. AMGCS handles IaaS resources (Infrastructure-as-a-Service — compute, storage and physical resources), and schedules compute-intensive jobs for execution over available resources based on QoS criteria, with optimized task-execution and high resource-utilization, through the capabilities of grid clouds. This prototypal design and implementation has been tested and shown a proven ability to increase the reliability and performance of cloud application by distributing its tasks to more than one cloud system, hence increase the reliability of user jobs and complex tasks submitted from regular machines.

Big Data Knowledge Mining

Big Data (BD) era has been arrived. The ascent of big data applications where information accumulation has grown beyond the ability of the present programming instrument to catch, manage and process within tolerable short time. The volume is not only the characteristic that defines big data, but also velocity, variety, and value. Many resources contain BD that should be processed. The biomedical research literature is one among many other domains that hides a rich knowledge. MEDLINE is a huge biomedical research database which remain a significantly underutilized source of biological information. Discovering the useful knowledge from such huge corpus leading to many problems related to the type of information such as the related concepts of the domain of texts and the semantic relationship associated with them. In this paper, an agent-based system of two–level for Self-supervised relation extraction from MEDLINE using Unified Medical Language System (UMLS) Knowledgebase, has been proposed . The model uses a Self-supervised Approach for Relation Extraction (RE) by constructing enhanced training examples using information from UMLS with hybrid text features. The model incorporates Apache Spark and HBase BD technologies with multiple data mining and machine learning technique with the Multi Agent System (MAS). The system shows a better result in comparison with the current state of the art and naive approach in terms of Accuracy, Precision, Recall and F-score.

Mobile Agent Framework for Distributed Network Performance Management

traditional centralized network management approach presents severe efficiency and scalability limitations in large scale networks. The process of data collection and analysis typically involves huge transfers of management data to the manager which consumes considerable network bandwidth and causes bottlenecks at the manager side. Mobile agent technology provides an effective solution to alleviate this burden by distributing the management functionality over the network elements. A Mobile Agent has the ability to autonomously move among network elements to perform the required tasks locally. Thus, the code is transferred to the data location instead of moving the entire data to the managers site. The present study aims to investigate the effectiveness of using mobile agents to overcome the limitations of the centralized structure. Focusing on the network performance management functional area, a prototype is developed to assess the effectiveness of a distributed mobile-agent-based network management system. The developed prototype installs itself automatically on remote machines and periodically checks their software and hardware status. Experiments are done to measure the network traffic volume when managing a typical network. Practical measurements are compared for the traffic generated by both the developed prototype and the current centralized network management standard (SNMP). This comparison confirms that mobile- agent-based management employs much less traffic than the centralized system. An estimation of the required management delays is provided for both sequential- and parallel- dispatching of the mobile agents.

A Reliability and Performance Model For Static Data Flow Machines

The major advantages of parallel processing sys tems are their great reliability and high perfor mance. A class of massively parallel computing systems is the data flow machines. These machines work on the basis of data flow rather than control flow. This paper presents a reliability analysis of data flow machines using a graph theoretical ap proach. Three machines are considered here. They are the MIT, DDP and LAU static data flow ma chines. The data flow graph has been employed as a natural tool for representing that class of ma chines. The isomorphism between Petri nets and data flow graphs has been exploited to detect whether the consistency constraints are satisfied during various operational conditions. Such a graph is extended so that a timed data flow model has been constructed. This model integrates both the reliability features dependent on the system structure and the performance characteristics dependent on the components behavior. More over, a productivity index is introduced for evaluating the three machines.

A computational model for static data flow machines

Abstract Computer architects have been constantly looking for new approaches to design high-performance machines. Data flow and VLSI offer two mutually supportive approaches towards a promising design for future super-computers. When very high speed computations are needed, data flow machines may be relied upon as an adequate solution in which extremely parallel processing is achieved. This paper presents a formal analysis for data flow machines. Moreover, the following three machines are considered: (1) MIT static data flow machine; (2) TIs DDP static data flow machine; (3) LAU data flow machine. These machines are investigated by making use of a reference model. The contributions of this paper include: (1) Developing a Data Flow Random Access Machine model (DFRAM), for first time, to serve as a formal modeling tool. Also, by making use of this model one can calculate the time cost of various static data machines, as well as the performance of these machines. (2) Constructing a practical Data Flow Simulator (DFS) on the basis of the DFRAM model. Such DFS is modular and portable and can be implemented with less sophistication. The DFS is used not only to study the performance of the underlying data flow machines but also to verify the DFRAM model.