Exascale MPI-based program deadlock detection

Deadlock detection is one of the main issues of software testing in High Performance Computing (HPC) and also in exascale computing areas in the near future. Developing and testing programs for machines which have millions of cores is not an easy task. HPC program consists of thousands (or millions) of parallel processes which need to communicate with each other in the runtime. Message Passing Interface (MPI) is a standard library which provides this communication capability and it is frequently used in the HPC. Exascale programs are expected to be developed using MPI standard library. For parallel programs, deadlock is one of the expected problems. In this paper, we discussed the deadlock detection for exascale MPI-based programs where the scalability and efficiency are critical issues. The proposed method is implemented to detect and flag the processes and communication commands which are potential to cause deadlocks in a scalable and efficient manner. MPI benchmark programs were used to test the propose method

Exascale Message Passing Interface based Program Deadlock Detection

Deadlock detection is one of the main issues of software testing in High Performance Computing (HPC) and also inexascale computing areas in the near future. Developing and testing programs for machines which have millions of cores is not an easy task. HPC program consists of thousands (or millions) of parallel processes which need to communicate with each other in the runtime. Message Passing Interface (MPI) is a standard library which provides this communication capability and it is frequently used in the HPC. Exascale programs are expected to be developed using MPI standard library. For parallel programs, deadlock is one of the expected problems. In this paper, we discuss the deadlock detection for exascale MPI-based programs where the scalability and efficiency are critical issues. The proposed method detects and flags the processes and communication operations which are potential to cause deadlocks in a scalable and efficient manner. MPI benchmark programs were used to test the proposed method

An Ensemble Machine Learning Technique for Functional Requirement Classification

In Requirement Engineering, software requirements are classified into two main categories: Functional Requirement (FR) and Non-Functional Requirement (NFR). FR describes user and system goals. NFR includes all constraints on services and functions. Deeper classification of those two categories facilitates the software development process. There are many techniques for classifying FR; some of them are Machine Learning (ML) techniques, and others are traditional. To date, the classification accuracy has not been satisfactory. In this paper, we introduce a new ensemble ML technique for classifying FR statements to improve their accuracy and availability. This technique combines different ML models and uses enhanced accuracy as a weight in the weighted ensemble voting approach. The five combined models are Naïve Bayes, Support Vector Machine (SVM), Decision Tree, Logistic Regression, and Support Vector Classification (SVC). The technique was implemented, trained, and tested using a collected dataset. The accuracy of classifying FR was 99.45%, and the required time was 0.7 s

Retracted: Adaptive e- learning system based on personalized learning style

This article was withdrawn and retracted by the Journal of Fundamental and Applied Sciences and has been removed from AJOL at the request of the journal Editor in Chief and the organisers of the conference at which the articles were presented (www.iccmit.net). Please address any queries to [email protected]

BCNBI: A Blockchain-Based Security Framework for Northbound Interface in Software-Defined Networking

Software-defined networking (SDN) has emerged as a flexible and programmable network architecture that takes advantage of the benefits of global visibility and centralized control over a network. One of the main properties of the SDN architecture is the ability to offer a northbound interface (NBI), which enables network applications to access the SDN controller resources. However, the NBI can be compromised by a malicious application due to the lack of standardization and security aspects in the most current NBI designs. Therefore, in this paper, we propose a novel comprehensive security solution for securing the application–controller interface, named BCNBI. We propose a controller-independent lightweight blockchain architecture and exploit the security features of blockchain while limiting the blockchain’s computational overhead. BCNBI automatically verifies application and SDN controller credentials through token-based authentication. The proposed solution enforces fine-grained access control for each application’s API request and classifies the permission set into strict and normal policies, in order to add an extra level of security. In addition, the trustworthiness of applications is evaluated in order to prevent malicious activities. We implemented our blockchain-based solution to analyze its security, based on the confidentiality–integrity–availability model criteria, and evaluated the introduced overhead in terms of processing time and packet overhead. The experimental results demonstrate that the BCNBI can effectively secure the NBI, based on the fundamental security goals, while introducing insignificant overhead

A Proposed Framework for Secure Data Storage in a Big Data Environment Based on Blockchain and Mobile Agent

The sum of Big Data generated from different sources is increasing significantly with each passing day to extent that it is becoming challenging for traditional storage methods to store this massive amount of data. For this reason, most organizations have resolved to use third-party cloud storage to store data. Cloud storage has advanced in recent times, but it still faces numerous challenges with regard to security and privacy. This paper discusses Big Data security and privacy challenges and the minimum requirements that must be provided by future solutions. The main objective of this paper is to propose a new technical framework to control and manage Big Data security and privacy risks. A design science research methodology is used to carry out this project. The proposed framework takes advantage of Blockchain technology to provide secure storage of Big Data by managing its metadata and policies and eliminating external parties to maintain data security and privacy. Additionally, it uses mobile agent technology to take advantage of the benefits related to system performance in general. We present a prototype implementation for our proposed framework using the Ethereum Blockchain in a real data storage scenario. The empirical results and framework evaluation show that our proposed framework provides an effective solution for secure data storage in a Big Data environment