Securing Serverless Computing

Serverless applications are based on a microservices-oriented system design, often consisting of several services, each with distinct functions that are composed and orchestrated to deliver specific functionality. The architecture allows firms to build and deploy software applications without consideration towards provisioning or maintaining the underlying infrastructure. The novelty of the architecture and its inherent characteristics present new challenges for cybersecurity. We discuss the security imperatives of this emerging cloud computing software paradigm. We then identify some of the approaches and practices that can be used by organizations to mitigate security threats in the context of serverless computing

A Network Science-Based Approach for an Optimal Microservice Governance

With the introduction of microservice architecture for the development of software applications a new breed of tools platforms and development technologies emerged that enabled developers and system administrators to monitor orchestrate and deploy their containerized microservice applications more effectively and efficiently Among these vast arrays of technologies Kubernetes has become one such prominent technology widely popular due to its ability to deploy and orchestrate containerized microservices Nevertheless a common issue faced in such orchestration technologies is the employment of vast arrays of disjoint monitoring solutions that fail to portray a holistic perspective on the state of microservice deployments which in turn inhibit the creation of more optimized deployment policies In response to this issue this publication proposes the use of a network science-based approach to the creation of a microservice governance model that incorporates the use of dependency analysis load prediction centrality analysis and resilience evaluation to effectively construct a more holistic perspective on a given microservice deployment Furthermore through analysis of the factors mentioned above the research conducted then proceeds to create an optimized deployment strategy for the deployment with the aid of a developed optimization algorithm Analysis of results revealed the developed governance model aided through the utilization of the developed optimization algorithm proposed in this publication proved to be quite effective in the generation of optimized microservice deployment policie

CLOUD DEPLOYMENTS USING MICRO-SERVICES FOR DIGITAL MONETIZATION APPLICATION FOR CUSTOMERS

The market today is heading towards cloud. Market demand is growing starting with customer experience (CX) and business applications heading towards “pay-as-you-go” subscription pricing model for the software requiring a unified and personalized experience. The traditional cloud deployments take the portfolio step wise and fail to unify the portfolio in the right way. This paper proposes to develop a new breed of cloud services providing digital monetization experience to the customer, where the customer can rapidly turn their ideas into new revenue streams at web pace and web scale. The use of microservice architecture drastically reduced the build and test time for the application to minutes when compared to the on-premise deployment which is typically in hours. The debugging of the solution would not result in shutting the entire application rather only the microservice module that has issue this helps to provide service with ideally zero downtime

Formal Analysis of Load Balancing in Microservices with Scenario Calculus

Load balancing plays a crucial role in realising the benefits of microservices, especially to achieve elastic scalability and performance optimisation. However, it is different from load balancing for virtual machines, because workloads on microservices are harder to predict and the number of services in the systems is large. In this paper, we formalise load balance as an emergent property of the microservices ecosystem, and employ scenario calculus to formally analyse the impact of scheduling on service capability and scalability. We discovered that elastic round robin scheduling is highly scalable but the service capability is limited by the slowest microservice instance. In contrast, shortest waiting queue scheduling is less scalable, but the service capability is higher

Development of components for microservice based applications

[Resumen] La utilización de la computación en la Niebla está ganando terreno en el dominio de la fabricación avanzada. Este paradigma de computación distribuida habilita el análisis de los datos producidos por los activos de fabricación en los procesos industriales, acercando los servicios que suele ofrecer la Nube a los dispositivos que producen los datos, mejorando la seguridad de estos y el rendimiento de las comunicaciones. Las aplicaciones desplegadas en la Niebla suelen diseñarse como conjuntos de componentes distribuidos que aprovechan el paradigma de microservicios, generalmente encapsulados en contenedores, para adaptar su despliegue en nodos con capacidades heterogéneas. Este artículo hace uso de la Ingeniería Conducida por Modelos para proporcionar a los programadores de componentes pautas y herramientas a la hora de diseñar y desarrollar dichos componentes de aplicación, preparándolos para su posterior despliegue en forma de microservicios.[Abstract] Fog computing is getting more widespread on the advanced manufacturing domain. This distributed computing paradigm enables analysing the data produced by the manufacturing assets at the industrial processes, offering Cloud-like services closer to the devices that produce the data, improving their data security and the performance of their communications. The applications deployed on the Fog layer are usually designed as sets of distributed components that leverage the advantages of the microservice paradigm, generally encapsulated in containers, to adapt their deployment to nodes with heterogeneous capabilities. This article makes use of Model Driven Engineering to provide component programmers with tools and steps when designing and developing said application components, preparing them for their latter deployment as microservices.Ministerio de Ciencia e Innovación; RTI2018-096116-B-I00Universidad del País Vasco; PES18/4

A Microservices Architecture for Reactive and Proactive Fault Tolerance in IoT Systems

Providing fault-tolerance (FT) support to Internet of Things (IoT) systems is an open challenge, with many implementations providing static, tightly coupled FT support that does not adapt and evolve like IoT systems do. This paper proposes a pluggable framework based on a microservices architecture that implements FT support as two complementary microservices: one that uses complex event processing for realtime FT detection, and another that uses online machine learning to detect fault patterns and pre-emptively mitigate faults before they are activated. We provide an early evaluation of how our framework can handle a real-world scenario

Reporte de experiencias industriales en el uso de contenedores con microservicios

Hoy en día, el paradigma del microservicio está siendo ampliamente adoptado por la industria para satisfacer las demandas de integración continua y entrega continua. Sin embargo, todavía hay varios problemas que podrían poner en peligro el despliegue de microservicios, tales como: implementación de principios autónomos, mayor complejidad y mayor riesgo de falla. En este contexto, realizamos una encuesta para descubrir cómo los profesionales del software manejan esos problemas. Se administraron 24 preguntas a 46 participantes de diferentes países para recopilar datos sobre la integración del paradigma de microservicios, la tecnología de contenedores y los métodos ágiles. Un análisis inicial de nuestros resultados revela varias ideas sobre el conocimiento de las herramientas, cómo se maneja la arquitectura de microservicios, cómo los métodos ágiles para ayudar a los desarrolladores de software en la transición a DevOps y qué líneas de investigación se están llevando a cabo en la integración.Nowadays, the microservice paradigm is being widely adopted by industry to cater to the demands of continuous integration and continuous delivery. However, there are still several issues that might jeopardize the deployment of microservices, such as: implementing autonomic principles, higher complexity, and increased failure risk. In this context, we surveyed to find out how software practitioners deal with those issues. Twenty-four questions were administered to 46 participants from different countries to gather data about the integration of the microservice paradigm, containers technology, and agile methods. An initial analysis of our results reveals several insights about knowledge of tools, how the microservice architecture is handled, how agile methods to aid software developers in the transition to DevOps, and what research lines are being pursued in integration.Atualmente, o paradigma de microsserviço está sendo amplamente adotado pela indústria para atender às demandas de integração contínua e entrega contínua. No entanto, ainda existem vários problemas que podem comprometer a implantação de microsserviços, como: desenvolvimento de princípios autonômicos, maior complexidade e aumento do risco de falhas. Nesse contexto, pesquisamos para descobrir como os profissionais de software lidam com esses problemas. Foram aplicadas 24 perguntas a 46 participantes de diferentes países para coletar dados sobre a integração do paradigma de microsserviço, tecnologia de contêineres e métodos ágeis. Uma análise inicial de nossos resultados revela várias ideias sobre o conhecimento de ferramentas, como a arquitetura de microsserviços é tratada, como métodos ágeis para auxiliar desenvolvedores de software na transição para o DevOps e quais linhas de pesquisa estão sendo seguidas na integração.Fil: Barbara, Dawitt. No especifíca;Fil: Morais Azevedo, Igor. No especifíca;Fil: Menezes, Pablo. No especifíca;Fil: Rodríguez, Guillermo Horacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin