INSTRUMENTATION-BASED MONITORING TECHNIQUES SURVEY ON HOST, PLATFORM, AND SERVICE LEVEL IN MICROSERVICE ARCHITECTURE

Microservice is an application architecture that separates one big application into smaller ones. The architecture simplifies development, deployment, and management process. However, the architecture is quite complex thus the monitoring process becomes much more challenging. Classifications for the instrumentations that are used in the monitoring process is needed to achieve better practicality for the administrators. We surveyed the monitoring technique classification method in microservice architecture. The method is divided into three levels. They are host level, platform level, and service level. In this paper, we present the latest instruments that are being used in the monitoring process in each level. Correlation between the goals, needs, and stakeholder is also presented

Monitoring in Hybrid Cloud-Edge Environments

The increasing number of mobile and IoT(Internet of Things) devices accessing cloud services contributes to a surge of requests towards the Cloud and consequently, higher latencies. This is aggravated by the possible congestion of the communication networks connecting the end devices and remote cloud datacenters, due to the large data volume generated at the Edge (e.g. in the domains of smart cities, smart cars, etc.). One solution for this problem is the creation of hybrid Cloud/Edge execution platforms composed of computational nodes located in the periphery of the system, near data producers and consumers, as a way to complement the cloud resources. These edge nodes offer computation and data storage resources to accommodate local services in order to ensure rapid responses to clients (enhancing the perceived quality of service) and to filter data, reducing the traffic volume towards the Cloud. Usually these nodes (e.g. ISP access points and onpremises servers) are heterogeneous, geographically distributed, and resource-restricted (including in communication networks), which increase their management’s complexity. At the application level, the microservices paradigm, represented by applications composed of small, loosely coupled services, offers an adequate and flexible solution to design applications that may explore the limited computational resources in the Edge. Nevertheless, the inherent difficult management of microservices within such complex infrastructure demands an agile and lightweight monitoring system that takes into account the Edge’s limitations, which goes behind traditional monitoring solutions at the Cloud. Monitoring in these new domains is not a simple process since it requires supporting the elasticity of the monitored system, the dynamic deployment of services and, moreover, doing so without overloading the infrastructure’s resources with its own computational requirements and generated data. Towards this goal, this dissertation presents an hybrid monitoring architecture where the heavier (resource-wise) components reside in the Cloud while the lighter (computationally less demanding) components reside in the Edge. The architecture provides relevant monitoring functionalities such as metrics’ acquisition, their analysis and mechanisms for real-time alerting. The objective is the efficient use of computational resources in the infrastructure while guaranteeing an agile delivery of monitoring data where and when it is needed.Tem-se vindo a verificar um aumento significativo de dispositivos móveis e do domínio IoT(Internet of Things) em áreas emergentes como Smart Cities, Smart Cars, etc., que fazem pedidos a serviços localizados normalmente na Cloud, muitas vezes a partir de locais remotos. Como consequência, prevê-se um aumento da latência no processamento destes pedidos, que poderá ser agravado pelo congestionamento dos canais de comunicação, da periferia até aos centros de dados. Uma forma de solucionar este problema passa pela criação de sistemas híbridos Cloud/Edge, compostos por nós computacionais que estão localizados na periferia do sistema, perto dos produtores e consumidores de dados, complementando assim os recursos computacionais da Cloud. Os nós da Edge permitem não só alojar dados e computações, garantindo uma resposta mais rápida aos clientes e uma melhor qualidade do serviço, como também permitem filtrar alguns dos dados, evitando deste modo transferências de dados desnecessárias para o núcleo do sistema. Contudo, muitos destes nós (e.g. pontos de acesso, servidores proprietários) têm uma capacidade limitada, são bastante heterogéneos e/ou encontram-se espalhados geograficamente, o que dificulta a gestão dos recursos. O paradigma de micro-serviços, representado por aplicações compostas por serviços de reduzida dimensão, desacoplados na sua funcionalidade e que comunicam por mensagens, fornece uma solução adequada para explorar os recursos computacionais na periferia. No entanto, o mapeamento adequado dos micro-serviços na infra-estrutura, além de ser complexo, é difícil de gerir e requer um sistema de monitorização ligeiro e ágil, que considere as capacidades limitadas da infra-estrutura de suporte na periferia. A monitorização não é um processo simples pois deve possibilitar a elasticidade do sistema, tendo em conta as adaptações de "deployment", e sem sobrecarregar os recursos computacionais ou de rede. Este trabalho apresenta uma arquitectura de monitorização híbrida, com componentes de maior complexidade na Cloud e componentes mais simples na Edge. A arquitectura fornece funcionalidades importantes de monitorização, como a recolha de métricas variadas, a sua análise e alertas em tempo real. O objetivo é rentabilizar os recursos computacionais garantindo a entrega dos dados mais relevantes quando necessário

Optimierung der Visualisierung eines Dashboards für das Microservice-Monitoring

Microservice-Architekturen haben sich mittlerweile etabliert und werden von immer mehr Firmen übernommen. Die erhöhte Komplexität der Microservice-Architektur aufgrund der Verteilung des Systems hat jedoch zur Folge, dass die effiziente und erfolgreiche Administration des Systems erschwert wird. Ziel der Arbeit ist es, alle notwendigen Metriken für ein effizientes und erfolgreiches Microservice-Monitoring zu identifizieren und auf Basis dieser Erkenntnisse das Linkerd-Dashboard prototypisch weiterzuentwickeln. Hierfür wurde eine Literaturrecherche durchgeführt. Darüber hinaus wurden Praktiker mittels eines Online-Fragebogens befragt. Abschließend wurde die prototypische Weiterentwicklung mithilfe eines halbstrukturierten Interviews evaluiert. Die Literaturrecherche ergab, dass Central-Processing-Unit (CPU)- und Random-Access-Memory (RAM)-Nutzung, Antwortzeit, Workload, Fehlerrate und Service-Interaktion eine Metrik-Menge sind, mit der Microservice-Architekturen effektiv überwacht werden können. Außerdem konnte konstatiert werden, dass die Darstellung der Metriken hauptsächlich mit Visualisierungen realisiert wird. CPU- und RAM-Auslastung sind eine sinnvolle Erweiterung des Linkerd-Dashboards, da diese in der Literatur sowie im Fragebogen als wichtige Kennzahlen deklariert und alle anderen als essenziell eingestuften Metriken bereits vom Linkerd-Dashboard abgedeckt werden. Der Prototyp wurde als gelungen eingestuft, benötigt aber einige kleinere Verbesserungen der Visualisierung, bevor er in der Produktion eingesetzt werden kann

A Cloud-based On-line Disaggregation Algorithm for Home Appliance Loads

In this work, we address the problem of providing fast and on-line households appliance load detection in a non-intrusive way from aggregate electric energy consumption data. Enabling on-line load detection is a relevant research problem as it can unlock new grid services such as demand-side management and raises interactivity in energy awareness possibly leading to more green behaviours. To this purpose, we propose an On-line-NILM (Non-Intrusive Load Monitoring) machine learning algorithm combining two methodologies: i) Unsupervised event-based profiling and ii) Markov chain appliance load modelling. The event-based part performs event detection through contiguous and transient data segments, events clustering and matching. The resulting features are used to build household-specific appliance models from generic appliance models. Disaggregation is then performed on-line using an Additive Factorial Hidden Markov Model from the generated appliance model parameters. Our solution is implemented on the cloud and tested with public benchmark datasets. Accuracy results are presented and compared with literature solutions, showing that the proposed solution achieves on-line detection with comparable detection performance with respect to non on-line approaches

A Black-box Approach for Containerized Microservice Monitoring in Fog Computing

The goal of the Internet of Things (IoT) is to convert the physical world into a smart space in which physical objects, called things, are equipped with computing and communication capabilities. Those things can connect with anything, anyone at any time, any space via any network or service. The predominant Internet of Things (IoT) system model today is cloud centric. This model introduces latencies into the application execution, as data travels first upstream for processing and secondly the results, i.e., control commands, travel downstream to the devices. In contrast with the cloud-model, the cloud-fog-based model pushes computing capability to the edge of the network, which is closer to the data sources. This enables lower latency and a faster response time. The end-device can directly receive the service from the fog node instead of sending all the data to the central cloud server. In addition, with the application of microservice containerization technology, fog nodes can quickly set up various environments for heterogeneous services. Compared with cloud computing, fog computing needs to consider users’ mobility and geographic location. The application scenarios that fog computing is more dynamic and flexible. Therefore, fog computing requires real-time data monitoring and service management. In this thesis, we will explore how to deploy fog computing resources, what data is needed in the deployment process, and how to implement data monitoring

Performance Observability and Monitoring of High Performance Computing with Microservices

Traditionally, High Performance Computing (HPC) softwarehas been built and deployed as bulk-synchronous, parallel executables based on the message-passing interface (MPI) programming model. The rise of data-oriented computing paradigms and an explosion in the variety of applications that need to be supported on HPC platforms have forced a re-think of the appropriate programming and execution models to integrate this new functionality. In situ workflows demarcate a paradigm shift in HPC software development methodologies enabling a range of new applications --- from user-level data services to machine learning (ML) workflows that run alongside traditional scientific simulations. By tracing the evolution of HPC software developmentover the past 30 years, this dissertation identifies the key elements and trends responsible for the emergence of coupled, distributed, in situ workflows. This dissertation's focus is on coupled in situ workflows involving composable, high-performance microservices. After outlining the motivation to enable performance observability of these services and why existing HPC performance tools and techniques can not be applied in this context, this dissertation proposes a solution wherein a set of techniques gathers, analyzes, and orients performance data from different sources to generate observability. By leveraging microservice components initially designed to build high performance data services, this dissertation demonstrates their broader applicability for building and deploying performance monitoring and visualization as services within an in situ workflow. The results from this dissertation suggest that: (1) integration of performance data from different sources is vital to understanding the performance of service components, (2) the in situ (online) analysis of this performance data is needed to enable the adaptivity of distributed components and manage monitoring data volume, (3) statistical modeling combined with performance observations can help generate better service configurations, and (4) services are a promising architecture choice for deploying in situ performance monitoring and visualization functionality. This dissertation includes previously published and co-authored material and unpublished co-authored material

Real-Time QoS Monitoring and Anomaly Detection on Microservice-based Applications in Cloud-Edge Infrastructure

Ph. D. Thesis.Microservices have emerged as a new approach for developing and deploying cloud applications that require higher levels of agility, scale, and reliability. A microservicebased cloud application architecture advocates decomposition of monolithic application components into independent software components called \microservices". As the independent microservices can be developed, deployed, and updated independently of each other, it leads to complex run-time performance monitoring and management challenges. The deployment environment for microservices in multi-cloud environments is very complex as there are numerous components running in heterogeneous environments (VM/container) and communicating frequently with each other using REST-based/REST-less APIs. In some cases, multiple components can also be executed inside a VM/container making any failure or anomaly detection very complicated. It is necessary to monitor the performance variation of all the service components to detect any reason for failure. Microservice and container architecture allows to design loose-coupled services and run them in a lightweight runtime environment for more e cient scaling. Thus, containerbased microservice deployment is now the standard model for hosting cloud applications across industries. Despite the strongest scalability characteristic of this model which opens the doors for further optimizations in both application structure and performance, such characteristic adds an additional level of complexity to monitoring application performance. Performance monitoring system can lead to severe application outages if it is not able to successfully and quickly detecting failures and localizing their causes. Machine learning-based techniques have been applied to detect anomalies in microservice-based cloud-based applications. The existing research works used di erent tracking algorithms to search the root cause if anomaly observed behaviour. However, linking the observed failures of an application with their root causes by the use of these techniques is still an open research problem. Osmotic computing is a new IoT application programming paradigm that's driven by the signi cant increase in resource capacity/capability at the network edge, along with support for data transfer protocols that enable such resources to interact more seamlessly with cloud-based services. Much of the di culty in Quality of Service (QoS) and performance monitoring of IoT applications in an osmotic computing environment is due to the massive scale and heterogeneity (IoT + edge + cloud) of computing environments. To handle monitoring and anomaly detection of microservices in cloud and edge datacenters, this thesis presents multilateral research towards monitoring and anomaly detection on microservice-based applications performance in cloud-edge infrastructure. The key contributions of this thesis are as following: • It introduces a novel system, Multi-microservices Multi-virtualization Multicloud monitoring (M3 ) that provides a holistic approach to monitor the performance of microservice-based application stacks deployed across multiple cloud data centers. • A framework forMonitoring, Anomaly Detection and Localization System (MADLS) which utilizes a simpli ed approach that depends on commonly available metrics o ering a simpli ed deployment environment for the developer. • Developing a uni ed monitoring model for cloud-edge that provides an IoT application administrator with detailed QoS information related to microservices deployed across cloud and edge datacenters.Royal Embassy of Saudi Arabia Cultural Bureau in London, government of Saudi Arabi

A tourism overcrowding sensor using multiple radio techniques detection

The motivation for this dissertation came from the touristic pressure felt in the historic neighborhoods of Lisbon. This pressure is the result of the rise in the number of touristic arrivals and the proliferation of local accommodation. To mitigate this problem the research project in which this dissertation is inserted aims to disperse the pressure felt by routing the tourists to more sustainable locations and locations that are not crowded. The goal of this dissertation is then to develop a crowding sensor to detect, in real-time, the number of persons in its vicinity by detecting how many smartphones it observes in its readings. The proposed solution aims to detect the wireless trace elements generated by the normal usage of smartphones. The technologies in which the sensor will detect devices are Wi-Fi, Bluetooth and the mobile network. For testing the results gathered by the sensor we developed a prototype that was deployed on our campus and in a museum, during an event with strong attendance. The data gathered was stored in a time-series database and a data visualization tool was used to interpret the results. The overall conclusions of this dissertation are that it is possible to build a sensor that detects nearby devices thereby allowing to detect overcrowding situations. The prototype built allows to detect crowd mobility patterns. The composition of technologies and identity unification are topics deserving future research.A motivação para a presente dissertação surgiu da pressão turística sentida nos bairros históricos de Lisboa. Esta pressão é a consequência de um crescimento do número de turistas e de uma cada vez maior utilização e proliferação do alojamento local. Para mitigar este problema o projeto de investigação em que esta dissertação está inserida pretende dispersar os turistas por locais sustentáveis e que não estejam sobrelotados. O objetivo desta dissertação é o de desenvolver um sensor que consiga detetar, em tempo real, detetar quantas pessoas estão na sua proximidade com base nos smartphones que consegue detetar. A solução proposta tem como objetivo detetar os traços gerados pela normal utilização de um smartphone. As tecnologias nas quais o sensor deteta traços de utilização são Wi-Fi, Bluetooth e a rede móvel. Para realizar os testes ao sensor, foi desenvolvido um protótipo que foi instalado no campus e num museu durante um evento de grande afluência. Os dados provenientes destes testes foram guardados numa base de dados de séries temporais e analisados usando uma ferramenta de visualização de dados. As conclusões obtidas nesta dissertação são que é possível criar um sensor capaz de detetar dispositivos na sua proximidade e detetar situações de sobrelotação/apinhamento. O protótipo contruído permite detectar padrões de mobilidade de multidões. A composição de tecnologias e a unificação de identidade são problemas que requerem investigação futura

A maturity model for DevOps

Nowadays, businesses aim to respond to customer needs at unprecedented speed. Thus, many companies are rushing to the DevOps movement. DevOps is the combination of Development and Operations and a new way of thinking in the software engineering domain. However, no common understanding of what it means has yet been achieved. Also, no adoption models or fine-grained maturity models to assist DevOps maturation and implementation were identified. Therefore, this research attempt to fill these gaps. A systematic literature review is performed to identify the determining factors contributing to the implementation of DevOps, including the main capabilities and areas with which it evolves. Then, two sets of interviews with DevOps experts were performed and their experience used to build the DevOps Maturity Model. The DevOps maturity model was then developed grounded on scientific and professional viewpoints. Once developed the Maturity Model was demonstrated in a real organisation.info:eu-repo/semantics/acceptedVersio