Runtime reconfiguration of data services for dealing with out-of-range stream fluctuation in cloud-edge environments

The integration of cloud and IoT edge devices is of significance in reducing the latency of IoT stream data processing by moving services closer to the edge-end.In this connection,a key issue is to determine when and where services should be deployed.Common service deployment strategies used to be static based on the rules defined at the design time.However,dynamically changing IoT environments bring about unexpected situations such as out-of-range stream fluctuation,where the static service deployment solutions are not efficient.In this paper,we propose a dynamic service deployment mechanism based on the prediction of upcoming stream data.To effectively predict upcoming workloads,we combine the online machine learning methods with an online optimization algorithm for service deployment.A simulation-based evaluation demonstrates that,compared with those state-of-the art approaches,the approach proposed in this paper has a lower latency of stream processing.

Helmholtz decomposition-based SPH

Background SPH method has been widely used in the simulation of water scenes.As a numerical method of partial differential equations,SPH can easily deal with the distorted and complex boundary.In addition,the implementation of SPH is relatively simple,and the results are stable and not easy to diverge.However,SPH method also has its own limitations.In order to further improve the performance of SPH method and expand its application scope,a series of key and difficult problems restricting the development of SPH need to be improved.Methods In this paper,we introduce the idea of Helmholtz decomposition into the framework of smoothed particle hydrodynamics(SPH)and propose a novel velocity projection scheme for three-dimensional water simulation.First,we apply Helmholtz decomposition to a three-dimensional velocity field and decompose it into three orthogonal subspaces.Then,our method combines the idea of spatial derivatives in SPH to obtain a discrete Poisson velocity equation.Finally,the conjugate gradient(CG)is utilized to efficiently solve the Poisson equation.Results The experimental results show that the proposed scheme is suitable for various situations and has higher efficiency than the current SPH projection scheme.Conclusion Compared with the previous projection scheme,our solution does not need to modify the particle velocity indirectly by pressure projection,but directly by velocity field projection.The new scheme can be well integrated into the existing SPH framework,and can be applied to the interaction of water with static and dynamic obstacles,even for viscous fluid.

Multipath affinage stacked-hourglass networks for human pose estimation

Recently,stacked hourglass network has shown outstanding performance in human pose estimation.However,repeated bottom-up and top-down stride convolution operations in deep convolutional neural networks lead to a significant decrease in the initial image resolution.In order to address this problem,we propose to incorporate affinage module and residual attention module into stacked hourglass network for human pose estimation.This paper introduces a novel network architecture to replace the stacked hourglass network of up-sampling operation for getting high-resolution features.We refer to the architecture as an affinage module which is critical to improve the performance of the stacked hourglass network.Additionally,we also propose a novel residual attention module to increase the supervision of up-sample process.The effectiveness of the introduced module is evaluated on standard benchmarks.Various experimental results demonstrated that our method can achieve more accurate and more robust human pose estimation results in images with complex background.