Monitoring in fog computing: state-of-the-art and research challenges

Fog computing has rapidly become a widely accepted computing paradigm to mitigate cloud computing-based infrastructure limitations such as scarcity of bandwidth, large latency, security, and privacy issues. Fog computing resources and applications dynamically vary at run-time, and they are highly distributed, mobile, and appear-disappear rapidly at any time over the internet. Therefore, to ensure the quality of service and experience for end-users, it is necessary to comply with a comprehensive monitoring approach. However, the volatility and dynamism characteristics of fog resources make the monitoring design complex and cumbersome. The aim of this article is therefore three-fold: 1) to analyse fog computing-based infrastructures and existing monitoring solutions; 2) to highlight the main requirements and challenges based on a taxonomy; 3) to identify open issues and potential future research directions.This work has been (partially) funded by H2020 EU/TW 5G-DIVE (Grant 859881) and H2020 5Growth (Grant 856709). It has been also funded by the Spanish State Research Agency (TRUE5G project, PID2019-108713RB-C52 PID2019-108713RB-C52 / AEI / 10.13039/501100011033)

The Interplay between Cloud-based SOA and IT Departments: Research Directions

Cloud-based SOA refers to an application architecture within which all functionalities are defined as independent serviceswith cloud-based APIs that can be used to leverage external computing resources through ubiquitous Internet access. Thispaper uses cloud-based SOA as a new form of IT-enabled enterprise transformation to reconceptualize the roles of ITdepartments. A proposed conceptual framework argues that IT departments and cloud-based SOA are mutually influenced.Upon reviewing relevant literature, this paper suggests that research should reexamine critical success factors in cloud-basedSOA implementations and investigate influences of cloud-based SOA implementations in the reconstruction of ITdepartments. Research questions and propositions are developed to guide research in this area

Big Data and Regional Science: Opportunities, Challenges, and Directions for Future Research

Recent technological, social, and economic trends and transformations are contributing to the production of what is usually referred to as Big Data. Big Data, which is typically defined by four dimensions -- Volume, Velocity, Veracity, and Variety -- changes the methods and tactics for using, analyzing, and interpreting data, requiring new approaches for data provenance, data processing, data analysis and modeling, and knowledge representation. The use and analysis of Big Data involves several distinct stages from "data acquisition and recording" over "information extraction" and "data integration" to "data modeling and analysis" and "interpretation", each of which introduces challenges that need to be addressed. There also are cross-cutting challenges, which are common challenges that underlie many, sometimes all, of the stages of the data analysis pipeline. These relate to "heterogeneity", "uncertainty", "scale", "timeliness", "privacy" and "human interaction". Using the Big Data analysis pipeline as a guiding framework, this paper examines the challenges arising in the use of Big Data in regional science. The paper concludes with some suggestions for future activities to realize the possibilities and potential for Big Data in regional science.Series: Working Papers in Regional Scienc