Current industry trends in enterprise architectures indicate movement from Service-Oriented Architecture (SOA) to Microservices By understanding the key differences between these two approaches and their features, we can design a more effective Microservice architecture by avoiding SOA pitfalls To do this, we must know why this shift is happening and how key SOA functionality is addressed by key features of the Microservice-based system Unfortunately, Microservices do not address all SOA shortcomings In addition, Microservices introduce new challenges This work provides a detailed analysis of the differences between these two architectures and their features Next, we describe both research and industry perspectives on the strengths and weaknesses of both architectural directions Finally, we perform a systematic mapping study related to Microservice research, identifying interest and challenges in multiple categories from a range of recent research

Contextual understanding of microservice architecture: current and future directions

This book examines an important phenomenon for competitiveness and innovation in industry: namely the growing use of scientific principles in industrial research. Industrial innovation still arises from systematic trial-and-error experiments with many designs and objects, but these experiments are being guided by a more rational understanding of phenomena. This has important implications for market structure, firm strategies and competition. Science and Innovation focuses on the pharmaceutical industry. It discusses the changes that the notable advances in the life sciences since the 1980s have exerted on the strategies of drug companies, the organization of their internal research, their relationships with scientific institutions, the division of labour between large pharmaceutical firms and small research-intensive suppliers, the productivity of drug discovery and the productivity of R & D.

Science and innovation

Deep neural network based date palm tree detection in drone imagery

Palm oil is a major contributor to Malaysia’s GDP in the agriculture sector. The sheer vastness of oil palm plantations requires a huge effort to administer. An oil palm plantation in regards to th...

Automatic Detection of Oil Palm Tree from UAV Images Based on the Deep Learning Method

Microservice architecture style has been gaining wide impetus in the software engineering industry. Researchers and practitioners have adopted the microservices concepts into several application domains such as the internet of things, cloud computing, service computing, and healthcare. Applications developed in alignment with the microservices principles require an underlying platform with management capabilities to coordinate the different microservice units and ensure that the application functionalities are delivered to the user. A multitude of approaches has been proposed for the various tasks in microservices‐based systems. However, since the field is relatively young, there is a need to organize the different research works. In this study, we present a comprehensive review of the research approaches directed toward microservice architectures and propose a multilevel taxonomy to categorize the existing research. The study also discusses the different distributed computing paradigms employing microservices and identifies the open research challenges in the domain.

Straddling the crevasse: A review of microservice software architecture foundations and recent advancements

In order to achieve economies of scale, a Software as a Service (SaaS) should be configurable, multi-tenant efficient, and scalable. But building SaaS with these characteristics comes at a price of having more complex services. Some works in the literature integrate software product line engineering and service-oriented architecture to tackle the complexity of building multi-tenant SaaS. Most of these works focused on centralized approaches that rely on middleware or platforms, but they do not investigate the use of decentralized architectural style. Microservices architecture is an architectural style that relies on small, decentralized, and autonomous services that work together. Thus, this paper investigates the integrated use of microservices architecture and software produt line techniques to develop multi-tenant SaaS. We conducted an empirical study that analyzes the behavior of software reuse during the evolution of a multi-tenant SaaS. This empirical study showed an average software reuse of 62% of lines of code among tenants. We also provide lessons we learned during the the re-engineering and maintenance of such multi-tenant SaaS.

Using Microservices and Software Product Line Engineering to Support Reuse of Evolving Multi-tenant SaaS

Palm oil is the largest vegetable oil in the world in terms of produced volume, and 75% of global production is used for food and cooking purposes. Sustainable management of the producing areas calls for the frequent assessment of field conditions. In this paper, we investigate an automatic algorithm based on deep learning that is capable to build an inventory of individual oil-palm trees using aereal color images collected by unmanned aerial vehicles. The idea consists of combining the outputs of two independent convolutional neural networks, trained on partially distinct subsets of samples and different spatial scales to capture coarse and fine details of image patches. The estimated posterior probabilities are combined by simple averaging as to improve detection accuracy and estimate the confidence for each individual detection. Non-maxima suppression removes weak detections. Experiments at three commercial oil-palm tree plantations sites aged two, four, and 16 years in Northern Brazil revealed overall detection accuracies in the range 91.2-98.8% using orthomosaics of decimeter spatial resolution. The proposed approach can be a useful component of a forest monitoring system based on remote sensing.

Oil-Palm Tree Detection in Aerial Images Combining Deep Learning Classifiers

Knowledge Engineering is key to enable knowledge extraction, representation and reasoning, leading to better business insights and decisions. Current advances in machine learning and new trends in AI are bringing a plethora of algorithms capable of performing advanced pattern recognition and data classification. The ability to link, to organize and to query the outputs of these algorithms as well as the ability to handle huge amounts of data and its multiple sources is crucial to maximize the potential of such advances, specially over large datasets. This paper presents challenges in the context of digital agriculture and our position in moving forward with these capabilities whilst using knowledge engineering techniques.

Facing Digital Agriculture Challenges with Knowledge Engineering

Computational Science and Engineering (CSE) projects are typically developed by multidisciplinary teams. Despite being part of the same project, each team manages its own workflows, using specific execution environments and data processing tools. Analyzing the data processed by all workflows globally is a core task in a CSE project. However, this analysis is hard because the data generated by these workflows are not integrated. In addition, since these workflows may take a long time to execute, data analysis needs to be done at runtime to reduce cost and time of the CSE project. A typical solution in scientific data analysis is to capture and relate the data in a provenance database while the workflows run, thus allowing for data analysis at runtime. However, the main problem is that such data capture competes with the running workflows, adding significant overhead to their execution. To mitigate this problem, we introduce in this paper a system called ProvLake, which adopts design principles for providing efficient distributed data capture from the workflows. While capturing the data, ProvLake logically integrates and ingests them into a provenance database ready for analyses at runtime. We validated ProvLake in a real use case in the O&G industry encompassing four workflows that process 5 TB datasets for a deep learning classifier. Compared with Komadu, the closest solution that meets our goals, our approach enables runtime multiworkflow data analysis with much smaller overhead, such as 0.1%.

/pdf/efficient-runtime-capture-of-multiworkflow-data-using-32yh22p0mj.pdf

Efficient Runtime Capture of Multiworkflow Data Using Provenance

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Using Microservices and Software Product Line Engineering to Support Reuse of Evolving Multi-tenant SaaS

Oil-Palm Tree Detection in Aerial Images Combining Deep Learning Classifiers

Facing Digital Agriculture Challenges with Knowledge Engineering

Efficient Runtime Capture of Multiworkflow Data Using Provenance