Digital twin (DT) is an emerging concept that is gaining attention in various industries It refers to the ability to clone a physical object (PO) into a software counterpart The softwarized object, termed logical object, reflects all the important properties and characteristics of the original object within a specific application context To fully determine the expected properties of the DT, this article surveys the state-of-the-art starting from the original definition within the manufacturing industry It takes into account related proposals emerging in other fields, namely augmented and virtual reality (eg, avatars), multiagent systems, and virtualization This survey thereby allows for the identification of an extensive set of DT features that point to the “softwarization” of POs To properly consolidate a shared DT definition, a set of foundational properties is identified and proposed as a common ground outlining the essential characteristics (must-haves) of a DT Once the DT definition has been consolidated, its technical and business value is discussed in terms of applicability and opportunities Four application scenarios illustrate how the DT concept can be used and how some industries are applying it The scenarios also lead to a generic DT architectural model This analysis is then complemented by the identification of software architecture models and guidelines in order to present a general functional framework for the DT This article, eventually, analyses a set of possible evolution paths for the DT considering its possible usage as a major enabler for the softwarization process

/pdf/digital-twin-in-the-iot-context-a-survey-on-technical-4dp6h32olt.pdf

Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models

The Cloud computing paradigm has revolutionised the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured significant attention of academia, industries, and government bodies. Now, it has emerged as the backbone of modern economy by offering subscription-based services anytime, anywhere following a pay-as-you-go model. This has instigated (1) shorter establishment times for start-ups, (2) creation of scalable global enterprise applications, (3) better cost-to-value associativity for scientific and high-performance computing applications, and (4) different invocation/execution models for pervasive and ubiquitous applications. The recent technological developments and paradigms such as serverless computing, software-defined networking, Internet of Things, and processing at network edge are creating new opportunities for Cloud computing. However, they are also posing several new challenges and creating the need for new approaches and research strategies, as well as the re-evaluation of the models that were developed to address issues such as scalability, elasticity, reliability, security, sustainability, and application models. The proposed manifesto addresses them by identifying the major open challenges in Cloud computing, emerging trends, and impact areas. It then offers research directions for the next decade, thus helping in the realisation of Future Generation Cloud Computing.

/pdf/a-manifesto-for-future-generation-cloud-computing-research-1w3p7gmvjj.pdf

A Manifesto for Future Generation Cloud Computing: Research Directions for the Next Decade

In line with cloud computing emergence as the dominant enterprise computing paradigm, our conceptualization of the cloud computing reference architecture and service construction has also evolved. For example, to address the need for cost reduction and rapid provisioning, virtualization has moved beyond hardware to containers. More recently, serverless computing or Function-as-a-Service has been presented as a means to introduce further cost-efficiencies, reduce configuration and management overheads, and rapidly increase an application's ability to speed up, scale up and scale down in the cloud. The potential of this new computation model is reflected in the introduction of serverless computing platforms by the main hyperscale cloud service providers. This paper provides an overview and multi-level feature analysis of seven enterprise serverless computing platforms. It reviews extant research on these platforms and identifies the emergence of AWS Lambda as a de facto base platform for research on enterprise serverless cloud computing. The paper concludes with a summary of avenues for further research.

A Preliminary Review of Enterprise Serverless Cloud Computing (Function-as-a-Service) Platforms

The Cloud computing paradigm has revolutionised the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured significant attention of academia, industries, and government bodies. Now, it has emerged as the backbone of modern economy by offering subscription-based services anytime, anywhere following a pay-as-you-go model. This has instigated (1) shorter establishment times for start-ups, (2) creation of scalable global enterprise applications, (3) better cost-to-value associativity for scientific and high performance computing applications, and (4) different invocation/execution models for pervasive and ubiquitous applications. The recent technological developments and paradigms such as serverless computing, software-defined networking, Internet of Things, and processing at network edge are creating new opportunities for Cloud computing. However, they are also posing several new challenges and creating the need for new approaches and research strategies, as well as the re-evaluation of the models that were developed to address issues such as scalability, elasticity, reliability, security, sustainability, and application models. The proposed manifesto addresses them by identifying the major open challenges in Cloud computing, emerging trends, and impact areas. It then offers research directions for the next decade, thus helping in the realisation of Future Generation Cloud Computing.

pdf/a-manifesto-for-future-generation-cloud-computing-research-clzu0835e3.pdf

Big data processing systems are evolving to be more stream oriented where each data record is processed as it arrives by distributed and low-latency computational frameworks on a continuous basis. As the stream processing technology matures and more organizations invest in digital transformations, new applications of stream analytics will be identified and implemented across a wide spectrum of industries. One of the challenges in developing a streaming analytics infrastructure is the difficulty in selecting the right stream processing framework for the different use cases. With a view to addressing this issue, in this paper we present a taxonomy, a comparative study of distributed data stream processing and analytics frameworks, and a critical review of representative open source (Storm, Spark Streaming, Flink, Kafka Streams) and commercial (IBM Streams) distributed data stream processing frameworks. The study also reports our ongoing study on a multilevel streaming analytics architecture that can serve as a guide for organizations and individuals planning to implement a real-time data stream processing and analytics framework.

/pdf/a-survey-of-distributed-data-stream-processing-frameworks-28krx7v0ll.pdf

A Survey of Distributed Data Stream Processing Frameworks

Sensor and smart phone technologies present opportunities for data explosion, streaming and collecting from heterogeneous devices every second. Analyzing these large datasets can unlock multiple behaviors previously unknown, and help optimize approaches to city wide applications or societal use cases. However, collecting and handling of these massive datasets presents challenges in how to perform optimized online data analysis ‘on-the-fly’, as current approaches are often limited by capability, expense and resources. This presents a need for developing new methods for data management particularly using public clouds to minimize cost, network resources and on-demand availability. This paper presents an implementation of the lambda architecture design pattern to construct a data-handling backend on Amazon EC2, providing high throughput, dense and intense data demand delivered as services, minimizing the cost of the network maintenance. This paper combines ideas from database management, cost models, query management and cloud computing to present a general architecture that could be applied in any given scenario where affordable online data processing of Big Datasets is needed. The results are presented with a case study of processing router sensor data on the current ESnet network data as a working example of the approach. The results showcase a reduction in cost and argue benefits for performing online analysis and anomaly detection for sensor data.

/pdf/lambda-architecture-for-cost-effective-batch-and-speed-big-aqakl6syv6.pdf

Lambda architecture for cost-effective batch and speed big data processing

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Peter Murphy

Author Tools

Chat about Author

Papers

Lambda architecture for cost-effective batch and speed big data processing