No abstract

No abstract

In this work, we present a novel approach that leverages Linux containers to achieve High Availability (HA) for cloud applications. A middleware that is comprised of a set of HA agents is defined to compensate the limitations of Linux containers in achieving HA. In our approach we start modeling at the application level, considering the dependencies among application components. We generate the proper scheduling scheme and then deploy the application across containers in the cloud. For each container that hosts critical component(s), we continuously monitor its status and checkpoint its full state, and then react to its failure by restarting locally or failing over to another host where we resume the computing from the most recent state. By using this strategy, all components hosted in a container are preserved without intrusively imposing modification on the application side. Finally, the feasibility of our approach is verified by building a proof-of-concept prototype and a case study of a video streaming application.

Reputation and trust are significantly important and play a pivotal role in enabling multiple parties to establish relationships that achieve mutual benefit especially in an E-Commerce (EC) environment. There are several factors negatively affecting the sight of customers and sellers in terms of reputation. For instance, lack of credibility in providing feedback reviews, by which users might create phantom feedback reviews to support their reputation. Thus, we will feel that these reviews and ratings are unfair. In this study, we have used Sentiment Analysis (SA) which is now the subject generating the most interest in the field of text analysis. One of the major challenges confronting SA today is how to detect unfair negative reviews, unfair neutral reviews and unfair positive reviews from opinion reviews. Sentiment classification techniques are used against a dataset of consumer reviews. Precisely, we provide comparison of four supervised machine learning algorithms: Naïve Bayes (NB), Decision Tree (DT-J48), Logistic Regression (LR) and Support Vector Machine (SVM) for sentiment classification using three datasets of reviews, including Clothing, Shoes and Jewelry reviews, Baby reviews as well as Pet Supplies reviews. In order to evaluate the performance of sentiment classification, this work has implemented accuracy, precision and recall as a performance measure. Our experiments' results show that the Logistic Regression (LR) algorithm is the best classifier with the highest accuracy as compared to the other three classifiers, not merely in text classification, but in unfair reviews detection as well.

Undoubtedly, the supply chain management (SCM) system is an important part of many organizations worldwide; over time, the technologies used to manage a supply chain ecosystem have, therefore, a great impact on businesses’ effectiveness. Among others, numerous developments have been made that targeted to have robust supply chain systems to efficiently manage the growing demands of various supplies, considering the underlying requirements and main challenges such as scalability, specifically privacy and security, of various business networks. Internet of things (IoT) comes with a solution to manage a complex, scalable supply chain system, but to provide and attain enough security during information exchange, along with keeping the privacy of its users, is the great inherent challenge of IoT. To fulfill these limitations, this study designs and models a scaled IoT-based supply chain (IoT-SC) system, comprising several operations and participants, and deploys mechanisms to leverage the security, mainly confidentially, integrity, authentication (CIA), and a digital signature scheme to leverage potentially secured non-repudiation security service for the worst-case scenario, and to leverage privacy to keep users sensitive personal and location information protected against adversarial entities to the IoT-SC system. Indeed, a scaled IoT-SC system certainly opens new challenges to manage privacy and security while communicating. Therefore, in the IoT-SC system, each transaction writes from edge computing nodes to the IoT-SC controller is thoroughly examined to ensure the proposed solutions in bi-directional communication, and their robustness against adversarial behaviors. Future research works, employing blockchain and its integrations, are detailed as paces to accelerate the privacy and security of the IoT-SC system, for example, migrating IoT-centric computing to an immutable, decentralized platform.

No abstract

Real-time systems (RTS) have strict timing constraints and limited resources. The satisfaction of RTS timing constraints is required for their correction. In order to reduce the cost due to late discovery of design flaws and/or violations of timing constraints of RTS as well as to speed up their development to cope with time-to-market requirements, it is important to validate, at early stages of the development process, the functional and nonfunctional properties of RTS. In addition, RTS complexity is continuously increasing which makes their design very challenging. UML, a graphical object-oriented modeling language, is suitable to deal with this complexity. UML also supports predictive, quantitative analysis through its real-time profiles. The objective of this paper is to review the most important UML profiles for real-time from the academia, the industry and/or standard organizations; and the research activity that revolves around these profiles.

Abstract. Graphical notations designed by committees in the context of standardization bodies, like Object Management Group (OMG), are widely used in the industry and academia. Naive users of these notations have limited background on visualization, documentation and specification of workflows, data or software systems. Several studies have pointed out the fact that these notations do not convey any particular semantics and their understanding is not perceptually immediate. As reported in these studies, this lack of semantic transparency increases the cognitive load to differentiate between concepts, slows down the learning and comprehension of the language constructs. This paper reports on a set of experiments that confirm the lack of semantic transparency of the Unified Modeling Language (UML) as designed by OMG and compares this standard to alternative solutions where naive users are involved in the design of the notations to speed-up the learning of these languages to new users.