On the Application of a Mobile Grid Technology to Computational Fluid Dynamics

On the basis of harmonic mapping theory,a mobile grid technology is applied to computational fluid dynamics(CFD).Starting from the observation that standard fixed-grid techniques often fail in addressing problems with large deformations,we elaborate a new algorithm relying on the software COMSOL Multiphysics 5.3a to solve the coupling of the mobile grid equation and the governing differential equations for fluid flow.The motion of water in a water tank when the tank waggles is simulated.We demonstrate that this technology can be implemented without a significant increase in the computational cost with respect to standard numerical methods.

A scheduling algorithm with dynamic properties in mobile grid

Mobile grid is a branch of grid computing that incorporates mobile devices into the grid infrastructure. It poses new challenges because mobile devices are typically resource-constrained and exhibit unique characteristics such as instability in network connections. New scheduling strate- gies are imperative in mobile grid to efficiently utilize the devices. This paper presents a scheduling algorithm that con- siders dynamic properties of mobile devices such as avail- ability, reliability, maintainability, and usage pattern in mo- bile grid environments. In particular, usage patterns caused by voluntarily or involuntarily losing a connection, such as switching off the device or a network interruption could be important criteria for choosing the best resource to execute a job. The experimental results show that our scheduling al- gorithm provides superior performance in terms of execution time, as compared to the other methods that do not consider usage pattern. Throughout the experiments, we found it es- sential to consider usage pattern for improving performance in the mobile grid.

Predicting Resource Availability in Local Mobile Crowd Computing Using Convolutional GRU

In mobile crowd computing(MCC),people’s smart mobile devices(SMDs)are utilized as computing resources.Considering the ever-growing computing capabilities of today’s SMDs,a collection of them can offer significantly high-performance computing services.In a localMCC,the SMDs are typically connected to a local Wi-Fi network.Organizations and institutions can leverage the SMDs available within the campus to form local MCCs to cater to their computing needs without any financial and operational burden.Though it offers an economical and sustainable computing solution,users’mobility poses a serious issue in the QoS of MCC.To address this,before submitting a job to an SMD,we suggest estimating that particular SMD’s availability in the network until the job is finished.For this,we propose a convolutional GRU-based prediction model to assess how long an SMD is likely to be available in the network from any given point of time.For experimental purposes,we collected real users’mobility data(in-time and outtime)with respect to a Wi-Fi access point.To build the prediction model,we presented a novel feature extraction method to be applied to the time-series data.The experimental results prove that the proposed convolutional GRU model outperforms the conventional GRU model.