A Capillary Computing Architecture for Dynamic Internet of Things: Orchestration of Microservices from Edge Devices to Fog and Cloud Providers.

Salman Taherizadeh,Marko Grobelnik,Vlado Stankovski

doi:10.3390/s18092938

Abstract

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.

Highlights

With the emergence of the Internet of Things (IoT), various Artificial Intelligence (AI) algorithms of different computational complexities are being designed to operate on continuously generated sensor data streams
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
The existing approaches to the definition of a suitable Edge or Fog computing architecture that we found in the literature are hereby categorized into three groups: (i) studies that use a static amount of Edge and Fog resources as a complete replacement to Cloud infrastructures; (ii) studies that intend to discover available Edge and Fog computing resources during runtime, when the workload increases and elasticity must be achieved; and (iii) replicating services in all Edge devices, Fog nodes and Cloud resources to address reliability and resilience problems

Summary

Introduction

With the emergence of the Internet of Things (IoT), various Artificial Intelligence (AI) algorithms of different computational complexities are being designed to operate on continuously generated sensor data streams. Examples are battery-driven vehicles, robots, smartphones, Raspberry Pi [4] or Arduino [5]. In this context, Fog nodes can be understood as. The concept of Microservices provides a revolutionary architecture, which relies mainly on new lightweight container technologies for virtualization. Sensors 2018, 18, x FOR PEER REVIEW it possible to develop new distributed computing software systems that are able to achieve high. Fog architectures providing such healing, self-regulating and so on. QoS, flexibility, dependability other properties due to their autonomic suchselfas achieve high QoS, flexibility, and dependability and other properties due to self-behavior, their autonomic self-monitoring, self-reconfiguration, self-healing, self-regulating behavior, such self-adaptation, as self-monitoring, self-adaptation, self-optimization, self-reconfiguration, self-optimization, selfand so on.

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