IoTScal‐H : Hybrid monitoring solution based on cloud computing for autonomic middleware‐level scalability management within IoT systems and different SLA traffic requirements

Abstract

SummaryWireless sensor network (WSN) technologies have enabled ubiquitous sensing to intersect many areas of modern day living. The creation of these devices offers the ability to get, gather, exchange, and consume environmental measurement from the physical world in a communicating‐actuating network, called the Internet of Things (IoT). As the number of physical world objects from heterogeneous network environments grows, the data produced by these objects raise uncontrollably, bringing a delicate challenge into scalability management in the IoT networks. Cloud computing is a much more mature technology, offering unlimited virtual capabilities in terms of storage capacity and processing power. Ostensibly, it seems that cloud computing and IoT are evolving independently on their own paths, but in reality, the integration of clouds with IoT will lead to deal with the inability to scale automatically depending on the overload caused by the drastic growth of the number of connected devices and/or by the huge amount of exchanged data in the IoT networks. In this paper, our objective is to promote the scalability management, using hybrid mechanism that will combine traffic‐oriented mechanism and resources‐oriented mechanism, with adaption actions. By the use of autonomic middleware within IoT systems, we seek to improve the monitoring components's architectural design, based on cloud computing‐oriented scalability solution. The intention is to maximize the number of satisfied requests, while maintaining at an acceptable QoS level of the system performances (RTT of the system, RAM, and CPU of the middleware). In order to evaluate our solution performance, we have performed different scenarios testbed experiments. Generally, our proposed results are better than those mentioned as reference.

Design and Implementation of an Information Management System for College Students in Higher Education Institutions Based on Cloud Computing

“…A data flow diagram (DFD) serves as a visual representation of the system's logical function, the direction of logical flow, and the logical transformation of data within the student information management system. It is a primary tool for expressing structural system analysis methods and employs graphical techniques to depict software models [26]. The accompanying data flow diagram does not include specific physical elements; instead, it solely describes the system's flow and processing of information.…”

Autonomic cloud computing based management and security solutions: State‐of‐the‐art, challenges, and opportunities

“…It is observed that the controller incrementally and proactively adjusts the servers based on the requirements. A cloud‐based monitoring solution for autonomic scalability management within IoT systems is described in Reference 61. It is a hybrid approach which combines traffic‐oriented and resource oriented mechanisms with adaptation actions.…”

Analysis of factors affecting IoT-based smart hospital design

“…The second optimization factor is the scalability that defines the ability of a device to adapt to the changes and ability to change based on IoT system's future needs. Zyane et al [151] suggested combining the traffic-oriented mechanism and the resource-oriented mechanism with adaptation actions using autonomous middleware for scalability management. This method, aimed to maximize the number of requests received by providing an acceptable QoS level and system performance.…”