Abstract
Internet of Things (IoT) contributes to improve the quality of life as it supports many applications, especially healthcare systems. Data generated from IoT devices is sent to the Cloud Computing (CC) for processing and storage, despite the latency caused by the distance. Because of the revolution in IoT devices, data sent to CC has been increasing. As a result, another problem added to the latency was increasing congestion on the cloud network. Fog Computing (FC) was used to solve these problems because of its proximity to IoT devices, while filtering data is sent to the CC. FC is a middle layer located between IoT devices and the CC layer. Due to the massive data generated by IoT devices on FC, Dynamic Weighted Round Robin (DWRR) algorithm was used, which represents a load balancing (LB) algorithm that is applied to schedule and distributes data among fog servers by reading CPU and memory values of these servers in order to improve system performance. The results proved that DWRR algorithm provides high throughput which reaches 3290 req/sec at 919 users. A lot of research is concerned with distribution of workload by using LB techniques without paying much attention to Fault Tolerance (FT), which implies that the system continues to operate even when fault occurs. Therefore, we proposed a replication FT technique called primary-backup replication based on dynamic checkpoint interval on FC. Checkpoint was used to replicate new data from a primary server to a backup server dynamically by monitoring CPU values of primary fog server, so that checkpoint occurs only when the CPU value is larger than 0.2 to reduce overhead. The results showed that the execution time of data filtering process on the FC with a dynamic checkpoint is less than the time spent in the case of the static checkpoint that is independent on the CPU status.
Highlights
Internet of Things (IoT) allows the connection of different things such as sensors and cellular phones via the Internet [1]
Cloud Computing (CC) is the easiest way to gather and process data generated from IoT devices by connecting these devices to cloud servers [3]
After data are distributed based on the Dynamic Weighted Round Robin (DWRR) algorithm, data in the fog servers will be preserved by using the primary-backup replication, called the active/passive (A/P) replication technique
Summary
IoT allows the connection of different things such as sensors and cellular phones via the Internet [1]. A computing paradigm called Fog Computing (FC) was proposed to process data generated from IoT (edge) devices in real-time [5]. This paper proposes an FT architecture on fog servers for healthcare data generated from edge devices (sensors). Ryuji et al [11] presented results for the non-replication fault tolerance which was applied to protect only data coming from the sensor to the fog server. Al-Joboury and Al-Hemiary [13] provided a mechanism to monitor healthcare data in real-time and reduce the congestion on the cloud network This was achieved by sending the pulse (heartbeat) sensor messages by MQTT protocol to the fog server. The remainder of the present paper is arranged as an illustration of the system architecture of FC that consists of three layers: IoT (edge) devices, fog, and cloud layer.
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