Leonard Heilig

A Scientometric Analysis of Cloud Computing Literature

The popularity and rapid development of cloud computing in recent years has led to a huge amount of publications containing the achieved knowledge of this area of research. Due to the interdisciplinary nature and high relevance of cloud computing research, it becomes increasingly difficult or even impossible to understand the overall structure and development of this field without analytical approaches. While evaluating science has a long tradition in many fields, we identify a lack of a comprehensive scientometric study in the area of cloud computing. Based on a large bibliographic data base, this study applies scientometric means to empirically study the evolution and state of cloud computing research with a view from above the clouds. By this, we provide extensive insights into publication patterns, research impact and research productivity. Furthermore, we explore the interplay of related subtopics by analyzing keyword clusters. The results of this study provide a better understanding of patterns, trends and other important factors as a basis for directing research activities, sharing knowledge and collaborating in the area of cloud computing research.

A cloud brokerage approach for solving the resource management problem in multi-cloud environments

The Cloud Resource Management Problem in multi-clouds is discussed and tackled.A Biased Random-Key Genetic Algorithm for solving the problem is proposed.Our proposal allows to find high-quality solutions within short computational times providing the basis for real-time cloud brokerage.New best solutions for some of the well-defined problem instances are obtained. Cloud computing is increasingly becoming a mainstream technology-delivery model from which companies and research aim to gain value. As different cloud providers offer cloud services in various forms, there is a huge potential of optimizing the selection of those services to better fulfill user-, i.e., consumer- and application-related requirements. Recently, multi-cloud environments have been introduced thus making it possible to execute applications not only on single-provider resources, but also by using resources from multiple cloud providers. Due to the growing complexity in cloud marketplaces, a cloud brokerage mechanism, interacting on behalf of the consumers with various cloud providers, can be used to provide decision support for consumers. In this paper, we address the Cloud Resource Management Problem in multi-cloud environments that is a recent optimization problem aimed at reducing the monetary cost and the execution time of consumer applications using Infrastructure as a Service of multiple cloud providers. Due to the fact that consumers require real-time and high-quality solutions to economically automate cloud resource management and corresponding deployment processes, we propose an efficient Biased Random-Key Genetic Algorithm. The computational experiments over a large benchmark suite generated based on real cloud market resources indicate that the performance of our approach outperforms the approaches proposed in the literature.

Information systems in seaports: a categorization and overview

Information systems have become indispensable to the competitiveness of ports, facilitating communication and decision making for enhancing the visibility, efficiency, reliability, and security in port operations under various conditions. Providing value-added information services and analytics is increasingly important to maintain a competitive edge and to fulfill regulatory requirements. Consequently, it is necessary to survey current information systems both from an academic and practical standpoint. In this paper, we present a classification and a comprehensive survey of information systems and related information technologies applied in ports. As such, the paper provides a state-of-the-art information-centric view on port operations and aims to bridge the gap between industry solutions and academic works.

Supply Chain Risk Management in the Era of Big Data

The trend of big data implies novel opportunities and challenges for improving supply chain management. In particular, supply chain risk management can largely benefit from big data technologies and analytic methods for collecting, analyzing, and monitoring both supply chain internal data and environmental data. Due to the increasing complexity, particular attention must not only be put on the processing and analysis of data, but also on the interaction between big data information systems and users. In this paper, we analyze the role of big data in supply chains and present a novel framework of a supply chain risk management system for improving supply chain planning and supply chain risk management under stochastic environments by using big data technologies and analytics. The process-oriented framework serves as a guideline to integrate and analyze big data as well as to implement a respective supply chain risk management system. As such, this paper provides a novel direction of utilizing big data in supply chain risk management.

On the Value and Challenge of Real-Time Information in Dynamic Dispatching of Service Vehicles

Ubiquitous computing technologies and information systems pave the way for real-time planning and management. In the process of dynamic vehicle dispatching, the adherent challenge is to develop decision support systems using real-time information in an appropriate quality and at the right moment in order to improve their value creation. As real-time information enables replanning at any point in time, the question arises when replanning should be triggered. Frequent replanning may lead to efficient routing decisions due to vehicles’ diversions from current routes while less frequent replanning may enable effective assignments due to gained information. In this paper, the authors analyze and quantify the impact of the three main triggers from the literature, exogenous customer requests, endogenous vehicle statuses, and replanning in fixed intervals, for a dynamic vehicle routing problem with stochastic service requests. To this end, the authors generalize the Markov-model of an established dynamic routing problem and embed the different replanning triggers in an existing anticipatory assignment and routing policy. They particularly analyze under which conditions each trigger is advantageous. The results indicate that fixed interval triggers are inferior and dispatchers should focus either on the exogenous customer process or the endogenous vehicle process. It is further shown that the exogenous trigger is advantageous for widely spread customers with long travel durations and few dynamic requests while the endogenous trigger performs best for many dynamic requests and when customers are accumulated in clusters.

A Visualization Approach for Reducing the Perceived Complexity of COBIT 5

COBIT 5 is positioned in the market as a de-facto standard for enterprise governance of IT. Relevant literature and management experience, however, indicate that the adoption of the framework is challenging due to its perceived complexity. In this paper we present a software prototype aiming to promote the understanding of COBIT 5, its components and their relationships by means of information visualization, thus facilitating its usage and adoption in scientific and practical context. The current state of evaluating the prototype is outlined.

A Cloud-Based SOA for Enhancing Information Exchange and Decision Support in ITT Operations

With the emergence of global markets, the efficiency of port operations has become a decisive factor for the economy and quality of global supply chains. After decades of streamlining terminal operations, ports still experience process and coordination problems due to a lack of information exchange and decision support. To improve the overall efficiency and quality of port operations, innovative information systems for gathering and processing operational data based on identification, sensing and mobile technologies are required to enhance the visibility of operations in information systems. A port-centric information system also requires the integration of external systems, such as traffic control systems, to further consider external factors. In this paper, we present a cloud-based system architecture for the real-time collection, management, and utilization of operational data with a particular focus on inter-terminal transportation. The proposed system architecture is based on widely adopted standards and utilizes the cost-effectiveness and scalability of a cloud computing infrastructure for processing large amounts of operational data. The information system lowers the bounded rationality of actors providing information and decision support as a service to the port community and other stakeholders. As such, this paper is intended to provide a conceptual view on a system for enhancing the management of real-time data to better manage traffic flows and transport modes, enhance the coordination between intra-terminal and inter-terminal/landside operations, and reduce empty handling processes.

Cloud-Based Intelligent Transportation Systems Using Model Predictive Control

Recent and future technology development make intelligent transport systems a reality in contemporary societies leading to a higher quality, performance, and safety in transportation systems. In a big data era, however, efficient information technology infrastructures are necessary to support real-time applications efficiently. In this paper, we review different control structures based on model predictive control and embed them in cloud infrastructures. We especially focus on conceptual ideas for intelligent road transportation and explain how the proposed cloud-based system can be used for parallel and scalable computing supporting real-time decision making based on large volumes and a variety of data from different sources. As such, the paper provides a novel approach for applying data-driven intelligent transport systems that utilize scalable and cost-efficient cloud infrastructures based on model predictive control structures.

A Biased Random-Key Genetic Algorithm for the Cloud Resource Management Problem

Flexible use options and associated cost savings of cloud computing are increasingly attracting the interest from both researchers and practitioners. Since cloud providers offer various cloud services in different forms, there is a large potential of optimizing the selection of those services from the consumer perspective. In this paper, we address the Cloud Resource Management Problem that is a recent optimization problem aimed at reducing the payment cost and the execution time of consumer applications. In the related literature, there is one approach that successfully addresses this problem based on a Greedy Randomized Adaptive Search Procedure. Due to the fact that consumers require fast and high-quality solutions to economically automate cloud resource management and deployment processes, we propose an efficient Biased Random-Key Genetic Algorithm. The computational experiments over a benchmark suite generated based on real cloud market offerings indicate that the performance of our approach outperforms the approaches proposed in the literature.

A Scientometric Analysis of Public Transport Research

Public transport research involves a lot of disciplinary and interdisciplinary research applying methods, techniques, and technologies to investigate, regulate, and advance public transport. The importance of research in this area has led to a huge amount of publications in recent years. In this study, the authors conducted a comprehensive scientometric analysis of related literature published in 2009–2013 to empirically explore the consistence, focus areas, and key contributors of public transport research from a meta-perspective, providing novel insights into publication patterns, major topics, research impact, and productivity by focusing on short-term developments. As such, the results of this study provide a novel perspective on public transport research and may help achieving an overview on important characteristics.