700 research outputs found
SMOTEC: An Edge Computing Testbed for Adaptive Smart Mobility Experimentation
Smart mobility becomes paramount for meeting net-zero targets. However,
autonomous, self-driving and electric vehicles require more than ever before an
efficient, resilient and trustworthy computational offloading backbone that
expands throughout the edge-to-cloud continuum. Utilizing on-demand
heterogeneous computational resources for smart mobility is challenging and
often cost-ineffective. This paper introduces SMOTEC, a novel open-source
testbed for adaptive smart mobility experimentation with edge computing. SMOTEC
provides for the first time a modular end-to-end instrumentation for
prototyping and optimizing placement of intelligence services on edge devices
such as augmented reality and real-time traffic monitoring. SMOTEC supports a
plug-and-play Docker container integration of the SUMO simulator for urban
mobility, Raspberry Pi edge devices communicating via ZeroMQ and EPOS for an
AI-based decentralized load balancing across edge-to-cloud. All components are
orchestrated by the K3s lightweight Kubernetes. A proof-of-concept of
self-optimized service placements for traffic monitoring from Munich
demonstrates in practice the applicability and cost-effectiveness of SMOTEC.Comment: 6 pages and 6 figure
Evaluator services for optimised service placement in distributed heterogeneous cloud infrastructures
Optimal placement of demanding real-time interactive applications in a distributed heterogeneous cloud very quickly results in a complex tradeoff between the application constraints and resource capabilities. This requires very detailed information of the various requirements and capabilities of the applications and available resources. In this paper, we present a mathematical model for the service optimization problem and study the concept of evaluator services as a flexible and efficient solution for this complex problem. An evaluator service is a service probe that is deployed in particular runtime environments to assess the feasibility and cost-effectiveness of deploying a specific application in such environment. We discuss how this concept can be incorporated in a general framework such as the FUSION architecture and discuss the key benefits and tradeoffs for doing evaluator-based optimal service placement in widely distributed heterogeneous cloud environments
Microservices-based IoT Applications Scheduling in Edge and Fog Computing: A Taxonomy and Future Directions
Edge and Fog computing paradigms utilise distributed, heterogeneous and
resource-constrained devices at the edge of the network for efficient
deployment of latency-critical and bandwidth-hungry IoT application services.
Moreover, MicroService Architecture (MSA) is increasingly adopted to keep up
with the rapid development and deployment needs of the fast-evolving IoT
applications. Due to the fine-grained modularity of the microservices along
with their independently deployable and scalable nature, MSA exhibits great
potential in harnessing both Fog and Cloud resources to meet diverse QoS
requirements of the IoT application services, thus giving rise to novel
paradigms like Osmotic computing. However, efficient and scalable scheduling
algorithms are required to utilise the said characteristics of the MSA while
overcoming novel challenges introduced by the architecture. To this end, we
present a comprehensive taxonomy of recent literature on microservices-based
IoT applications scheduling in Edge and Fog computing environments.
Furthermore, we organise multiple taxonomies to capture the main aspects of the
scheduling problem, analyse and classify related works, identify research gaps
within each category, and discuss future research directions.Comment: 35 pages, 10 figures, submitted to ACM Computing Survey
MicroFog: A Framework for Scalable Placement of Microservices-based IoT Applications in Federated Fog Environments
MicroService Architecture (MSA) is gaining rapid popularity for developing
large-scale IoT applications for deployment within distributed and
resource-constrained Fog computing environments. As a cloud-native application
architecture, the true power of microservices comes from their loosely coupled,
independently deployable and scalable nature, enabling distributed placement
and dynamic composition across federated Fog and Cloud clusters. Thus, it is
necessary to develop novel microservice placement algorithms that utilise these
microservice characteristics to improve the performance of the applications.
However, existing Fog computing frameworks lack support for integrating such
placement policies due to their shortcomings in multiple areas, including MSA
application placement and deployment across multi-fog multi-cloud environments,
dynamic microservice composition across multiple distributed clusters,
scalability of the framework, support for deploying heterogeneous microservice
applications, etc. To this end, we design and implement MicroFog, a Fog
computing framework providing a scalable, easy-to-configure control engine that
executes placement algorithms and deploys applications across federated Fog
environments. Furthermore, MicroFog provides a sufficient abstraction over
container orchestration and dynamic microservice composition. The framework is
evaluated using multiple use cases. The results demonstrate that MicroFog is a
scalable, extensible and easy-to-configure framework that can integrate and
evaluate novel placement policies for deploying microservice-based applications
within multi-fog multi-cloud environments. We integrate multiple microservice
placement policies to demonstrate MicroFog's ability to support horizontally
scaled placement, thus reducing the application service response time up to
54%
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