دانلود رایگان مقاله انگلیسی جایگذاری آگاه منابع واحد های کاربردی IOT در پارادایم محاسبات ابری - مه - نشریه IEEE

Abstract

With the evolving IoT scenario, computing has spread to the most minuscule everyday activities, leading to a momentous shift in the way applications are developed and deployed. With the volume of impact increasing exponentially, a coherent approach of deploying these applications is critical for an efficient utilization of the network infrastructure. A typical IoT application consists of various modules running together with active interdependencies; traditionally running on the Cloud hosted in global data centres. In this paper, we present a Module Mapping Algorithm for efficient utilization of resources in the network infrastructure by efficiently deploying Application Modules in Fog-Cloud Infrastructure for IoT based applications. With Fog computing into picture, computation is dynamically distributed across the Fog and Cloud layer, and the modules of an application can thus be deployed closer to the source on devices in the Fog layer. The result of this work can serve as a Micro-benchmark in studies/research related with IoT and Fog Computing, and can be used for Quality of Service (QoS) and Service Level Objective benchmarking for IoT applications. The approach is generic, and applies to a wide range of standardized IoT applications over varied network topologies irrespective of load.

VI. CONCLUSION AND FUTURE WORK

Carrying forward our work [23], [24] here we present the result of the efficient utilization of resources in the network infrastructure by efficiently deploying Application Modules in Fog-Cloud Infrastructure for IoT based applications. We present the impact of an evolving paradigm that is Fog Computing towards solving the problem of latency in time critical IoT applications, while also accounting for the pressure on the existing network resources owing to the exponentially increasing workload due to heavy IoT usage in daily life across myriad sectors.

We outlined the key characteristics that impact the performance of such IoT applications, and have classified and kept into account the static part while increasing the network efficiency and broadening the scope of such applications. The logarithmic complexity of the Module Mapping Algorithm as proposed in the paper trumps the usual Brute Force solution to such problems, which tends to be NP-hard.

We believe that the result of this work can serve as a Micro-benchmark in studies/research related with IoT and Fog Computing, as the algorithmic approach is generic and the case study of the application has been developed keeping in mind several inline use cases applying to a wide range of IoT and Fog/Cloud applications over varied network topologies. The result obtained can thus be used for QoS and Service Level Objective benchmarking for IoT applications.