Monitoring of applications deployed to Infrastructure as-a-Service clouds is still an open problem. In this paper, we discuss an approach based on the complex event processing paradigm, which allows application developers to specify and monitor high-level application performance metrics. We use the case of a Web 2.0 sentiment anal ysis application to illustrate the limitations we currently experience with regard to cloud monitoring, and show how our approach allows for more expressive definitions of monitored metrics. Furthermore, we indicate how the higher-level metrics produced by our approach can be used to increase application elasticity in an existing cloud middleware.
Abstract-The cloud computing paradigm introduces new possibilities and challenges for application design and deployment. On-demand resource provisioning, as well as resource and cost elasticity, need to be considered when realizing largescale distributed applications for cloud environments. Current approaches do not sufficiently address the challenges of efficiently architecting and deploying cloud applications in a holistic manner and do not deal with the specific challenges encountered in cloud infrastructures. In this paper we introduce a methodology tackling the practical problems encountered when designing and deploying cloud applications. It enables the structured creation of cloud-native applications, addressing the complete application development lifecycle, from architectural design to concrete deployment topologies provisioned and executed on cloud infrastructure. By using iterative refinement and seamless provenance documentation of decisions made in the process, the methodology eases communication with relevant stakeholders and enables efficient design and deployment of distributed cloud applications.
Internet of Things (IoT) devices are usually considered as external dependencies that only provide data, or process and execute simple instructions. Recently, IoT devices with embedded execution environments emerged that allow practitioners to deploy and execute custom application logic on the device. This approach fundamentally changes the overall process of designing, developing, deploying, and managing IoT systems. However, these devices exhibit significant differences in available execution environments, processing, and storage capabilities. To accommodate this diversity, a structured approach is needed to uniformly and transparently deploy application components onto a large number of heterogeneous devices. This is especially important in the context of current large-scale IoT systems, such as in the smart city domain. In this paper, we present LEONORE, a service oriented infrastructure that provides elastic provisioning of application components on resource-constrained and heterogeneous edge devices in large-scale IoT deployments. LEONORE supports push-based as well as pull-based deployments and we show that our solution is able to elastically provision large numbers of devices using a testbed based on a real-world industry scenario.
SUMMARYThe Cloud computing paradigm provides the basis for a class of platforms and applications that face novel challenges related to multi-tenancy, adaptivity, and elasticity. To account for service delivery guarantees in the face of ever increasing levels of heterogeneity, scale, and dynamism, service provisioning in the Cloud has raised the demand for systematic and flexible approaches to monitoring and adaptation of applications. In this paper, we tackle this issue and present a framework for efficient runtime management of Cloud environments and distributed heterogeneous systems in general. A novel domain-specific language termed MONINA is introduced that allows to define integrated monitoring and adaptation functionality for controlling such systems. We propose a mechanism for optimal deployment of the defined control operators onto available computing resources. Deployment is based on solving a quadratic programming problem, which aims at achieving minimized reaction times, low overhead, and scalable monitoring and adaptation. The monitoring infrastructure is based on a distributed messaging middleware, providing high level of decoupling and allowing new monitoring nodes to join the system dynamically. We provide a detailed formalization of the problem domain, discuss architectural details, highlight the implementation of the developed prototype, and put our work into perspective with existing work in the field.
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