Proceedings of the 8th IEEE International Conference on Cloud Computing Technology and Science (ClouCom 2016)

CloudCom is the premier conference on Cloud Computing worldwide, attracting researchers, developers, users, students and practitioners from the fields of big data, systems architecture, services research, virtualization, security and privacy, high performance computing, always with an emphasis on how to build cloud computing platforms with real impact. The conference is co-sponsored by the Institute of Electrical and Electronics Engineers (IEEE), is steered by the Cloud Computing Association, and draws on the excellence of its world-class Program Committee and its participants. The 8th IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2016) was held in the city of Luxembourg on 12-15 December 201

A Demo of Application Lifecycle Management for IoT Collaborative Neighborhood in the Fog

International audienceRegarding latency, privacy, resiliency and network scarcity management, only distributed approaches such as proposed by Fog Computing architecture can efficiently address the fantastic growth of the Internet of Things (IoT). IoT applications could be deployed and run hierarchically at different levels in an infrastructure ranging from centralized datacenters to the connected things themselves. Consequently, software entities composing IoT applications could be executed in many different configurations. The heterogeneity of the equipment and devices of the target infrastructure opens opportunities in the placement of the software entities, taking into account their requirements in terms of hardware, cyber-physical interactions and software dependencies. Once the most appropriate place has been found, software entities have to be deployed and run. Container-based virtualization has been considered to overpass the complexity of packaging, deploying and running software entities in a heterogeneous distributed infrastructure at the vicinity of the connected devices. This paper reports a practical experiment presented as a live demo that showcases a " Smart Bell in a Collaborative Neighborhood " IoT application in the Fog. Application Lifecycle Management (ALM) has been put in place based on Docker technologies to deploy and run micro-services in the context of Smart Homes operated by Orange

A Demo of Application Lifecycle Management for IoT Collaborative Neighborhood in the Fog

International audienceRegarding latency, privacy, resiliency and network scarcity management, only distributed approaches such as proposed by Fog Computing architecture can efficiently address the fantastic growth of the Internet of Things (IoT). IoT applications could be deployed and run hierarchically at different levels in an infrastructure ranging from centralized datacenters to the connected things themselves. Consequently, software entities composing IoT applications could be executed in many different configurations. The heterogeneity of the equipment and devices of the target infrastructure opens opportunities in the placement of the software entities, taking into account their requirements in terms of hardware, cyber-physical interactions and software dependencies. Once the most appropriate place has been found, software entities have to be deployed and run. Container-based virtualization has been considered to overpass the complexity of packaging, deploying and running software entities in a heterogeneous distributed infrastructure at the vicinity of the connected devices. This paper reports a practical experiment presented as a live demo that showcases a " Smart Bell in a Collaborative Neighborhood " IoT application in the Fog. Application Lifecycle Management (ALM) has been put in place based on Docker technologies to deploy and run micro-services in the context of Smart Homes operated by Orange

A Decision Support System for Planning and Operation of Maintenance and Customer Services in Electric Power Distribution Systems

This chapter aims to present the design and development of a decision support system (DSS) for the analysis, simulation, planning, and operation of maintenance and customer services in electric power distribution system (EPDS). The main objective of the DSS is to improve the decision‐making processes through visualization tools and simulation of real cases in the EPDS, in order to allow better planning in the short, medium, and long term. Therefore, the DSS helps managers and decision‐makers to reduce maintenance and operational costs, to improve system reliability, and to analyze new scenarios and conditions for system expansion planning. First, we introduce the key challenges faced by the decision‐makers in the planning and operation of maintenance and customer services in EPDS. Next, we discuss the benefits and the requirements for the DSS design and development, including use cases modeling and the software architecture. Afterwards, we present the capabilities of the DSS and discuss important decisions made during the implementation phases. We conclude the chapter with a discussion about the obtained results, pointing out the possible enhancements of the DSS, future extensions, and new use cases that may be addressed

Why Energy Matters? Profiling Energy Consumption of Mobile Crowdsensing Data Collection Frameworks

Mobile Crowdsensing (MCS) has emerged in the last years and has become one of the most prominent paradigms for urban sensing. The citizens actively participate in the sensing process by contributing data with their mobile devices. To produce data, citizens sustain costs, i.e., the energy consumed for sensing and reporting operations. Hence, devising energy efficient data collection frameworks (DCF) is essential to foster participation. In this work, we investigate from an energy-perspective the performance of different DCFs. Our methodology is as follows: (i) we developed an Android application that implements the DCFs, (ii) we profiled the energy and network performance with a power monitor and Wireshark, (iii) we included the obtained traces into CrowdSenSim simulator for large-scale evaluations in city-wide scenarios such as Luxembourg, Turin and Washington DC. The amount of collected data, energy consumption and fairness are the performance indexes evaluated. The results unveil that DCFs with continuous data reporting are more energy-efficient and fair than DCFs with probabilistic reporting. The latter exhibit high variability of energy consumption, i.e., to produce the same amount of data, the associated energy cost of different users can vary significantly

A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing

Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices, at the edge of the current network. To achieve higher performance in this new paradigm one has to consider how to combine the efficiency of resource usage at all three layers of architecture: end devices, edge devices, and the cloud. While cloud capacity is elastically extendable, end devices and edge devices are to various degrees resource-constrained. Hence, an efficient resource management is essential to make edge computing a reality. In this work, we first present terminology and architectures to characterize current works within the field of edge computing. Then, we review a wide range of recent articles and categorize relevant aspects in terms of 4 perspectives: resource type, resource management objective, resource location, and resource use. This taxonomy and the ensuing analysis is used to identify some gaps in the existing research. Among several research gaps, we found that research is less prevalent on data, storage, and energy as a resource, and less extensive towards the estimation, discovery and sharing objectives. As for resource types, the most well-studied resources are computation and communication resources. Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. Specifically, the impact of mobility and collaboration schemes requiring incentives are expected to be different in edge architectures compared to the classic cloud solutions. Finally, we find that fewer works are dedicated to the study of non-functional properties or to quantifying the footprint of resource management techniques, including edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless Communications and Mobile Computing journa

A Cost-Effective Distributed Framework for Data Collection in Cloud-based Mobile Crowd Sensing Architectures

Mobile crowd sensing received significant attention in the recent years and has become a popular paradigm for sensing. It operates relying on the rich set of built-in sensors equipped in mobile devices, such as smartphones, tablets and wearable devices. To be effective, mobile crowd sensing systems require a large number of users to contribute data. While several studies focus on developing efficient incentive mechanisms to foster user participation, data collection policies still require investigation. In this paper, we propose a novel distributed and sustainable framework for gathering information in cloud-based mobile crowd sensing systems with opportunistic reporting. The proposed framework minimizes cost of both sensing and reporting, while maximizing the utility of data collection and, as a result, the quality of contributed information. Analytical and simulation results provide performance evaluation for the proposed framework by providing a fine-grained analysis of the energy consumed. The simulations, performed in a real urban environment and with a large number of participants, aim at verifying the performance and scalability of the proposed approach on a large scale under different user arrival patterns

Data Science as an Interdiscipline: Historical Parallels from Information Science

Considerable debate exists today on almost every facet of what data science entails. Almost all commentators agree, however, that data science must be characterized as having an interdisciplinary or metadisciplinary nature. There is interest from many stakeholders in formalizing the emerging discipline of data science by defining boundaries and core concepts for the field. This paper presents a comparison between the data science of today and the development and evolution of information science over the past century. Data science and information science present a number of similarities: diverse participants and institutions, contested disciplinary boundaries, and diffuse core concepts. This comparison is used to discuss three questions about data science going forward: (1) What will be the focal points around which data science and its stakeholders coalesce? (2) Can data science stakeholders use the lack of disciplinary clarity as a strength? (3) Can data science feed into an “empowering profession”? The historical comparison to information science suggests that the boundaries of data science will be a source of contestation and debate for the foreseeable future. Stakeholders face many questions as data science evolves with the inevitable societal and technological changes of the next few decades

The data scientist: what companies expect from recent graduates and the role of business schools

This research aims to understand if universities are preparing students well enough to cater to companies’ current data science needs, particularly in terms of skills. To do so, the study reviews data science programs in Portuguese business schools and follows a mixed-method approach, by interviewing data science managers, and surveying recent data scientists online. It was concluded that there is room to improve the skillsets of graduates and recommendations are provided to business schools, who can have a strategic role in bridging the skill gap for future graduates, in what is consensually perceived as strategic, fast growing professional occupation