Artificial Intelligence Laboratory, Jozef Stefan Institute, Ljubljana 1000, Slovenia.
CVS Mobile d.d., Ljubljana 1000, Slovenia.
Sensors (Basel). 2018 Sep 4;18(9):2938. doi: 10.3390/s18092938.
The adoption of advanced Internet of Things (IoT) technologies has impressively improved in recent years by placing such services at the extreme Edge of the network. There are, however, specific Quality of Service (QoS) trade-offs that must be considered, particularly in situations when workloads vary over time or when IoT devices are dynamically changing their geographic position. This article proposes an innovative capillary computing architecture, which benefits from mainstream Fog and Cloud computing approaches and relies on a set of new services, including an Edge/Fog/Cloud Monitoring System and a Capillary Container Orchestrator. All necessary Microservices are implemented as Docker containers, and their orchestration is performed from the Edge computing nodes up to Fog and Cloud servers in the geographic vicinity of moving IoT devices. A car equipped with a Motorhome Artificial Intelligence Communication Hardware (MACH) system as an Edge node connected to several Fog and Cloud computing servers was used for testing. Compared to using a fixed centralized Cloud provider, the service response time provided by our proposed capillary computing architecture was almost four times faster according to the 99th percentile value along with a significantly smaller standard deviation, which represents a high QoS.
近年来,通过将此类服务置于网络的极端边缘,先进的物联网 (IoT) 技术的采用令人印象深刻。然而,必须考虑到特定的服务质量 (QoS) 权衡,特别是在工作负载随时间变化或物联网设备动态改变其地理位置的情况下。本文提出了一种创新的毛细管计算架构,该架构受益于主流雾计算和云计算方法,并依赖于一组新服务,包括边缘/雾/云监控系统和毛细管容器编排器。所有必要的微服务都实现为 Docker 容器,并且它们的编排是从边缘计算节点执行到靠近移动物联网设备的雾计算和云服务器。配备房车人工智能通信硬件 (MACH) 系统作为边缘节点并连接到几个雾计算和云计算服务器的汽车被用于测试。与使用固定的集中式云提供商相比,根据第 99 个百分位数值,我们提出的毛细管计算架构提供的服务响应时间几乎快了四倍,同时标准偏差也显著减小,这代表了高服务质量。