Digital Twin Platform for Water Treatment Plants Using Microservices Architecture

The effects of climate change and the rapid growth of societies often lead to water scarcity and inadequate water quality, resulting in a significant number of diseases. The digitalization of infrastructure and the use of Digital Twins are presented as alternatives for optimizing resources and the necessary infrastructure in the water cycle. This paper presents a framework for the development of a Digital Twin platform for a wastewater treatment plant, based on a microservices architecture which optimized its design for edge computing implementation. The platform aims to optimize the operation and maintenance processes of the plant's systems, by employing machine learning techniques, process modeling and simulation, as well as leveraging the information contained in BIM models to support decision-making.

Smart Resource Allocation in Mobile Cloud Next-Generation Network (NGN) Orchestration with Context-Aware Data and Machine Learning for the Cost Optimization of Microservice Applications

Mobile cloud computing (MCC) provides resources to users to handle smart mobile applications. In MCC, task scheduling is the solution for mobile users' context-aware computation resource-rich applications. Most existing approaches have achieved a moderate service reliability rate due to a lack of instance-centric resource estimations and task offloading, a statistical NP-hard problem. The current intelligent scheduling process cannot address NP-hard problems due to traditional task offloading approaches. To address this problem, the authors design an efficient context-aware service offloading approach based on instance-centric measurements. The revised machine learning model/algorithm employs task adaptation to make decisions regarding task offloading. The proposed MCVS scheduling algorithm predicts the usage rates of individual microservices for a practical task scheduling scheme, considering mobile device time, cost, network, location, and central processing unit (CPU) power to train data. One notable feature of the microservice software architecture is its capacity to facilitate the scalability, flexibility, and independent deployment of individual components. A series of simulation results show the efficiency of the proposed technique based on offloading, CPU usage, and execution time metrics. The experimental results efficiently show the learning rate in training and testing in comparison with existing approaches, showing efficient training and task offloading phases. The proposed system has lower costs and uses less energy to offload microservices in MCC. Graphical results are presented to define the effectiveness of the proposed model. For a service arrival rate of 80%, the proposed model achieves an average 4.5% service offloading rate and 0.18% CPU usage rate compared with state-of-the-art approaches. The proposed method demonstrates efficiency in terms of cost and energy savings for microservice offloading in mobile cloud computing (MCC).

Integrated Simulation and Calibration Framework for Heating System Optimization

In a time where sustainability and CO2 efficiency are of ever-increasing importance, heating systems deserve special considerations. Despite well-functioning hardware, inefficiencies may arise when controller parameters are not well chosen. While monitoring systems could help to identify such issues, they lack improvement suggestions. One possible solution would be the use of digital twins; however, critical values such as the water consumption of the residents can often not be acquired for accurate models. To address this issue, coarse models can be employed to generate quantitative predictions, which can then be interpreted qualitatively to assess "better or worse" system behavior. In this paper, we present a simulation and calibration framework as well as a preprocessing module. These components can be run locally or deployed as containerized microservices and are easy to interface with existing data acquisition infrastructure. We evaluate the two main operating modes, namely automatic model calibration, using measured data, and the optimization of controller parameters. Our results show that using a coarse model of a real heating system and data augmentation through preprocessing, it is possible to achieve an acceptable fit of partially incomplete measured data, and that the calibrated model can subsequently be used to perform an optimization of the controller parameters in regard to the simulated boiler gas consumption.

AIMS: An Automatic Semantic Machine Learning Microservice Framework to Support Biomedical and Bioengineering Research

The fusion of machine learning and biomedical research offers novel ways to understand, diagnose, and treat various health conditions. However, the complexities of biomedical data, coupled with the intricate process of developing and deploying machine learning solutions, often pose significant challenges to researchers in these fields. Our pivotal achievement in this research is the introduction of the Automatic Semantic Machine Learning Microservice (AIMS) framework. AIMS addresses these challenges by automating various stages of the machine learning pipeline, with a particular emphasis on the ontology of machine learning services tailored to the biomedical domain. This ontology encompasses everything from task representation, service modeling, and knowledge acquisition to knowledge reasoning and the establishment of a self-supervised learning policy. Our framework has been crafted to prioritize model interpretability, integrate domain knowledge effortlessly, and handle biomedical data with efficiency. Additionally, AIMS boasts a distinctive feature: it leverages self-supervised knowledge learning through reinforcement learning techniques, paired with an ontology-based policy recording schema. This enables it to autonomously generate, fine-tune, and continually adapt to machine learning models, especially when faced with new tasks and data. Our work has two standout contributions demonstrating that machine learning processes in the biomedical domain can be automated, while integrating a rich domain knowledge base and providing a way for machines to have self-learning ability, ensuring they handle new tasks effectively. To showcase AIMS in action, we have highlighted its prowess in three case studies of biomedical tasks. These examples emphasize how our framework can simplify research routines, uplift the caliber of scientific exploration, and set the stage for notable advances.

Moving Microgrid Hierarchical Control to an SDN-Based Kubernetes Cluster: A Framework for Reliable and Flexible Energy Distribution

Software Defined Networking (SDN) is a communication alternative to increase the scalability and resilience of microgrid hierarchical control. The common architecture has a centralized and monolithic topology, where the controller is highly susceptible to latency problems, resiliency, and scalability issues. This paper proposes a novel and intelligent control network to improve the performance of microgrid communications, solving the typical drawback of monolithic SDN controllers. The SDN controller's functionalities are segregated into microservices groups and distributed through a bare-metal Kubernetes cluster. Results are presented from PLECS hardware in the loop simulation to validate the seamless transition between standard hierarchical control to the SDN networked microgrid. The microservices significantly impact the performance of the SDN controller, decreasing the latency by 10.76% compared with a monolithic architecture. Furthermore, the proposed approach demonstrates a 42.23% decrease in packet loss versus monolithic topologies and a 53.41% reduction in recovery time during failures. Combining Kubernetes with SDN microservices can eliminate the single point of failure in hierarchical control, improve application recovery time, and enhance containerization benefits, including security and portability. This proposal represents a reference framework for future edge computing and intelligent control approaches in networked microgrids.

Integration of neuromorphic AI in event-driven distributed digitized systems: Concepts and research directions

Increasing complexity and data-generation rates in cyber-physical systems and the industrial Internet of things are calling for a corresponding increase in AI capabilities at the resource-constrained edges of the Internet. Meanwhile, the resource requirements of digital computing and deep learning are growing exponentially, in an unsustainable manner. One possible way to bridge this gap is the adoption of resource-efficient brain-inspired "neuromorphic" processing and sensing devices, which use event-driven, asynchronous, dynamic neurosynaptic elements with colocated memory for distributed processing and machine learning. However, since neuromorphic systems are fundamentally different from conventional von Neumann computers and clock-driven sensor systems, several challenges are posed to large-scale adoption and integration of neuromorphic devices into the existing distributed digital-computational infrastructure. Here, we describe the current landscape of neuromorphic computing, focusing on characteristics that pose integration challenges. Based on this analysis, we propose a microservice-based conceptual framework for neuromorphic systems integration, consisting of a neuromorphic-system proxy, which would provide virtualization and communication capabilities required in distributed systems of systems, in combination with a declarative programming approach offering engineering-process abstraction. We also present concepts that could serve as a basis for the realization of this framework, and identify directions for further research required to enable large-scale system integration of neuromorphic devices.

A Survey on Graph Neural Networks for Microservice-Based Cloud Applications

Graph neural networks (GNNs) have achieved great success in many research areas ranging from traffic to computer vision. With increased interest in cloud-native applications, GNNs are increasingly being investigated to address various challenges in microservice architecture from prototype design to large-scale service deployment. To appreciate the big picture of this emerging trend, we provide a comprehensive review of recent studies leveraging GNNs for microservice-based applications. To begin, we identify the key areas in which GNNs are applied, and then we review in detail how GNNs can be designed to address the challenges in specific areas found in the literature. Finally, we outline potential research directions where GNN-based solutions can be further applied. Our research shows the popularity of leveraging convolutional graph neural networks (ConGNNs) for microservice-based applications in the current design of cloud systems and the emerging area of adopting spatio-temporal graph neural networks (STGNNs) and dynamic graph neural networks (DGNNs) for more advanced studies.

Comparison of Representative Microservices Technologies in Terms of Performance for Use for Projects Based on Sensor Networks

Reading and analyzing data from sensors are crucial in many areas of life. IoT concepts and related issues are becoming more and more popular, but before we can process data and draw conclusions, we need to think about how to design an application. The most popular solutions today are microservices and monolithic architecture. In addition to this choice, there is also the question of the technology in which you will work. There are more and more of them on the market and in each of them it is practically possible to achieve similar results, but the difference lies in how quickly it will be possible and whether the approach invented will turn out to be the most optimal. Making the right decisions at the beginning of application development can determine its path to success or failure. The main goal of this article was to compare technologies used in applications based on microservice architecture. The preparation of a book lending system, whose server part was implemented in three different versions, each using a different type of technology, helped to achieve this goal. The compared solutions were: Spring Boot, Micronaut and Quarkus. The reason for this research was to investigate projects using sensor networks, ranging from telemedicine applications to extensive sensor networks collecting scientific data, or working in an environment with limited resources, e.g., with BLE or WIFI transmitters, where it is critical to supply energy to these transmitters. Therefore, the issue of efficiency and hence energy savings may be a key issue depending on the selected programming technology.

Using IoT Assistive Technologies for Older People Non-Invasive Monitoring and Living Support in Their Homes

Many western societies are confronted with issues in planning and adapting their health policies due to an ageing population living alone. The “NOt Alone at Home—NOAH” project aimed to involve older people in the Agile co-creation of services for a collaborative monitoring and awareness notification for remote caregivers. Our research aim was to create a scalable and modern information system that permitted a non-invasive monitorization of the users for keeping their caregivers up to date. This was done via a cloud IoT (Internet of Things), which collects and processes data from its domotic sensors. The notifications generated by the system, via the three applications we developed (NOAH/NOAH Care/Admin Centre), offer caregivers an easy way of detecting changes in the day-to-day behaviour and activities of their patients, giving them time to intervene in case of abnormal activity. Such an approach would lead to a longer and more independent life for the older people. We evaluated our system by conducting a year-long pilot-study, offering caregivers constant information from the end-users while still living independently. For creating our pilot groups, we used the ABAS (Adaptive Behaviour Assessment System) II, which we then matched with the pre-profiled Behavioral Analysis Models of older people familiar with modern communication devices. Our results showed a low association between daily skills and the sensors we used, in contrast with the results from previous studies done in this field. Another result was efficiently capturing the behaviour changes that took place due to the COVID-19 Lockdown measures.

Data Fusion of Observability Signals for Assisting Orchestration of Distributed Applications

Nowadays, various frameworks are emerging for supporting distributed tracing techniques over microservices-based distributed applications. The objective is to improve observability and management of operational problems of distributed applications, considering bottlenecks in terms of high latencies in the interaction among the deployed microservices. However, such frameworks provide information that is disjoint from the management information that is usually collected by cloud computing orchestration platforms. There is a need to improve observability by combining such information to easily produce insights related to performance issues and to realize root cause analyses to tackle them. In this paper, we provide a modern observability approach and pilot implementation for tackling data fusion aspects in edge and cloud computing orchestration platforms. We consider the integration of signals made available by various open-source monitoring and observability frameworks, including metrics, logs and distributed tracing mechanisms. The approach is validated in an experimental orchestration environment based on the deployment and stress testing of a proof-of-concept microservices-based application. Helpful results are produced regarding the identification of the main causes of latencies in the various application parts and the better understanding of the behavior of the application under different stressing conditions.

A New Digital Platform for Collecting Measurement Data from the Novel Imaging Sensors in Urology

The use of UT and EIT technologies gives the opportunity to develop new, effective, minimally invasive diagnostic methods for urology. The introduction of new diagnostic methods into medicine requires the development of new tools for collecting, processing and analysing the data obtained from them. Such system might be seen as a part of the electronic health record EHR system. The digital medical data management platform must provide the infrastructure that will make medical activity possible and effective in the presented scope. The solution presented in this article was implemented using the newest computer technologies to obtain advantages such as mobility, versatility, flexibility and scalability. The architecture of the developed platform, technological stack proposals, database structure and user interface are presented. In the course of this study, an analysis of known and available standards such as Hl7, RIM, DICOM, and tools for collecting medical data was performed, and the results obtained using them are also presented. The developed digital platform also falls into an innovative path of creating a network of sensors communicating with each other in the digital space, resulting in the implementation of the IoT (Internet of Things) vision. The issues of building software based on the architecture of microservices were discussed emphasizing the role of message brokers. The selected message brokers were also analysed in terms of available features and message transmission time.

Building Low-Cost Sensing Infrastructure for Air Quality Monitoring in Urban Areas Based on Fog Computing

Because the number of air quality measurement stations governed by a public authority is limited, many methodologies have been developed in order to integrate low-cost sensors and to improve the spatial density of air quality measurements. However, at the large-scale level, the integration of a huge number of sensors brings many challenges. The volume, velocity and processing requirements regarding the management of the sensor life cycle and the operation of system services overcome the capabilities of the centralized cloud model. In this paper, we present the methodology and the architectural framework for building large-scale sensing infrastructure for air quality monitoring applicable in urban scenarios. The proposed tiered architectural solution based on the adopted fog computing model is capable of handling the processing requirements of a large-scale application, while at the same time sustaining real-time performance. Furthermore, the proposed methodology introduces the collection of methods for the management of edge-tier node operation through different phases of the node life cycle, including the methods for node commission, provision, fault detection and recovery. The related sensor-side processing is encapsulated in the form of microservices that reside on the different tiers of system architecture. The operation of system microservices and their collaboration was verified through the presented experimental case study.

Recommender-as-a-Service with Chatbot Guided Domain-science Knowledge Discovery in a Science Gateway

Scientists in disciplines such as neuroscience and bioinformatics are increasingly relying on science gateways for experimentation on voluminous data, as well as analysis and visualization in multiple perspectives. Though current science gateways provide easy access to computing resources, datasets and tools specific to the disciplines, scientists often use slow and tedious manual efforts to perform knowledge discovery to accomplish their research/education tasks. Recommender systems can provide expert guidance and can help them to navigate and discover relevant publications, tools, data sets, or even automate cloud resource configurations suitable for a given scientific task. To realize the potential of integration of recommenders in science gateways in order to spur research productivity, we present a novel "OnTimeRecommend" recommender system. The OnTimeRecommend comprises of several integrated recommender modules implemented as microservices that can be augmented to a science gateway in the form of a recommender-as-a-service. The guidance for use of the recommender modules in a science gateway is aided by a chatbot plug-in viz., Vidura Advisor. To validate our OnTimeRecommend, we integrate and show benefits for both novice and expert users in domain-specific knowledge discovery within two exemplar science gateways, one in neuroscience (CyNeuro) and the other in bioinformatics (KBCommons).

A WoT-Based Method for Creating Digital Sentinel Twins of IoT Devices

The data produced by sensors of IoT devices are becoming keystones for organizations to conduct critical decision-making processes. However, delivering information to these processes in real-time represents two challenges for the organizations: the first one is achieving a constant dataflow from IoT to the cloud and the second one is enabling decision-making processes to retrieve data from dataflows in real-time. This paper presents a cloud-based Web of Things method for creating digital twins of IoT devices (named sentinels).The novelty of the proposed approach is that sentinels create an abstract window for decision-making processes to: (a) find data (e.g., properties, events, and data from sensors of IoT devices) or (b) invoke functions (e.g., actions and tasks) from physical devices (PD), as well as from virtual devices (VD). In this approach, the applications and services of decision-making processes deal with sentinels instead of managing complex details associated with the PDs, VDs, and cloud computing infrastructures. A prototype based on the proposed method was implemented to conduct a case study based on a blockchain system for verifying contract violation in sensors used in product transportation logistics. The evaluation showed the effectiveness of sentinels enabling organizations to attain data from IoT sensors and the dataflows used by decision-making processes to convert these data into useful information.

Using Computable Phenotypes in Point-of-Care Clinical Trial Recruitment

A key challenge in point-of-care clinical trial recruitment is to autonomously identify eligible patients on presentation. Similarly, the aim of computable phenotyping is to identify those individuals within a population that exhibit a certain condition. This synergy creates an opportunity to leverage phenotypes in identifying eligible patients for clinical trials. To investigate the feasibility of this approach, we use the Transform clinical trial platform and replace its archetype-based eligibility criteria mechanism with a computable phenotype execution microservice. Utilising a phenotype for acute otitis media with discharge (AOMd) created with the Phenoflow platform, we compare the performance of Transform with and without the use of phenotype-based eligibility criteria when recruiting AOMd patients. The parameters of the trial simulated are based on those of the REST clinical trial, conducted in UK primary care.

Explora: Interactive Querying of Multidimensional Data in the Context of Smart Cities

Citizen engagement is one of the key factors for smart city initiatives to remain sustainable over time. This in turn entails providing citizens and other relevant stakeholders with the latest data and tools that enable them to derive insights that add value to their day-to-day life. The massive volume of data being constantly produced in these smart city environments makes satisfying this requirement particularly challenging. This paper introduces Explora, a generic framework for serving interactive low-latency requests, typical of visual exploratory applications on spatiotemporal data, which leverages the stream processing for deriving—on ingestion time—synopsis data structures that concisely capture the spatial and temporal trends and dynamics of the sensed variables and serve as compacted data sets to provide fast (approximate) answers to visual queries on smart city data. The experimental evaluation conducted on proof-of-concept implementations of Explora, based on traditional database and distributed data processing setups, accounts for a decrease of up to 2 orders of magnitude in query latency compared to queries running on the base raw data at the expense of less than 10% query accuracy and 30% data footprint. The implementation of the framework on real smart city data along with the obtained experimental results prove the feasibility of the proposed approach.

An IoT Platform Based on Microservices and Serverless Paradigms for Smart Farming Purposes

Nowadays, the concept of “Everything is connected to Everything” has spread to reach increasingly diverse scenarios, due to the benefits of constantly being able to know, in real-time, the status of your factory, your city, your health or your smallholding. This wide variety of scenarios creates different challenges such as the heterogeneity of IoT devices, support for large numbers of connected devices, reliable and safe systems, energy efficiency and the possibility of using this system by third-parties in other scenarios. A transversal middleware in all IoT solutions is called an IoT platform. the IoT platform is a piece of software that works like a kind of “glue” to combine platforms and orchestrate capabilities that connect devices, users and applications/services in a “cyber-physical” world. In this way, the IoT platform can help solve the challenges listed above. This paper proposes an IoT agnostic architecture, highlighting the role of the IoT platform, within a broader ecosystem of interconnected tools, aiming at increasing scalability, stability, interoperability and reusability. For that purpose, different paradigms of computing will be used, such as microservices architecture and serverless computing. Additionally, a technological proposal of the architecture, called SEnviro Connect, is presented. This proposal is validated in the IoT scenario of smart farming, where five IoT devices (SEnviro nodes) have been deployed to improve wine production. A comprehensive performance evaluation is carried out to guarantee a scalable and stable platform.

Challenges and Opportunities

Docker containers are the lightweight-virtualization technology prevailing today for the provision of microservices. This work raises and discusses two main challenges in Docker containers' scheduling in cloud-fog-internet of things (IoT) networks. First, the convenience to integrate intelligent containers' schedulers based on soft-computing in the dominant open-source containers' management platforms: Docker Swarm, Google Kubernetes and Apache Mesos. Secondly, the need for specific intelligent containers' schedulers for the different interfaces in cloud-fog-IoT networks: cloud-to-fog, fog-to-IoT and cloud-to-fog. The goal of this work is to support the optimal allocation of microservices provided by the main cloud service providers today and used by millions of users worldwide in applications such as smart health, content delivery networks, smart health, etc. Particularly, the improvement is studied in terms of quality of service (QoS) parameters such as latency, load balance, energy consumption and runtime, based on the analysis of previous works and implementations. Moreover, the scientific-technical impact of smart containers' scheduling in the market is also discussed, showing the possible repercussion of the raised opportunities in the research line.

DNS/DANE Collision-Based Distributed and Dynamic Authentication for Microservices in IoT †

IoT devices provide real-time data to a rich ecosystem of services and applications. The volume of data and the involved subscribe/notify signaling will likely become a challenge also for access and core networks. To alleviate the core of the network, other technologies like fog computing can be used. On the security side, designers of IoT low-cost devices and applications often reuse old versions of development frameworks and software components that contain vulnerabilities. Many server applications today are designed using microservice architectures where components are easier to update. Thus, IoT can benefit from deploying microservices in the fog as it offers the required flexibility for the main players of ubiquitous computing: nomadic users. In such deployments, IoT devices need the dynamic instantiation of microservices. IoT microservices require certificates so they can be accessed securely. Thus, every microservice instance may require a newly-created domain name and a certificate. The DNS-based Authentication of Named Entities (DANE) extension to Domain Name System Security Extensions (DNSSEC) allows linking a certificate to a given domain name. Thus, the combination of DNSSEC and DANE provides microservices' clients with secure information regarding the domain name, IP address, and server certificate of a given microservice. However, IoT microservices may be short-lived since devices can move from one local fog to another, forcing DNSSEC servers to sign zones whenever new changes occur. Considering DNSSEC and DANE were designed to cope with static services, coping with IoT dynamic microservice instantiation can throttle the scalability in the fog. To overcome this limitation, this article proposes a solution that modifies the DNSSEC/DANE signature mechanism using chameleon signatures and defining a new soft delegation scheme. Chameleon signatures are signatures computed over a chameleon hash, which have a property: a secret trapdoor function can be used to compute collisions to the hash. Since the hash is maintained, the signature does not have to be computed again. In the soft delegation schema, DNS servers obtain a trapdoor that allows performing changes in a constrained zone without affecting normal DNS operation. In this way, a server can receive this soft delegation and modify the DNS zone to cope with frequent changes such as microservice dynamic instantiation. Changes in the soft delegated zone are much faster and do not require the intervention of the DNS primary servers of the zone.

SmartHerd management: A microservices-based fog computing-assisted IoT platform towards data-driven smart dairy farming

Internet of Things (IoT), fog computing, cloud computing, and data-driven techniques together offer a great opportunity for verticals such as dairy industry to increase productivity by getting actionable insights to improve farming practices, thereby increasing efficiency and yield. In this paper, we present SmartHerd, a fog computing-assisted end-to-end IoT platform for animal behavior analysis and health monitoring in a dairy farming scenario. The platform follows a microservices-oriented design to assist the distributed computing paradigm and addresses the major issue of constrained Internet connectivity in remote farm locations. We present the implementation of the designed software system in a 6-month mature real-world deployment, wherein the data from wearables on cows is sent to a fog-based platform for data classification and analysis, which includes decision-making capabilities and provides actionable insights to farmer towards the welfare of animals. With fog-based computational assistance in the SmartHerd setup, we see an 84% reduction in amount of data transferred to the cloud as compared with the conventional cloud-based approach.

SatEC: A 5G Satellite Edge Computing Framework Based on Microservice Architecture

As outlined in the 3Gpp Release 16, 5G satellite access is important for 5G network development in the future. A terrestrial-satellite network integrated with 5G has the characteristics of low delay, high bandwidth, and ubiquitous coverage. A few researchers have proposed integrated schemes for such a network; however, these schemes do not consider the possibility of achieving optimization of the delay characteristic by changing the computing mode of the 5G satellite network. We propose a 5G satellite edge computing framework (5GsatEC), which aims to reduce delay and expand network coverage. This framework consists of embedded hardware platforms and edge computing microservices in satellites. To increase the flexibility of the framework in complex scenarios, we unify the resource management of the central processing unit (CPU), graphics processing unit (GPU), and field-programmable gate array (FPGA); we divide the services into three types: system services, basic services, and user services. In order to verify the performance of the framework, we carried out a series of experiments. The results show that 5GsatEC has a broader coverage than the ground 5G network. The results also show that 5GsatEC has lower delay, a lower packet loss rate, and lower bandwidth consumption than the 5G satellite network.

Design Methodology of Microservices to Support Predictive Analytics for IoT Applications

In the era of digital transformation, the Internet of Things (IoT) is emerging with improved data collection methods, advanced data processing mechanisms, enhanced analytic techniques, and modern service platforms. However, one of the major challenges is to provide an integrated design that can provide analytic capability for heterogeneous types of data and support the IoT applications with modular and robust services in an environment where the requirements keep changing. An enhanced analytic functionality not only provides insights from IoT data, but also fosters productivity of processes. Developing an efficient and easily maintainable IoT analytic system is a challenging endeavor due to many reasons such as heterogeneous data sources, growing data volumes, and monolithic service development approaches. In this view, the article proposes a design methodology that presents analytic capabilities embedded in modular microservices to realize efficient and scalable services in order to support adaptive IoT applications. Algorithms for analytic procedures are developed to underpin the model. We implement the Web Objects to virtualize IoT resources. The semantic data modeling is used to promote interoperability across the heterogeneous systems. We demonstrate the use case scenario and validate the proposed design with a prototype implementation.

Norwegian e-Infrastructure for Life Sciences (NeLS)

The Norwegian e-Infrastructure for Life Sciences (NeLS) has been developed by ELIXIR Norway to provide its users with a system enabling data storage, sharing, and analysis in a project-oriented fashion. The system is available through easy-to-use web interfaces, including the Galaxy workbench for data analysis and workflow execution. Users confident with a command-line interface and programming may also access it through Secure Shell (SSH) and application programming interfaces (APIs).  NeLS has been in production since 2015, with training and support provided by the help desk of ELIXIR Norway. Through collaboration with NorSeq, the national consortium for high-throughput sequencing, an integrated service is offered so that sequencing data generated in a research project is provided to the involved researchers through NeLS. Sensitive data, such as individual genomic sequencing data, are handled using the TSD (Services for Sensitive Data) platform provided by Sigma2 and the University of Oslo. NeLS integrates national e-infrastructure storage and computing resources, and is also integrated with the SEEK platform in order to store large data files produced by experiments described in SEEK.   In this article, we outline the architecture of NeLS and discuss possible directions for further development.

The growing need for microservices in bioinformatics

OBJECTIVE

Within the information technology (IT) industry, best practices and standards are constantly evolving and being refined. In contrast, computer technology utilized within the healthcare industry often evolves at a glacial pace, with reduced opportunities for justified innovation. Although the use of timely technology refreshes within an enterprise's overall technology stack can be costly, thoughtful adoption of select technologies with a demonstrated return on investment can be very effective in increasing productivity and at the same time, reducing the burden of maintenance often associated with older and legacy systems. In this brief technical communication, we introduce the concept of microservices as applied to the ecosystem of data analysis pipelines. Microservice architecture is a framework for dividing complex systems into easily managed parts. Each individual service is limited in functional scope, thereby conferring a higher measure of functional isolation and reliability to the collective solution. Moreover, maintenance challenges are greatly simplified by virtue of the reduced architectural complexity of each constitutive module. This fact notwithstanding, rendered overall solutions utilizing a microservices-based approach provide equal or greater levels of functionality as compared to conventional programming approaches. Bioinformatics, with its ever-increasing demand for performance and new testing algorithms, is the perfect use-case for such a solution. Moreover, if promulgated within the greater development community as an open-source solution, such an approach holds potential to be transformative to current bioinformatics software development.

CONTEXT

Bioinformatics relies on nimble IT framework which can adapt to changing requirements.

AIMS

To present a well-established software design and deployment strategy as a solution for current challenges within bioinformatics.

CONCLUSIONS

Use of the microservices framework is an effective methodology for the fabrication and implementation of reliable and innovative software, made possible in a highly collaborative setting.