Performance Analysis of Microservices Behavior in Cloud vs Containerized Domain based on CPU Utilization

Enterprise application development is rapidly moving towards a microservices-based approach. Microservices development makes application deployment more reliable and responsive based on their architecture and the way of deployment. Still, the performance of microservices is different in all environments based on resources provided by the respective cloud and services provided in the backend such as auto-scaling, load balancer, and multiple monitoring parameters. So, it is strenuous to identify Scaling and monitoring of microservice-based applications are quick as compared to monolithic applications [1]. In this paper, we deployed microservice applications in cloud and containerized environments to analyze their CPU utilization over multiple network input requests. Monolithic applications are tightly coupled while microservices applications are loosely coupled which help the API gateway to easily interact with each service module. With reference to monitoring parameters, CPU utilization is 23 percent in cloud environment. Additionally, we deployed the equivalent microservice in a containerized environment with extended resources to minimize CPU utilization to 17 percent. Furthermore, we have shown the performance of the application with “Network IN” and “Network Out” requests

Dynamic balancing method of power distribution and consumption tasks based on state iterative prediction and resource peaking shifting

In the context of the new power system, the widespread access to massive distributed new energy sources has led to the power distribution and consumption tasks characterized by multiple time scales, wide random distribution, and large demand differences, resulting in unpredictable resource peaks in the tasks computing resource demand curve. In view of this situation, a method of forecasting and dynamic balancing of computing resource demand for power distribution and consumption tasks based on state iteration was proposed: firstly, the tasks computing resource demand model was established under the analysis of the attributes and parameter demand of the power distribution and consumption tasks scenario. Secondly, on the basis of the short-term effectiveness prediction of the traditional Markov model, the first-order difference of the state is used for data training to track the state fluctuation, and the historical state and the predicted state are used for state iteration, so as to avoid the convergence of long-term prediction. Finally, a dynamic balancing model is established according to the time-scale characteristics of cyclical and non-cyclical tasks, and the optimal configuration of load imbalance is achieved through the identification and adjustment of historical data and burst data. The simulation results show that the improved Markov model based on first-order difference and state iteration has the short-term accuracy of the traditional model and the long-term traceability of data fluctuations. The dynamic balancing model can combine the characteristics of historical data and burst data to effectively reduce the imbalance of resource demand, and show good ability to cope with resource imbalance deviation

The rockerverse : packages and applications for containerisation with R

The Rocker Project provides widely used Docker images for R across different application scenarios. This article surveys downstream projects that build upon the Rocker Project images and presents the current state of R packages for managing Docker images and controlling containers. These use cases cover diverse topics such as package development, reproducible research, collaborative work, cloud-based data processing, and production deployment of services. The variety of applications demonstrates the power of the Rocker Project specifically and containerisation in general. Across the diverse ways to use containers, we identified common themes: reproducible environments, scalability and efficiency, and portability across clouds. We conclude that the current growth and diversification of use cases is likely to continue its positive impact, but see the need for consolidating the Rockerverse ecosystem of packages, developing common practices for applications, and exploring alternative containerisation software

Resource management in a containerized cloud : status and challenges

Cloud computing heavily relies on virtualization, as with cloud computing virtual resources are typically leased to the consumer, for example as virtual machines. Efficient management of these virtual resources is of great importance, as it has a direct impact on both the scalability and the operational costs of the cloud environment. Recently, containers are gaining popularity as virtualization technology, due to the minimal overhead compared to traditional virtual machines and the offered portability. Traditional resource management strategies however are typically designed for the allocation and migration of virtual machines, so the question arises how these strategies can be adapted for the management of a containerized cloud. Apart from this, the cloud is also no longer limited to the centrally hosted data center infrastructure. New deployment models have gained maturity, such as fog and mobile edge computing, bringing the cloud closer to the end user. These models could also benefit from container technology, as the newly introduced devices often have limited hardware resources. In this survey, we provide an overview of the current state of the art regarding resource management within the broad sense of cloud computing, complementary to existing surveys in literature. We investigate how research is adapting to the recent evolutions within the cloud, being the adoption of container technology and the introduction of the fog computing conceptual model. Furthermore, we identify several challenges and possible opportunities for future research

Orchestration of Microservices for IoT Using Docker and Edge Computing

The world of connected devices has led to the rise of the Internet of Things paradigm, where applications rely on multiple devices, gathering and sharing data across highly heterogeneous networks. The variety of possible mechanisms, protocols, and hardware has become a hindrance in the development of architectures capable of addressing the most common IoT use cases, while abstracting services from the underlying communication subsystem. Moreover, the world is moving toward new strict requirements in terms of timeliness and low latency in combination with ultra-high availability and reliability. Thus, future IoT architectures will also have to support the requirements of these cyber-physical applications. In this regard, edge computing has been presented as one of the most promising solutions, relying on the cooperation of nodes by moving services directly to end devices and caching information locally. Therefore, in this article, we propose a modular and scalable architecture based on lightweight virtualization. The provided modularity, combined with the orchestration supplied by Docker, simplifies management and enables distributed deployments, creating a highly dynamic system. Moreover, characteristics such as fault tolerance and system availability are achieved by distributing the application logic across different layers, where failures of devices and micro-services can be masked by this natively redundant architecture, with minimal impact on the overall system performance. Experimental results have validated the implementation of the proposed architecture for on-demand services deployment across different architecture layers.publishe

Diseño y desarrollo de una arquitectura de Internet de las Cosas de nueva generación orientada al cálculo y predicción de índices compuestos aplicada en entornos reales

[ES] El Internet de las Cosas (IoT) ha experimentado un gran crecimiento en los últimos años. El incremento en el número de dispositivos, una mayor miniaturización de la capacidad de computación y las técnicas de virtualización, han favorecido su adopción en la industria y en otros sectores. Asimismo, la introducción de nuevas tecnologías (como la Inteligencia Artificial, el 5G, el Tactile Internet o la Realidad Aumentada) y el auge del edge computing preparan el terreno, y formulan los requisitos, para lo que se conoce como Internet de las Cosas de Nueva Generación (NGIoT). Estos avances plantean nuevos desafíos tales como el establecimiento de arquitecturas que cubran dichas necesidades y a la vez resulten flexibles, escalables y prácticas para implementar servicios que aporten valor a la sociedad. En este sentido, el IoT puede resultar un elemento clave para el establecimiento de políticas y la toma de decisiones. Una herramienta muy útil para ello es la definición y cálculo de indicadores compuestos, que representan un impacto en un fenómeno real a través de un único valor. La generación de estos indicadores es un aspecto promovido por entidades oficiales como la Unión Europea, aunque su automatización y uso en entornos de tiempo real es un campo poco explorado. Este tipo de índices deben seguir una serie de operaciones matemáticas y formalidades (normalización, ponderación, agregación¿) para ser considerados válidos. Esta tesis doctoral plantea la unión de ambos campos en alza, proponiendo una arquitectura de Internet de las Cosas de nueva generación orientada al servicio de cálculo y predicción de indicadores compuestos. Partiendo de la experiencia del candidato en proyectos de investigación europeos y regionales, y construyendo sobre tecnologías open source, se ha incluido el diseño, desarrollo e integración de los módulos de dicha arquitectura (adquisición de datos, procesamiento, visualización y seguridad) como parte de la tesis. Dichos planteamientos e implementaciones se han validado en cinco escenarios diferentes, cubriendo cinco índices compuestos en entornos con requisitos dispares siguiendo una metodología diseñada durante este trabajo. Los casos de uso están centrados en aspectos de sostenibilidad en entornos urbano y marítimo-portuario, pero se ha destacado que la solución puede ser extrapolada a otros sectores ya que ha sido diseñada de una manera agnóstica. El resultado de la tesis ha sido, además, analizado desde el punto de vista de transferencia tecnológica. Se ha propuesto la formulación de un producto, así como una posible financiación en fases de madurez más avanzadas y su potencial explotación como elemento comercializable[CA] La Internet de les Coses (IoT) ha experimentat un gran creixement en els últims anys. L'increment en el nombre de dispositius, una major miniaturització de la capacitat de computació i les tècniques de virtualització, han afavorit la seua adopció en la indústria i en altres sectors. Així mateix, la introducció de noves tecnologies (com la Intel·ligència Artificial, el 5G, la Internet Tàctil o la Realitat Augmentada) i l'auge del edge computing preparen el terreny, i formulen els requisits, per al que es coneix com a Internet de les Coses de Nova Generació (NGIoT). Aquests avanços plantegen nous desafiaments com ara l'establiment d'arquitectures que cobrisquen aquestes necessitats i resulten, alhora, flexibles, escalables i pràctiques per a implementar serveis que aporten valor a la societat. Ací, el IoT pot resultar un element clau per a l'establiment de polítiques i la presa de decisions. Una eina molt útil en aquest sentit és la definició i càlcul d'indicadors compostos, que representen un impacte en un fenomen real a través d'un únic valor. La generació d'aquests indicadors és un aspecte promogut per entitats oficials com la Unió Europea, encara que la seua automatització i ús en entorns de temps real és un camp poc explorat. Aquest tipus d'índexs han de seguir una sèrie d'operacions matemàtiques i formalitats (normalització, ponderació, agregació¿) per a ser considerats vàlids. Aquesta tesi doctoral planteja la unió de tots dos camps en alça, proposant una arquitectura d'Internet de les Coses de nova generació orientada al servei de càlcul i predicció d'indicadors compostos. Partint de l'experiència del candidat en projectes d'investigació europeus i regionals, i construint sobre tecnologies open source, s'ha inclòs el disseny, desenvolupament i integració dels mòduls d'aquesta arquitectura (adquisició de dades, processament, visualització i seguretat) com a part de la tesi. Aquests plantejaments i implementacions s'han validat en cinc escenaris diferents, cobrint cinc índexs compostos en entorns amb requisits dispars seguint una metodologia dissenyada durant aquest treball. Els casos d'ús estan centrats en aspectes de sostenibilitat en entorns urbà i marítim-portuari, però s'ha destacat que la solució pot ser extrapolada a altres sectors ja que ha sigut dissenyada d'una manera agnòstica. El resultat de la tesi ha sigut, a més, analitzat des del punt de vista de transferència tecnològica. S'ha proposat la formulació d'un producte, així com un possible finançament en fases de maduresa més avançades i la seua potencial explotació com a element comercialitzable[EN] The Internet of Things (IoT) has experienced tremendous growth in recent years. The increase in the number of devices, greater miniaturization of computing capacity and virtualization techniques have favored its adoption in industry and other sectors. Likewise, the introduction of new technologies (such as Artificial Intelligence, 5G, Tactile Internet or Augmented Reality), together with the rise of edge computing, are paving the way, and formulating the requirements, for what is known as the Next Generation Internet of Things (NGIoT). These advances pose new challenges such as the establishment of proper architectures that meet those needs and, at the same time, are flexible, scalable, and practical for implementing services that bring value to society. In this sense, IoT could be a key element for policy and decision making. A very useful tool for this is the definition and calculation of composite indicators, which represent an impact on a real phenomenon through a single value. The generation of these indicators is an aspect promoted by official entities such as the European Union, although their automation and use in real-time environments is a rather uncharted research field. This type of indexes must follow a series of mathematical operations and formalities (normalization, weighting, aggregation...) to be considered valid. This doctoral thesis proposes the union of both fields, proposing a new generation Internet of Things architecture oriented to the calculation and prediction of composite indicators. Based on the candidate's experience in European and regional research projects, and building on open source technologies, the design, development and integration of the modules of such architecture (data acquisition, processing, visualization and security) has been included as part of the thesis. These approaches and implementations have been validated in five different scenarios, covering five composite indexes in environments with disparate requirements following a methodology designed during this work. The use cases are focused on sustainability aspects in urban and maritime-port environments, but it has been highlighted that the solution can be extrapolated to other sectors as it has been designed in an agnostic way. The result of the thesis has also been analyzed from the point of view of technology transfer. A tentative product definition has been formulated, as well as a possible financing in more advanced stages of maturity and its potential exploitation as a marketable elementLacalle Úbeda, I. (2022). Diseño y desarrollo de una arquitectura de Internet de las Cosas de Nueva Generación orientada al cálculo y predicción de índices compuestos aplicada en entornos reales [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19063