Abstract
Fog computing aims to support applications requiring low latency and high scalability by using resources at the edge level. In general, fog computing comprises several autonomous mobile or static devices that share their idle resources to run different services. The providers of these devices also need to be compensated based on their device usage. In any fog-based resource-allocation problem, both cost and performance need to be considered for generating an efficient resource-allocation plan. Estimating the cost of using fog devices prior to the resource allocation helps to minimize the cost and maximize the performance of the system. In the fog computing domain, recent research works have proposed various resource-allocation algorithms without considering the compensation to resource providers and the cost estimation of the fog resources. Moreover, the existing cost models in similar paradigms such as in the cloud are not suitable for fog environments as the scaling of different autonomous resources with heterogeneity and variety of offerings is much more complicated. To fill this gap, this study first proposes a micro-level compensation cost model and then proposes a new resource-allocation method based on the cost model, which benefits both providers and users. Experimental results show that the proposed algorithm ensures better resource-allocation performance and lowers application processing costs when compared to the existing best-fit algorithm.
Highlights
In recent years, Internet of Things (IoT) has become a significant influence for many industries because of various smart features enabled by the advancements in sensor and communication technologies
This study proposed a model for micro-level cost estimation and Fog Stable Matching Resource Allocation (FSMRA) algorithm based on the proposed cost model for resource allocation that benefits both the customers and the providers
The study considered the user incentivization to the users who offer their resources in the proposed cost model, which is one of the most crucial aspects of fog computing paradigm
Summary
Internet of Things (IoT) has become a significant influence for many industries because of various smart features enabled by the advancements in sensor and communication technologies. As the number of devices is rapidly growing and the data generated by these devices are increasing at an alarming rate, considering the need for processing such data within the time bounds of an application, traditional cloud computing cannot be used due to the limitations such as high latency and limited network bandwidth. Fog computing is envisioned to empower the integration of computation and storage of idle autonomous end devices such as smartphones, tablets, laptops, and other stationary computation devices [3]. Using these idle devices can improve the quality of services needed for executing IoT applications including disaster-related services [4].
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