QoS-aware Storage Virtualization: A Framework for Multi-tier Infrastructures in Cloud Storage Systems

The emergence of the relatively modern phenomenon of cloud computing has manifested a different approach to the availability and storage of software and data on a remote online server ‘in the cloud’, which can be accessed by pre-determined users through the Internet, even allowing sharing of data in certain scenarios. Data availability, reliability, and access performance are three important factors that need to be taken into consideration by cloud providers when designing a high-performance storage system for any organization. Due to the high costs of maintaining and managing multiple local storage systems, it is now considered more applicable to design a virtualized multi-tier storage infrastructure, yet, the existing Quality of Service (QoS) must be guaranteed on the application level within the cloud without ongoing human intervention. Such interference seems necessary since the delivered QoS can vary widely both across and within storage tiers, depending on the access profile of the data. This survey paper encompasses a general framework for the optimal design of a distributed system in order to attain efficient data availability and reliability. To this extent, numerous state-of-the-art technologies and methods have been revised, especially for multi-tiered distributed cloud systems. Moreover, several critical aspects that must be taken into consideration for getting optimal performance of QoS-aware cloud systems are discussed, highlighting some solutions to handle failure situations, and the possible advantages and benefits of QoS. Finally, this papers attempts to argue the possible improvements that have been developed on QoS-aware cloud systems like Q-cloud since 2010, such as any extra attempts been carried forward to make the Q-cloud more adaptable and secure

Threats on the horizon: Understanding security threats in the era of cyber-physical systems

Disruptive innovations of the last few decades, such as smart cities and Industry 4.0, were made possible by higher integration of physical and digital elements. In today's pervasive cyber-physical systems, connecting more devices introduces new vulnerabilities and security threats. With increasing cybersecurity incidents, cybersecurity professionals are becoming incapable of addressing what has become the greatest threat climate than ever before. This research investigates the spectrum of risk of a cybersecurity incident taking place in the cyber-physical-enabled world using the VERIS Community Database. The findings were that the majority of known actors were from the US and Russia, most victims were from western states and geographic origin tended to reflect global affairs. The most commonly targeted asset was information, with the majority of attack modes relying on privilege abuse. The key feature observed was extensive internal security breaches, most often a result of human error. This tends to show that access in any form appears to be the source of vulnerability rather than incident specifics due to a fundamental trade-off between usability and security in the design of computer systems. This provides fundamental evidence of the need for a major reevaluation of the founding principles in cybersecurity

Buffer-aided 5G cooperative networks: Considering the source delay

© 2019 Association for Computing Machinery. Applying relays employed with data buffers drastically enhance the performance of cooperative networks. However, lengthening the packet delay is still a serious challenge for cooperative networks. This paper thoroughly studies a new factor affecting the packet delay which is the source delay. This factor plays a key role in calculating the total delay that messages encounter before reaching their destination. This delay is crucial especially in applications that require their messages to get transmitted as fast as possible. Markov chain is employed to model the system and analyze the source delay. Numerical simulations verify the analytical model, the results show that buffer-aided relays can beat non-buffer relays in terms of average packet delay, especially at low signal to noise ratio (SNR) range. This makes adding buffers to relays an attractive solution for the packet delay in 5G applications

A Methodology Of Real-Time Data Fusion For Localized Big Data Analytics

The traditional big-data analytical approaches use data clustering as small buckets while providing distributed computation among different child nodes. These approaches bring the issues especially concerning network capacity, specialized tools and applications not capable of being trained in a short period. Furthermore, raw data generated through IoT forming big data comes with the capability of producing highly unstructured and heterogeneous form of data. Such form of data grows into challenging task for the real-time analytics. It is highly valuable to have computational values available locally instead of through distributed resources to reduce real-time analytical challenges. This paper proposes a fusion of three different data models like relational, semantical, and big data based data and metadata involving their issues and enhanced capabilities. A case study is used to represent data fusion in action from RDB to Resource Description Framework. Whereas, issues and their feasible solutions are also being discussed in this paper

Articles 51 and 54 of the Jordanian Arbitration Act

Purpose On two different occasions, the Jordanian Constitutional Court has ruled that Articles 51 and 54 of the Jordanian Arbitration Act no. 31 of the year 2001 are unconstitutional and null. In view of this, this paper aims to attempt to give the reader a brief preview of the Jordanian Arbitration Act, the Jordanian Constitution and the Jordanian Constitutional Court. It also highlights and critically analyzes the Jordanian Constitutional Court two decisions pertaining to the Arbitration Act and its special implications in this regard from the perspective of arbitration law and the distinct characteristics embedded in it. Design/methodology/approach To examine how effective is the approach followed by the Constitutional Court in ruling the unconstitutionality of the aforementioned Articles, this work makes use of the primary and secondary data available in this regard as the main method to complete such an examination. By critically analyzing and comparing the various data contained in these sources, this work identifies the problems associated with such decisions. Findings This work submits that while the Constitutional Court has rested its rulings largely on constitutional principles, concerns arising from the Arbitration Act perspective have not been dealt with adequately by the Court. Furthermore, it argues that while the principles of the constitution shall be respected, the distinct characteristics of the arbitration law warrant a more careful approach than actually followed by the Court. Originality/value Taking into consideration the importance of arbitration as an alternative mean for dispute resolution, the Jordanian legislator has addressed the application of arbitration as early as the year 1953. However, while the Constitutional Court’s questionable approach to the aforementioned articles would necessarily hinder the use of arbitration, no comprehensive scholarly work has either examined such approach or addressed its implications. Accordingly, this work derives its originality and value from being the first of its kind to examine and address such a matte

A Framework for Identifying Influential People by Analyzing Social Media Data

In this paper, we introduce a new framework for identifying the most influential people from social sensor networks. Selecting influential people from social networks is a complicated task as it depends on many metrics like the network of friends, followers, reactions, comments, shares, etc. (e.g., friends-of-a-friend, friends-of-a-friend-of-a-friend). Data on social media are increasing day-by-day at an enormous rate. It is also a challenge to store and process these data. Towards this goal, we use Hadoop to store data and Apache Spark for the fast computation of the data. To select influential people, we apply the mechanisms of skyline query and top-k query. To the best of our knowledge, this is the first work to apply the Apache Spark framework to identify influential people on social sensor network, such as online social media. Our proposed mechanism can find influential people very quickly and efficiently on the data pattern of Facebook

Map as a Service: A Framework for Visualising and Maximising Information Return from Multi-Modal Wireless Sensor Networks

This paper presents a distributed information extraction and visualisation service, called the mapping service, for maximising information return from large-scale wireless sensor networks. Such a service would greatly simplify the production of higher-level, information-rich, representations suitable for informing other network services and the delivery of field information visualisations. The mapping service utilises a blend of inductive and deductive models to map sense data accurately using externally available knowledge. It utilises the special characteristics of the application domain to render visualisations in a map format that are a precise reflection of the concrete reality. This service is suitable for visualising an arbitrary number of sense modalities. It is capable of visualising from multiple independent types of the sense data to overcome the limitations of generating visualisations from a single type of sense modality. Furthermore, the mapping service responds dynamically to changes in the environmental conditions, which may affect the visualisation performance by continuously updating the application domain model in a distributed manner. Finally, a distributed self-adaptation function is proposed with the goal of saving more power and generating more accurate data visualisation. We conduct comprehensive experimentation to evaluate the performance of our mapping service and show that it achieves low communication overhead, produces maps of high fidelity, and further minimises the mapping predictive error dynamically through integrating the application domain model in the mapping service

A Methodology of Real-Time Data Fusion for Localized Big Data Analytics

The traditional big-data analytical approaches use data clustering as small buckets while providing distributed computation among different child nodes. These approaches bring the issues especially concerning network capacity, specialized tools and applications not capable of being trained in a short period. Furthermore, raw data generated through IoT forming big data comes with the capability of producing highly unstructured and heterogeneous form of data. Such form of data grows into challenging task for the real-time analytics. It is highly valuable to have computational values available locally instead of through distributed resources to reduce real-time analytical challenges. This paper proposes a fusion of three different data models like relational, semantical, and big data based data and metadata involving their issues and enhanced capabilities. A case study is used to represent data fusion in action from RDB to Resource Description Framework. Whereas, issues and their feasible solutions are also being discussed in this paper