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

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

A microservice architecture is expected to provide a better modularization and management of small and autonomous services. Other expected benefits include increased availability and time to market. There is a growing interest of both industry and academia on streamlining the migration of existing systems to a microservice architecture. However, the success of this migration is largely dependent on the use of appropriate criteria for extracting microservices from a code base. Recent studies indicate the selection and decomposition of microservices represent the main challenge along the migration. Academic techniques tend to support the extraction of microservices with either one or two conventional criteria, namely coupling and cohesion. There is limited knowledge on the criteria actually considered as useful by practitioners. Thus, we have performed an exploratory online survey with 15 specialists experienced on migrating systems to a microservices architecture. In particular, we question the relative usefulness of seven possible criteria for supporting decision-making along microservice extraction. The participants were also questioned about tools they have used, their limitations, and whether the decisions on extracted microservices were considered unsuccessful. Overall, the survey results suggest academic techniques do not totally satisfy the needs of practitioners. Practitioners often need to consider simultaneously at least four dominant criteria as well as their trade-offs to support their decisions. Most practitioners consider existing tooling support insufficient or even irrelevant to support their microservice extraction decisions.

Analysis of the criteria adopted in industry to extract microservices

Oil palm is of great importance in agricultural productivity for many tropic developing countries and accordingly investigating as well as counting oil palms is a meaningful and valuable research. In this paper, we firstly apply Faster-RCNN, one of the most popular object detection algorithms, to detect tree crowns from satellite images. Although Faster-RCNN has an excellent performance in well-known datasets of general object detection, it does not have obvious advantages in oil palm tree detection in this study compared with other classical machine learning based methods. We argue two reasons accounting for the drawbacks of Faster-RCNN: (1) the size of each oil palm tree is too small (only 17 × 17 pixels on average) in 0.6m-resolution QuickBird satellite images; (2) there are lots of other similar trees around the oil palm trees that make it difficult to detect them correctly. In order to reach a satisfying accuracy, we tailored the Region Proposal Network (RPN) and proposed a simple but practical post-processing strategy based on empirical planting rules, filtering out the wrongly detected trees (False Positives) effectively. Eventually we achieved a higher average F1-score of 94.99% (using IOU based evaluation matrices) in our six study regions compared wtih state-of-the-art oil palm detection methods. In addition, we proposed a workflow of large-scale oil palm tree detection using high-resolution remotely sensed images based deep learning methods.

Large-Scale Oil Palm Tree Detection from High-Resolution Remote Sensing Images Using Faster-RCNN

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

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%.

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Efficient Runtime Capture of Multiworkflow Data Using Provenance

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.

Marcelo Nery

Papers

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

Efficient Runtime Capture of Multiworkflow Data Using Provenance

Facing Digital Agriculture Challenges with Knowledge Engineering