Novel Resource Allocation Algorithms for the Social Internet of Things Based Fog Computing Paradigm

Abstract

Social Internet of Things (SIoT) is a control paradigm by the integration of social networking concepts into the Internet of Things, and Fog Computing (FC) is an emerging technology that is aimed at moving the cloud computing facilities to the access network. Recently, the SIoT and FC models are combined by using complementary features, and a new Social Fog IoT (SFIoT) paradigm has been developed. In this paper, we design novel resource allocation algorithms for the SFIoT system. Considering the social relationship and each preference, mobile devices in the SFIoT effectively share the limited computation and communication resources of FC operators. To formulate the interaction among mobile devices and FC operator, we adopt the basic concept of two game models: voting and bargaining games. Bicooperative voting approach can make control decisions for the resource allocation method, and Nash bargaining solution is used to effectively distribute the computation resource to different application tasks. Based on the two-phase game model, the proposed scheme takes various benefits in a fair-efficient way. Through the extensive simulation experiments, we can validate the superiority of our proposed approach by the fact that it produces a mutually acceptable agreement among game players and significantly outperforms the existing protocols. Last, we point out the further challenges and future research issues about the SFIoT paradigm opportunities.

Performance Enhancement of Fog Environment with Deadline Aware Resource Allocation Algorithm

“…Also, they demonstrated how to implement custom application placement in iFogSim simulated Fog environment along with an IoTenabled smart healthcare case study. in [30], it is designed novel resource allocation algorithms for the Social Internet of Things (SFIoT) system. They adopt the basic concept of two game models: voting and bargaining games to formulate the interaction among mobile devices and FC operator.…”

An Intelligent Adaptive Algorithm for Servers Balancing and Tasks Scheduling over Mobile Fog Computing Networks

“…And the maximized operating power on the user device is set to 2 W. For the wireless channel model, we apply a Gaussian Markov block fading autoregressive model. And the wireless channel gain G τ caused by path loss is set to -30 dB and the channel noise N τ is set to 10 −9 W. For the weighted cost model, the weight coefficients for adjusting the trade-off among the three factors are initially set to 6 Wireless Communications and Mobile Computing (1,6,3), which are mapping to the data size b τ+1 of the remaining task, the power consumption P τ of the user device, and the appended workload L τ of participant server, respectively. In the DRL-based algorithm, the number T of periods during one episode is set to 100.…”

“…The distances between the user device and three servers are all set to 100 m. The workload degrees of all servers are set to 1. The tuple of weight coefficients ðΩ 1 , Ω 2 , Ω 3 Þ is set to (1,6,3). We calculate the average values in all the periods of one episode, including weighted reward, data size of the remaining task, and power consumption of user device.…”

“…By deploying fog servers for providing supplementary resources between cloud and users, mobile fog computing (MFC) is designed as a novel computing paradigm in order to reduce responding latency of services and avoid traffic congestion of network [5]. Hence, in the cloud-aware MFC network [6], most services of users can be performed at the fog servers and others at the cloud servers. Differently, mobile edge computing (MEC) mainly considers the task offloading problem on mobile user devices [7].…”

Dynamic Task Scheduling using Balanced VM Allocation Policy for Fog Computing Platforms

“…It was noticed that the proposed algorithm showed better outcomes when compared to traditional ACO. Many researches [46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,70,71] discussed about scheduling and allocation methods in fog and cloud environments.…”