RACE: Resource Aware Cost-Efficient Scheduler for Cloud Fog Environment

Abstract

Fog Computing is one of the new computing structures which takes the Cloud to the verge of the network. The structure is formulated for applications that need low latency. Fog Computing has been projected to improve the disadvantages of Cloud Computing. The system is confronted with the variability of dynamic resources that are heterogeneous and distributed. Hence, efficient scheduling and resource allocation are necessary to maximize the use of these resources and the satisfaction of users. In this paper, a resource-aware scheduler RACE (Resource Aware Cost-Efficient Scheduler) is proposed to distribute the incoming application modules to Fog devices that maximize resource utilization at the Fog layer, reduces the monetary cost of using Cloud resources with minimum execution time of applications and minimum bandwidth usage. This RACE comprises of two algorithms. The ModuleScheduler in RACE categorizes the incoming application modules according to their computation and bandwidth requirements which are then placed by CompareModule. Comprehensive experimental results obtained from the simulation by using ifogsim simulator show that our approach performs better in most of the cases as compared to the Traditional Cloud placement and the baseline algorithm. INDEX TERMS Cloud/Fog environment, scheduling, internet of things (IoTs)

GA-IRACE: Genetic Algorithm-Based Improved Resource Aware Cost-Efficient Scheduler for Cloud Fog Computing Environment

“…Figure 8 shows that the execution time of A 1 for GA-IRACE is significantly improved compared to using the conventional method for cloud placement. It can be seen in Figure 8 that the execution time of A 1 improves for the GA-IRACE as compared to the traditional cloud placement, RACE [13], Taneja and Davy [41], and the random placement. For A 2 , the bandwidth-intensive application RACE [13] has a better execution time than Taneja and Davy [41], traditional cloud placement, and the random solution; however, the GA-IRACE has a better execution time than all baseline algorithms.…”

“…In the experiments, the proposed scheduling algorithm GA-IRACE was compared with the conventional cloud placement, RACE [13], and the other baseline algorithms, i.e., Taneja and Davy [41] and the random solution in terms of execution time, monetary cost, and bandwidth. The simulation findings (Figures 8-16) show that the proposed placement technique has a tremendously positive effect on execution time, the bandwidth, and the monetary cost of applications in all three network topologies tested.…”

“…In case when all application modules are accommodated at the fog layer, it may reduce the monetary cost and bandwidth of the application modules. Still, it may On the other hand, when data is transferred from the fog to the cloud layer for processing, it may reduce the task's execution time, increasing the bandwidth and the monetary cost for applications [13,49]. Therefore, we are essentially concerned with formulating the goal of scheduling in a cloud fog environment that can reduce the applications execution time while optimizing the bandwidth and monetary cost.…”

“…The CSP acquires cloud resources, such as storage and processing, and makes those resources available to cloud users [12]. Customer tasks are received by the cloud task manager in the form of cloudlets and are passed to the task scheduler [13]. These traditional clouds are expected to provide data service subscribers with flexible and efficient cloud computing [10].…”

“…Fog nodes can send data from the fog layer to the cloud, where it is stored and processed for long-term analytics. The data transferred from the fog layer to the cloud for processing reduces the task's execution time, but it may increase the bandwidth and the monetary cost when using the cloud resources [13]. On the other hand, real-time applications require a faster response than delaytolerant applications.…”