Microservices Architecture Enables DevOps: an Experience Report on Migration to a Cloud-Native Architecture

This article reports on experiences and lessons learned during incremental migration and architectural refactoring of a commercial mobile back end as a service to microservices architecture. It explains how the researchers adopted DevOps and how this facilitated a smooth migration

A collaboration platform for enabling industrial symbiosis : towards creating a self-learning waste-to-resource database for recommending industrial symbiosis transactions using text analytics

Industrial Symbiosis (IS) adopts a collaborative approach, which aims to re-channel resources – traditionally considered spent and non-productive – towards alternative value-adding pathways. Empirically, the concept of IS has been rapidly implemented in practice through a facilitated approach, whereby businesses are engaged and “match-made” via a facilitating body. While recommending alternative pathways for companies to establish IS-based transactions is a long-standing practice, recent technological advancement has shifted the nature of this task from one that is based purely on human intellect and reasoning, towards one which leverages intelligent recommendation algorithms to provide relevant suggestions. Traditionally, these recommendation engines rely on manually populated knowledge bases that are not only labor-intensive to build but also costly to maintain. This work presents the creation of a self-learning waste-to-resource database supporting an IS recommendation system by utilizing text analytics techniques. We further demonstrate its practical application to support IS facilitating bodies in their core activity

Big Data Management Challenges, Approaches, Tools and their limitations

International audienceBig Data is the buzzword everyone talks about. Independently of the application domain, today there is a consensus about the V's characterizing Big Data: Volume, Variety, and Velocity. By focusing on Data Management issues and past experiences in the area of databases systems, this chapter examines the main challenges involved in the three V's of Big Data. Then it reviews the main characteristics of existing solutions for addressing each of the V's (e.g., NoSQL, parallel RDBMS, stream data management systems and complex event processing systems). Finally, it provides a classification of different functions offered by NewSQL systems and discusses their benefits and limitations for processing Big Data

Data management in cloud environments: NoSQL and NewSQL data stores

: Advances in Web technology and the proliferation of mobile devices and sensors connected to the Internet have resulted in immense processing and storage requirements. Cloud computing has emerged as a paradigm that promises to meet these requirements. This work focuses on the storage aspect of cloud computing, specifically on data management in cloud environments. Traditional relational databases were designed in a different hardware and software era and are facing challenges in meeting the performance and scale requirements of Big Data. NoSQL and NewSQL data stores present themselves as alternatives that can handle huge volume of data. Because of the large number and diversity of existing NoSQL and NewSQL solutions, it is difficult to comprehend the domain and even more challenging to choose an appropriate solution for a specific task. Therefore, this paper reviews NoSQL and NewSQL solutions with the objective of: (1) providing a perspective in the field, (2) providing guidance to practitioners and researchers to choose the appropriate data store, and (3) identifying challenges and opportunities in the field. Specifically, the most prominent solutions are compared focusing on data models, querying, scaling, and security related capabilities. Features driving the ability to scale read requests and write requests, or scaling data storage are investigated, in particular partitioning, replication, consistency, and concurrency control. Furthermore, use cases and scenarios in which NoSQL and NewSQL data stores have been used are discussed and the suitability of various solutions for different sets of applications is examined. Consequently, this study has identified challenges in the field, including the immense diversity and inconsistency of terminologies, limited documentation, sparse comparison and benchmarking criteria, and nonexistence of standardized query languages

B-SMART: A Reference Architecture for Artificially Intelligent Autonomic Smart Buildings

The pervasive application of artificial intelligence and machine learning algorithms is transforming many industries and aspects of the human experience. One very important industry trend is the move to convert existing human dwellings to smart buildings, and to create new smart buildings. Smart buildings aim to mitigate climate change by reducing energy consumption and associated carbon emissions. To accomplish this, they leverage artificial intelligence, big data, and machine learning algorithms to learn and optimize system performance. These fields of research are currently very rapidly evolving and advancing, but there has been very little guidance to help engineers and architects working on smart buildings apply artificial intelligence algorithms and technologies in a systematic and effective manner. In this paper we present B-SMART: the first reference architecture for autonomic smart buildings. B-SMART facilitates the application of artificial intelligence techniques and technologies to smart buildings by decoupling conceptually distinct layers of functionality and organizing them into an autonomic control loop. We also present a case study illustrating how B-SMART can be applied to accelerate the introduction of artificial intelligence into an existing smart building

Facing the Giant: a Grounded Theory Study of Decision-Making in Microservices Migrations

Background: Microservices migrations are challenging and expensive projects with many decisions that need to be made in a multitude of dimensions. Existing research tends to focus on technical issues and decisions (e.g., how to split services). Equally important organizational or business issues and their relations with technical aspects often remain out of scope or on a high level of abstraction. Aims: In this study, we aim to holistically chart the decision-making that happens on all dimensions of a migration project towards microservices (including, but not limited to, the technical dimension). Method: We investigate 16 different migration cases in a grounded theory interview study, with 19 participants that recently migrated towards microservices. This study strongly focuses on the human aspects of a migration, through stakeholders and their decisions. Results: We identify 3 decision-making processes consisting of 22decision-points and their alternative options. The decision-points are related to creating stakeholder engagement and assessing feasibility, technical implementation, and organizational restructuring. Conclusions: Our study provides an initial theory of decision-making in migrations to microservices. It also outfits practitioners with a roadmap of which decisions they should be prepared to make and at which point in the migration.Comment: 11 pages, 7 figure

Consumer Life Cycle and Profiling: A Data Mining Perspective

With the development of technology and continuously increasing of the market demand, the concept to produce better merchandises is generated in the companies. Each customer wants an individual approach or exclusive product, which creates the concept: “one customer one product.” The implementation of the one-to-one approach in the current days is the main exciting task of companies. Millions of customers lead to millions of exclusive products from the manufactures’ views. It is the primary step to study the needs of customers in the market economy. The main task for a company is to know the customer and to provide their desired products and services. In order to get knowledge ahead of the customers’ wishes, a system of profiling potential customers is created accordingly. This chapter provides the review of the customer lifetime from the reach customer (claim future customer’s attention) to the loyalty customer (turn a customer into a company advocate). During the discussion about the customer lifetime, readers will get acquainted with such technologies as funnel analysis, data management platform, customer profiling, customer behavior analysis, and others. The listed technologies in a complex will be created as the one-to-one product or service with a high Return on Investment (ROI)

CPS Data Streams Analytics based on Machine Learning for Cloud and Fog Computing: A Survey

Cloud and Fog computing has emerged as a promising paradigm for the Internet of things (IoT) and cyber-physical systems (CPS). One characteristic of CPS is the reciprocal feedback loops between physical processes and cyber elements (computation, software and networking), which implies that data stream analytics is one of the core components of CPS. The reasons for this are: (i) it extracts the insights and the knowledge from the data streams generated by various sensors and other monitoring components embedded in the physical systems; (ii) it supports informed decision making; (iii) it enables feedback from the physical processes to the cyber counterparts; (iv) it eventually facilitates the integration of cyber and physical systems. There have been many successful applications of data streams analytics, powered by machine learning techniques, to CPS systems. Thus, it is necessary to have a survey on the particularities of the application of machine learning techniques to the CPS domain. In particular, we explore how machine learning methods should be deployed and integrated in cloud and fog architectures for better fulfilment of the requirements, e.g. mission criticality and time criticality, arising in CPS domains. To the best of our knowledge, this paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads to the discussion and guidance of how the CPS machine learning methods should be deployed in a cloud and fog architecture

Analysis of Microservice Coupling Measures

Microservices architectures are composed of a collection of modular, fault-tolerant services. In recent years, the software engineering community has published research on viable, recurring, and effective architectural patterns in microservices-based architectures, as they are critical to the maintenance and scaling of microservice-based systems. As well as, ensuring low coupling and strong cohesion among the microservices that comprise the cloud-native application is a crucial property.Services that are loosely connected and highly coherent allow development teams to work in parallel, eliminating communication overhead between teams. In the first section of this thesis, we attempted to generate a dataset by starting with a selected list of microservice-based projects. The collection is made up of 20 open-source applications that all use certain microservice architecture patterns. Furthermore, the dataset includes information about the aforementioned projects’ interservice calls and dependencies. In the second section, we suggested methods for computing and visualizing the coupling be- tween microservices by expanding and adapting the notions underlying standard of structural coupling calculation. We validate these measures using a case study of 17 projects selected from the aforementioned dataset, and we propose an automated method for measuring them. The findings of this study emphasize how these metrics give practitioners with quantitative and visual views of service architecture, that can be used to design advanced measures to monitor the development of services