Layers Of Cloud Computing Research Articles

Dynamic Fault Tolerance Aware Scheduling for Healthcare System on Fog Computing

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.

Hierarchical Offloading for Delay-Constrained Applications in Fog RAN

By computation offloading, fog radio access network (RAN) can support delay-constrained applications, since such a hierarchical network architecture enables parallel computing at local computing layer (LCL), edge computing layer (ECL), and cloud computing layer (CCL). Comparing to processing applications exclusively at the LCL, the assistance of processing at the ECL and the CCL can reduce computation latency, while introduce extra communication latency. These two latency are not only influenced by the hierarchical offloading decision but also by the available communication and computational resources at multiple computing layers. In this regard, this paper aims to jointly provision communication and computational resources for a powerful hierarchical offloading mechanism in fog RAN to complete delay-constrained applications with high-efficiency resource utilization. Specifically, we formulate a deadline-constrained energy minimization problem, which is followed by feasibility analysis, effective solution, and application extension. Through theoretical analyses and simulation results, we provide some insights on hierarchical offloading conditions and demonstrate advantages of our proposed algorithms in terms of the minimum affordable latency, total energy consumption, and scalability.