A robust resource allocation model for optimizing data skew and consumption rate in cloud-based IoT environments

Abstract

The Internet of Things (IoT) is a network of connected objects designed to collect and exchange data using smart equipment and technologies. A significant challenge in guaranteeing a high level of end-user experience is the administration of IoT services. IoT networks are constructed using a variety of smart technologies such as detectors, controllers, Radio-frequency identification (RFID), Universal Mobile Telecommunications Systems (UMTS), Third Generation Cellular Networks (3G), and Global Systems for Mobile communications (GSM). Cloud technology significantly impacts how these networks grow by providing processing capabilities, network bandwidth, virtualized systems, and system software in an integrated environment. Capacity management, which assures effective resource use and load-balancing, avoids service level agreement (SLA) infractions, and enhances machine efficiency by minimizing operational expenses and power utilization, represents one of the fundamental problems in cloud-based ecosystems. To address these concerns, IoT-based robust decision-making resource management is often used. In this study, we investigate resource provisioning methods and identify the factors that must be considered for better utilization of resources in distributed systems. Specifically, we aim to improve the minimization rate, data skew rate, and approximate amount rate. We also highlight the challenges and complexities of hybrid optimization for efficient cloud-based capital allocation in the IoT.