Distributed Resource Distribution and Offloading for Resource-Agnostic Microservices in Industrial IoT

Due to increase in real-time mobile applications and Industrial Internet-of-Things (IIoT) devices, the edge computing paradigm provides a systematic and eccentric platform for real-time Internet-of-Things applications. Though the paradigm provides an effective infrastructure, however the resource requirements of IIoT devices change radically with time, which is described as a resource-agnostic property. Therefore, the estimation of resource requirements of IIoT devices is a critical and resilient assignment. In addition, it requires an extensive amount of resources to process the data traffic flows and microservice offloading. Hence, we present RAISE, a novel resource-agnostic microservice offloading scheme for mobile IIoT devices. RAISE efficiently estimates the resource-agnostic nature of IIoT devices to maximize their resource utilization in the network. Based on the estimated resource requirement, we propose a resource-agnostic microservice offloading scheme to maximize the success rate. Extensive experiments show that RAISE provides better performance in terms of network throughput and Quality-of-Service (QoS) than the other existing methods, SDTO and DTOS, in terms of cost and reliability.

I. Introduction

The rapid growth of the industrial revolution has directed us to amalgamate different advanced fabrication methods with IoT to develop an acute and efficient network manufacturing system. The main motive of such advanced system is to enable an elevated automation system by merging heterogeneous technologies like IIoT [1], [2], [3] and edge computing to authorize the formation of interdependent, reactive, and smart inventing system. Therefore, Mobile Edge Computing (MEC) [4], [5], [6], [7] plays important to role to envision such IIoT networks. MEC has emerged as a principal and essential paradigm for real-time IIoT applications. This kind of paradigm shifts the offloading mechanism to the network edge instead of offloading to a centralized infrastructure, i.e. a cloud platform. The advancement of MEC unites the cloud resource potential of the IIoT devices. Thus, such combination systematically takes out the core centralized compute capabilities to edge [8], [9], [10], [11], [12]. Such a kind of platform provides a productive and distinct unification of the network functionalities of the cloud platform and the access network. The edge platform provides a plethora of composite value-added microservices to distributed mobile applications [13], [14], [15], [16], while providing a set of new functionalities for mission-critical applications. Expansion of MEC is mainly concentrated on performance improvement in terms of flexibility, microservice latency, and power consumption over the typical cloud computing platform.