2,099 research outputs found
Engineering and Experimentally Benchmarking a Container-based Edge Computing System
While edge computing is envisioned to superbly serve latency sensitive
applications, the implementation-based studies benchmarking its performance are
few and far between. To address this gap, we engineer a modular edge cloud
computing system architecture that is built on latest advances in
containerization techniques, including Kafka, for data streaming, Docker, as
application platform, and Firebase Cloud, as realtime database system. We
benchmark the performance of the system in terms of scalability, resource
utilization and latency by comparing three scenarios: cloud-only, edge-only and
combined edge-cloud. The measurements show that edge-only solution outperforms
other scenarios only when deployed with data located at one edge only, i.e.,
without edge computing wide data synchronization. In case of applications
requiring data synchronization through the cloud, edge-cloud scales around a
factor 10 times better than cloud-only, until certain number of concurrent
users in the system, and above this point, cloud-only scales better. In terms
of resource utilization, we observe that whereas the mean utilization increases
linearly with the number of user requests, the maximum values for the memory
and the network I/O heavily increase when with an increasing amount of data
ENORM: A Framework For Edge NOde Resource Management
Current computing techniques using the cloud as a centralised server will
become untenable as billions of devices get connected to the Internet. This
raises the need for fog computing, which leverages computing at the edge of the
network on nodes, such as routers, base stations and switches, along with the
cloud. However, to realise fog computing the challenge of managing edge nodes
will need to be addressed. This paper is motivated to address the resource
management challenge. We develop the first framework to manage edge nodes,
namely the Edge NOde Resource Management (ENORM) framework. Mechanisms for
provisioning and auto-scaling edge node resources are proposed. The feasibility
of the framework is demonstrated on a PokeMon Go-like online game use-case. The
benefits of using ENORM are observed by reduced application latency between 20%
- 80% and reduced data transfer and communication frequency between the edge
node and the cloud by up to 95\%. These results highlight the potential of fog
computing for improving the quality of service and experience.Comment: 14 pages; accepted to IEEE Transactions on Services Computing on 12
September 201
Scaling Performance of Serverless Edge Networking
When clustering devices at the edge, inter-node latency poses a significant
challenge that directly impacts the application performance. In this paper, we
experimentally examine the impact that inter-node latency has on application
performance by measuring the throughput of an distributed serverless
application in a real world testbed. We deploy Knative over a Kubernetes
cluster of nodes and emulate networking delay between them to compare the
performance of applications when deployed over a single-site versus multiple
distributed computing sites. The results show that multi-site edge networks
achieve half the throughput compared to a deployment hosted at a single site
under low processing times conditions, whereas the throughput performance
significantly improves otherwise
Benchmarking in a rotating annulus: a comparative experimental and numerical study of baroclinic wave dynamics
The differentially heated rotating annulus is a widely studied tabletop-size
laboratory model of the general mid-latitude atmospheric circulation. The two
most relevant factors of cyclogenesis, namely rotation and meridional
temperature gradient are quite well captured in this simple arrangement. The
radial temperature difference in the cylindrical tank and its rotation rate can
be set so that the isothermal surfaces in the bulk tilt, leading to the
formation of baroclinic waves. The signatures of these waves at the free water
surface have been analyzed via infrared thermography in a wide range of
rotation rates (keeping the radial temperature difference constant) and under
different initial conditions. In parallel to the laboratory experiments, five
groups of the MetStr\"om collaboration have conducted numerical simulations in
the same parameter regime using different approaches and solvers, and applying
different initial conditions and perturbations. The experimentally and
numerically obtained baroclinic wave patterns have been evaluated and compared
in terms of their dominant wave modes, spatio-temporal variance properties and
drift rates. Thus certain ``benchmarks'' have been created that can later be
used as test cases for atmospheric numerical model validation
Application Driven MOdels for Resource Management in Cloud Environments
El despliegue y la ejecución de aplicaciones de gran escala en sistemas distribuidos con unos parametros de Calidad de Servicio adecuados necesita gestionar de manera eficiente los recursos computacionales. Para desacoplar los requirimientos funcionales y los no funcionales (u operacionales) de dichas aplicaciones, se puede distinguir dos niveles de abstracción: i) el nivel funcional, que contempla aquellos requerimientos relacionados con funcionalidades de la aplicación; y ii) el nivel operacional, que depende del sistema distribuido donde se despliegue y garantizará aquellos parámetros relacionados con la Calidad del Servicio, disponibilidad, tolerancia a fallos y coste económico, entre otros. De entre las diferentes alternativas del nivel operacional, en la presente tesis se contempla un entorno cloud basado en la virtualización de contenedores, como puede ofrecer Kubernetes.El uso de modelos para el diseño de aplicaciones en ambos niveles permite garantizar que dichos requerimientos sean satisfechos. Según la complejidad del modelo que describa la aplicación, o el conocimiento que el nivel operacional tenga de ella, se diferencian tres tipos de aplicaciones: i) aplicaciones dirigidas por el modelo, como es el caso de la simulación de eventos discretos, donde el propio modelo, por ejemplo Redes de Petri de Alto Nivel, describen la aplicación; ii) aplicaciones dirigidas por los datos, como es el caso de la ejecución de analíticas sobre Data Stream; y iii) aplicaciones dirigidas por el sistema, donde el nivel operacional rige el despliegue al considerarlas como una caja negra.En la presente tesis doctoral, se propone el uso de un scheduler específico para cada tipo de aplicación y modelo, con ejemplos concretos, de manera que el cliente de la infraestructura pueda utilizar información del modelo descriptivo y del modelo operacional. Esta solución permite rellenar el hueco conceptual entre ambos niveles. De esta manera, se proponen diferentes métodos y técnicas para desplegar diferentes aplicaciones: una simulación de un sistema de Vehículos Eléctricos descrita a través de Redes de Petri; procesado de algoritmos sobre un grafo que llega siguiendo el paradigma Data Stream; y el propio sistema operacional como sujeto de estudio.En este último caso de estudio, se ha analizado cómo determinados parámetros del nivel operacional (por ejemplo, la agrupación de contenedores, o la compartición de recursos entre contenedores alojados en una misma máquina) tienen un impacto en las prestaciones. Para analizar dicho impacto, se propone un modelo formal de una infrastructura operacional concreta (Kubernetes). Por último, se propone una metodología para construir índices de interferencia para caracterizar aplicaciones y estimar la degradación de prestaciones incurrida cuando dos contenedores son desplegados y ejecutados juntos. Estos índices modelan cómo los recursos del nivel operacional son usados por las applicaciones. Esto supone que el nivel operacional maneja información cercana a la aplicación y le permite tomar mejores decisiones de despliegue y distribución.<br /
Cognitive Capabilities for the CAAI in Cyber-Physical Production Systems
This paper presents the cognitive module of the cognitive architecture for
artificial intelligence (CAAI) in cyber-physical production systems (CPPS). The
goal of this architecture is to reduce the implementation effort of artificial
intelligence (AI) algorithms in CPPS. Declarative user goals and the provided
algorithm-knowledge base allow the dynamic pipeline orchestration and
configuration. A big data platform (BDP) instantiates the pipelines and
monitors the CPPS performance for further evaluation through the cognitive
module. Thus, the cognitive module is able to select feasible and robust
configurations for process pipelines in varying use cases. Furthermore, it
automatically adapts the models and algorithms based on model quality and
resource consumption. The cognitive module also instantiates additional
pipelines to test algorithms from different classes. CAAI relies on
well-defined interfaces to enable the integration of additional modules and
reduce implementation effort. Finally, an implementation based on Docker,
Kubernetes, and Kafka for the virtualization and orchestration of the
individual modules and as messaging-technology for module communication is used
to evaluate a real-world use case
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