A Network-aware Virtual Machine Placement and Migration Approach in Cloud Computing

Abstract—Cloud computing represents a major step up in computing whereby shared computation resources are provided on demand. In such a scenario, applications and data thereof can be hosted by various networked virtual machines (VMs). As applications, especially data-intensive applications, often need to communicate with data frequently, the network I/O performance would affect the overall application performance significantly. Therefore, placement of virtual machines which host an application and migration of these virtual machines while the unexpected network latency or congestion occurs is critical to achieve and maintain the application performance. To address these issues, this paper proposes a virtual machine placement and migration approach to minimizing the data transfer time consumption. Our simulation studies suggest that the proposed approach is effective in optimizing the data transfer between the virtual machine and data, thus helping optimize the overall application performance. Keywords—cloud computing, virtual machine, placement, migration, network I

JefiAtten: An Attention Based Neural Network Model for Solving Maxwell's Equations with Charge and Current Sources

We present JefiAtten, a novel neural network model employing the attention mechanism to solve Maxwell's equations efficiently. JefiAtten uses self-attention and cross-attention modules to understand the interplay between charge density, current density, and electromagnetic fields. Our results indicate that JefiAtten can generalize well to a range of scenarios, maintaining accuracy across various spatial distribution and handling amplitude variations. The model showcases an improvement in computation speed after training, compared to traditional integral methods. The adaptability of the model suggests potential for broader applications in computational physics, with further refinements to enhance its predictive capabilities and computational efficiency. Our work is a testament to the efficacy of integrating attention mechanisms with numerical simulations, marking a step forward in the quest for data-driven solutions to physical phenomena.Comment: 29 pages, 11 figure

JefiGPU: Jefimenko's Equations on GPU

We have implemented a GPU version of the Jefimenko's equations -- JefiGPU. Given the proper distributions of the source terms $\rho$ (charge density) and $\mathbf{J}$ (current density) in the source volume, the algorithm gives the electromagnetic fields in the observational region (not necessarily overlaps the vicinity of the sources). To verify the accuracy of the GPU implementation, we have compared the obtained results with that of the theoretical ones. Our results show that the deviations of the GPU results from the theoretical ones are around 5\%. Meanwhile, we have also compared the performance of the GPU implementation with a CPU version. The simulation results indicate that the GPU code is significantly faster than the CPU version. Finally, we have studied the parameter dependence of the execution time and memory consumption on one NVIDIA Tesla V100 card. Our code can be consistently coupled to RBG (Relativistic Boltzmann equations on GPUs) and many other GPU-based algorithms in physics.Comment: 21 pages, 8 figures, 4 table

Assessment of changes in lipid profile and related enzymes in children with asthma

Purpose: To investigate the influence of the lipid profile and related parameters on the development of asthma in children aged 10 to 15 years.Methods: Peripheral blood samples were collected from a group diagnosed with asthma as well as from a healthy control group. The lipid profile parameters measured were total cholesterol (TC), triglycerides (TG), low-density lipoprotein (LDL), very-low-density lipoprotein (VLDL), high-density lipoprotein (HDL), serum total antioxidant capacity (TAC), reduced glutathione (GSH), and malondialdehyde (MDA), and the activities of lecithin–cholesterol acyltransferase (LCAT) and cholesteryl ester transfer protein (CETP).Results: TC, TG, LDL, and VLDL levels were significantly (p ≤ 0.05) higher in the asthma group compared with the controls, while HDL level was lower. Total TAC and GSH were lower in the asthma group, while MDA level, and LCAT and CETP activities were higher.Conclusion: There is a link between an elevated lipid profile and increased antioxidant capacity in asthmatic children

Obstructive sleep apnea and 19 gastrointestinal diseases: a Mendelian randomization study

BackgroundAlterations gastrointestinal diseases (GDs) were reported in individuals with obstructive sleep apnea (OSA), however, the genetic background between OSA and GDs is still unclear.MethodsThis investigation employed Mendelian randomization (MR) analyses to evaluate the causal effect between OSA and 19 types of GDs (gastroesophageal reflux disease (GERD), ulcerative colitis, celiac disease, Crohn’s disease, chronic gastritis, irritable bowel syndrome, primary biliary cholangitis, diverticular disease, gastroduodenal ulcer, acute pancreatitis, non-alcoholic fatty liver disease, primary sclerosing cholangitis, cirrhosis, calculus of bile duct, calculus of gallbladder, pancreatic cancer, gastric cancer, colorectal cancer, and esophageal cancer). The inverse-variance weighted (IVW) method was used to evaluate the main effects model of causality.ResultsThis MR study suggests that OSA may play a causal role inflammation-related GDs (GERD, PIVW=5.94×10-9; gastroduodenal ulcer, PIVW=1×10-4; chronic gastritis, PIVW=0.0214; ulcerative colitis, PIVW=0.0296), and gallstones (calculi of the gallbladder, PIVW=0.0429; calculi of the bile duct, PIVW=0.0068). After accounting for obesity, type 2 diabetes, smoking, and alcohol consumption, the multivariate MR (MVMR) analysis identified that OSA is an independent risk factor for GERD, gastroduodenal ulcer, and calculus of the bile duct. The reverse MVMR analysis showed a causal effect of GERD on OSA. Besides, we did not find that the predisposition to OSA was associated with 4 cancers.ConclusionThis MR analysis provides compelling evidence of an independent causal relationship between genetically predicted OSA and an elevated risk of inflammation-related GDs. Besides, no causal association was observed between OSA and cancers. Further studies should be carried out to verify our findings