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

End-to-End Test Coverage Metrics in Microservice Systems: An Automated Approach

Microservice architecture gains momentum by fueling systems with cloud-native benefits, scalability, and decentralized evolution. However, new challenges emerge for end-to-end (E2E) testing. Testers who see the decentralized system through the user interface might assume their tests are comprehensive, covering all middleware endpoints scattered across microservices. However, they do not have instruments to verify such assumptions. This paper introduces test coverage metrics for evaluating the extent of E2E test suite coverage for microservice endpoints. Next, it presents an automated approach to compute these metrics to provide feedback on the completeness of E2E test suites. Furthermore, a visual perspective is provided to highlight test coverage across the system's microservices to guide on gaps in test suites. We implement a proof-of-concept tool and perform a case study on a well-established system benchmark showing it can generate conclusive feedback on test suite coverage over system endpoints.Comment: This paper is accepted for publication at ESOCC 202

Towards a Deadline-Based Simulation Experimentation Framework Using Micro-Services Auto-Scaling Approach

There is growing number of research efforts in developing auto-scaling algorithms and tools for cloud resources. Traditional performance metrics such as CPU, memory and bandwidth usage for scaling up or down resources are not sufficient for all applications. For example, modeling and simulation experimentation is usually expected to yield results within a specific timeframe. In order to achieve this, often the quality of experiments is compromised either by restricting the parameter space to be explored or by limiting the number of replications required to give statistical confidence. In this paper, we present early stages of a deadline-based simulation experimentation framework using a micro-services auto-scaling approach. A case study of an agent-based simulation of a population physical activity behavior is used to demonstrate our framework

Tiarrah Computing: The Next Generation of Computing

The evolution of Internet of Things (IoT) brought about several challenges for the existing Hardware, Network and Application development. Some of these are handling real-time streaming and batch bigdata, real- time event handling, dynamic cluster resource allocation for computation, Wired and Wireless Network of Things etc. In order to combat these technicalities, many new technologies and strategies are being developed. Tiarrah Computing comes up with integration the concept of Cloud Computing, Fog Computing and Edge Computing. The main objectives of Tiarrah Computing are to decouple application deployment and achieve High Performance, Flexible Application Development, High Availability, Ease of Development, Ease of Maintenances etc. Tiarrah Computing focus on using the existing opensource technologies to overcome the challenges that evolve along with IoT. This paper gives you overview of the technologies and design your application as well as elaborate how to overcome most of existing challenge

Microvision: Static analysis-based approach to visualizing microservices in augmented reality

Microservices are supporting digital transformation; however, fundamental tools and system perspectives are missing to better observe, understand, and manage these systems, their properties, and their dependencies. Microservices architecture leans toward decentralization, which yields many advantages to system operation; it, however, brings challenges to their development. Microservices lack a system-centric perspective to better cope with system evolution and quality assessment. In this work, we explore microservice-specific architecture reconstruction based on static analysis. Such reconstruction typically results in system models to visualize selected system-centric perspectives. Conventional models are limited in utility when the service cardinality is high. We consider an alternative data visualization using 3D space using augmented reality. To begin testing the feasibility of deriving such perspectives from microservice systems, we developed and implemented prototype tools for software architecture reconstruction and visualization of compared perspectives

BHiveSense: An integrated information system architecture for sustainable remote monitoring and management of apiaries based on IoT and microservices

Precision Beekeeping, a field of Precision Agriculture, is an apiary management strategy based on monitoring honeybee colonies to promote more sustainable resource usage and maximise productivity. The approach related to Precision Beekeeping is based on methodologies to mitigate the stress associated with human intervention in the colonies and the waste of resources. These goals are achieved by supporting the intervention and managing the beekeeper’s timely and appropriate action at the colony’s level. In recent years, the growth of IoT (Internetof-Things) in Precision Agriculture has spurred several proposals to contribute to the paradigm of Precision Beekeeping, built on different technical concepts and with different production costs. This work proposes and describes an information systems architecture concept named BHiveSense, based on IoT and microservices, and different artefacts to demonstrate its concept: (1) a low-cost COTS (Commercial Off-The-Shelf) hive sensing prototype, (2) a REST backend API, (3) a Web application, and (4) a Mobile application. This project delivers a solution for a more integrated and sustainable beekeeping activity. Our approach stresses that by adopting microservices and a REST architecture, it is possible to deal with long-standing problems concerning interoperability, scalability, agility, and maintenance issues, delivering an efficient beehive monitoring system.info:eu-repo/semantics/publishedVersio

TOWARDS AN EFFICIENT MULTI-CLOUD OBSERVABILITY FRAMEWORK OF CONTAINERIZED MICROSERVICES IN KUBERNETES PLATFORM

A recent trend in software development adopts the paradigm of distributed microservices architecture (MA). Kubernetes, a container-based virtualization platform, has become a de facto environment in which to run MA applications. Organizations may choose to run microservices at several cloud providers to optimize cost and satisfy security concerns. This leads to increased complexity, due to the need to observe the performance characteristics of distributed MA systems. Following a decision guidance models (DGM) approach, this research proposes a decentralized and scalable framework to monitor containerized microservices that run on same or distributed Kubernetes clusters. The framework introduces efficient techniques to gather, distribute, and analyze the observed runtime telemetry data. It offers extensible and cloud-agnostic modules that can exchange data by using a multiplexing, reactive, and non-blocking data streaming approach. An experiment to observe samples of microservices deployed across different cloud platforms was used as a method to evaluate the efficacy and usefulness of the framework. The proposed framework suggests an innovative approach to the development and operations (DevOps) practitioners to observe services across different Kubernetes platforms. It could also serve as a reference architecture for researchers to guide further design options and analysis techniques

Towards Optimization of Anomaly Detection Using Autonomous Monitors in DevOps

Continuous practices including continuous integration, continuous testing, and continuous deployment are foundations of many software development initiatives. Another very popular industrial concept, DevOps, promotes automation, collaboration, and monitoring, to even more empower development processes. The scope of this thesis is on continuous monitoring and the data collected through continuous measurement in operations as it may carry very valuable details on the health of the software system. Aim: We aim to explore and improve existing solutions for managing monitoring data in operations, instantiated in the specific industry context. Specifically, we collaborated with a Swedish company responsible for ticket management and sales in public transportation to identify challenges in the information flow from operations to development and explore approaches for improved data management inspired by state-of-the-art machine learning (ML) solutions.Research approach: Our research activities span from practice to theory and from problem to solution domain, including problem conceptualization, solution design, instantiation, and empirical validation. This complies with the main principles of the design science paradigm mainly used to frame problem-driven studies aiming to improve specific areas of practice. Results: We present identified problem instances in the case company considering the general goal of better incorporating feedback from operations to development and corresponding solution design for reducing information overflow, e.g. alert flooding, by introducing a new element, a smart filter, in the feedback loop. Therefore, we propose a simpler version of the solution design based on ML decision rules as well as a more advanced deep learning (DL) alternative. We have implemented and partially evaluated the former solution design while we present the plan for implementation and optimization of the DL version of the smart filter, as a kind of autonomous monitor. Conclusion: We propose using a smart filter to tighten and improve feedback from operations to development. The smart filter utilizes operations data to discover anomalies and timely report alerts on strange and unusual system's behavior. Full-scale implementation and empirical evaluation of the smart filter based on the DL solution will be carried out in future work