On the fly deployment of fog nodes near users provides the flexibility of pushing services anywhere and whenever needed. Nevertheless, taking a real-life scenario, the cloud might limit the number of fogs to place for minimizing the complexity of monitoring a large number of fogs and cost for volunteers that do not offer their resources for free. This implies choosing the right time and best volunteer to create a fog which the cloud can benefit from is essential. This choice is subject to study the demand of a particular location for services in order to maximize the resources utilization of these fogs. A simple algorithm will not be able to explore randomly changing users' demands. Therefore, there is a need for an intelligent model capable of scheduling fog placement based on the user's requests. In this paper, we propose a Fog Scheduling Decision model based on reinforcement R-learning, which focuses on studying the behavior of service requesters and produces a suitable fog placement schedule based on the concept of average reward. Our model aims to decrease the cloud's load by utilizing the maximum available fogs resources over different locations. An implementation of our proposed R-learning model is provided in the paper, followed by a series of experiments on a real dataset to prove its efficiency in utilizing fog resources and minimizing the cloud's load. We also demonstrate the ability of our model to improve over time by adapting the new demand of users. Experiments comparing the decisions of our model with two other potential fog placement approaches used for task/service scheduling (threshold based and random based) show that the number of processed requests performed by the cloud decreases from 100 to 30% with a limited number of fogs to push. These results demonstrate that our proposed Fog Scheduling Decision model plays a crucial role in the placement of the on-demand fog to the right location at the right time while taking into account the user's needs.
No abstract
Fog computing is an extended cloud computing technology allowing services embedded into virtual machines or containers to be placed at the edge with closer proximity to the end devices. Nevertheless, one of the main difficulties adding up to the complexity of the on-demand fog placement topic is deciding on the proper time and place of the fog deployment process, and deriving the adequate container and service distribution over the available fogs at a certain location. Several techniques have been suggested in the current literature as potential solutions, in which some consider users preferences or random-based approach for fog deployment, while others reside on threshold-based mechanisms. However, due to the huge increase in the number of requests coming from end devices including IoT users, fog deployment and container placement must be scheduled to serve locations with high service requesting profiles while decreasing the cloud processing load. In this context, we first propose a fog placement model that allows to produce an adequate scheduling decision by using a hybrid technique combining time series forecasting and reinforcement learning. The proposed technique learns the intensity of service invocations and behavior of end devices at different locations over time for predicting the localization plan. Second, we propose a K-Means based clustering model embedded within a multi-objective optimization scheme for fog and container placement. A comparison of our proposed solution with random-based and threshold-based fog placement scheduling approaches show that the number of processed requests performed by the cloud decreases from 100% to 29% compared to 93% and 67% in the other two models. The aforementioned results explore the efficiency of our proposed scheme in scheduling the fogs in their rightful place, which helps in decreasing the cloud’s load, decreasing the network congestion, and increasing the overall quality of service. Moreover, experimental results illustrate that the proposed optimization model and clustering technique further improve the pre-existing heuristic-based solutions for container distribution.
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