Notes on Cloud computing principles

This letter provides a review of fundamental distributed systems and economic Cloud computing principles. These principles are frequently deployed in their respective fields, but their inter-dependencies are often neglected. Given that Cloud Computing first and foremost is a new business model, a new model to sell computational resources, the understanding of these concepts is facilitated by treating them in unison. Here, we review some of the most important concepts and how they relate to each other

Research Issues in Cloud Computing

Cloud computing moved away from personal computers and the individual enterprise application server to services provided by the cloud of computers The emergence of cloud computing has made a tremendous impact on the Information Technology IT industry over the past few years Currently IT industry needs Cloud computing services to provide best opportunities to real world Cloud computing is in initial stages with many issues still to be addressed The objective of this paper is to explore the different issues of cloud computing and identify important research opportunities in this increasingly important area We present different design challenges categorized under security challenges Data Challenges Performance challenges and other Design Challenge

Internet of things: Conceptual network structure, main challenges and future directions

Internet of Things (IoT) is a key technology trend that supports our digitalized society in applications such as smart countries and smart cities. In this study, we investigate the existing strategic themes, thematic evolution structure, key challenges, and potential research opportunities associated with the IoT. For this study, we conduct a Bibliometric Performance and Network Analysis (BPNA), supplemented by an exhaustive Systematic Literature Review (SLR). Specifically, in BPNA, the software SciMAT is used to analyze 14,385 documents and 30,381 keywords in the Web of Science (WoS) database, which was released between 2002 and 2019. The results reveal that 31 clusters are classified according to their importance and development, and the conceptual structures of key clusters are presented, along with their performance analysis and the relationship with other subthemes. The thematic evolution structure describes the important cluster(s) over time. For the SLR, 23 documents are analyzed. The SLR reveals key challenges and limitations associated with the IoT. We expect the results will form the basis of future research and guide decision-making in the IoT and other supporting industries.Coordenaç~ao de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES) - Finance Code 001 and the Spanish Ministry of Science and Innovation under grants PID2019-105381 GA-100 (iScience)Consejo Nacional de Ciencia y Tecnología (CONACYT) and Direcci on General de Relaciones Exteriores (DGRI

Cloud Computing and Grid Computing 360-Degree Compared

Cloud Computing has become another buzzword after Web 2.0. However, there are dozens of different definitions for Cloud Computing and there seems to be no consensus on what a Cloud is. On the other hand, Cloud Computing is not a completely new concept; it has intricate connection to the relatively new but thirteen-year established Grid Computing paradigm, and other relevant technologies such as utility computing, cluster computing, and distributed systems in general. This paper strives to compare and contrast Cloud Computing with Grid Computing from various angles and give insights into the essential characteristics of both.Comment: IEEE Grid Computing Environments (GCE08) 200

FogLearn: Leveraging Fog-based Machine Learning for Smart System Big Data Analytics

Big data analytics with the cloud computing are one of the emerging area for processing and analytics. Fog computing is the paradigm where fog devices help to reduce latency and increase throughput for assisting at the edge of the client. This paper discussed the emergence of fog computing for mining analytics in big data from geospatial and medical health applications. This paper proposed and developed fog computing based framework i.e. FogLearn for application of K-means clustering in Ganga River Basin Management and realworld feature data for detecting diabetes patients suffering from diabetes mellitus. Proposed architecture employed machine learning on deep learning framework for analysis of pathological feature data that obtained from smart watches worn by the patients with diabetes and geographical parameters of River Ganga basin geospatial database. The results showed that fog computing hold an immense promise for analysis of medical and geospatial big data

LEGIoT: a Lightweight Edge Gateway for the Internet of Things

International audienceThe stringent latency together with the higher bandwidth requirements of current Internet of Things (IoT) applications, are leading to the definition of new network-infrastructures, such as Multi-access Edge Computing (MEC). This emerging paradigm encompasses the execution of many network tasks at the edge and in particular on constrained gateways that have also to deal with the plethora of disparate technologies available in the IoT landscape. To cope with these issues, we introduce a Lightweight Edge Gateway for the Internet of Things (LEGIoT) architecture. It relies on the modular characteristic of microservices and the flexibility of lightweight virtualization technologies to guarantee an extensible and flexible solution. In particular, by combining the implementation of specific frameworks and the benefits of container-based virtualization, our proposal enhances the suitability of edge gateways towards a wide variety of IoT protocols/applications (for both downlink and uplink) enabling an optimized resource management and taking into account requirements such as energy efficiency, multi-tenancy, and interoperability. LEGIoT is designed to be hardware agnostic and its implementation has been tested within a real sensor network. Achieved results demonstrate its scalability and suitability to host different applications meant to provide a wide range of IoT services