Adaptive Resource Allocation in Cloud Data Centers using Actor-Critical Deep Reinforcement Learning for Optimized Load Balancing

This paper proposes a deep reinforcement learning-based actor-critic method for efficient resource allocation in cloud computing. The proposed method uses an actor network to generate the allocation strategy and a critic network to evaluate the quality of the allocation. The actor and critic networks are trained using a deep reinforcement learning algorithm to optimize the allocation strategy. The proposed method is evaluated using a simulation-based experimental study, and the results show that it outperforms several existing allocation methods in terms of resource utilization, energy efficiency and overall cost. Some algorithms for managing workloads or virtual machines have been developed in previous works in an effort to reduce energy consumption; however, these solutions often fail to take into account the high dynamic nature of server states and are not implemented at a sufficiently enough scale. In order to guarantee the QoS of workloads while simultaneously lowering the computational energy consumption of physical servers, this study proposes the Actor Critic based Compute-Intensive Workload Allocation Scheme (AC-CIWAS). AC-CIWAS captures the dynamic feature of server states in a continuous manner, and considers the influence of different workloads on energy consumption, to accomplish logical task allocation. In order to determine how best to allocate workloads in terms of energy efficiency, AC-CIWAS uses a Deep Reinforcement Learning (DRL)-based Actor Critic (AC) algorithm to calculate the projected cumulative return over time. Through simulation, we see that the proposed AC-CIWAS can reduce the workload of the job scheduled with QoS assurance by around 20% decrease compared to existing baseline allocation methods. The report also covers the ways in which the proposed technology could be used in cloud computing and offers suggestions for future study

A Safe Vaccine (DV-STM-07) against Salmonella Infection Prevents Abortion and Confers Protective Immunity to the Pregnant and New Born Mice

Pregnancy is a transient immuno-compromised condition which has evolved to avoid the immune rejection of the fetus by the maternal immune system. The altered immune response of the pregnant female leads to increased susceptibility to invading pathogens, resulting in abortion and congenital defects of the fetus and a subnormal response to vaccination. Active vaccination during pregnancy may lead to abortion induced by heightened cell mediated immune response. In this study, we have administered the highly attenuated vaccine strain ΔpmrG-HM-D (DV-STM-07) in female mice before the onset of pregnancy and followed the immune reaction against challenge with virulent S. Typhimurium in pregnant mice. Here we demonstrate that DV-STM-07 vaccine gives protection against Salmonella in pregnant mice and also prevents Salmonella induced abortion. This protection is conferred by directing the immune response towards Th2 activation and Th1 suppression. The low Th1 response prevents abortion. The use of live attenuated vaccine just before pregnancy carries the risk of transmission to the fetus. We have shown that this vaccine is safe as the vaccine strain is quickly eliminated from the mother and is not transmitted to the fetus. This vaccine also confers immunity to the new born mice of vaccinated mothers. Since there is no evidence of the vaccine candidate reaching the new born mice, we hypothesize that it may be due to trans-colostral transfer of protective anti-Salmonella antibodies. These results suggest that our vaccine DV-STM-07 can be very useful in preventing abortion in the pregnant individuals and confer immunity to the new born. Since there are no such vaccine candidates which can be given to the new born and to the pregnant women, this vaccine holds a very bright future to combat Salmonella induced pregnancy loss

An Efficient Comparative Analysis of CNN-based Image Classification in the Jupyter Tool Using Multi-Stage Techniques

The main process of this image classification with a convolution neural network using deep learning model was performed in the programming language Python code in the Jupyter tool, mainly using the data set of IRS P-6 LISS IV from an Indian remote sensing satellite with a high resolution multi-spectral camera with around 5.8m from an 817 km altitude Delhi image. To classify the areas within the cropped image required to apply enhancement techniques, the image size was 1000 mb. To view this image file required high-end software for opening. For that, initially, ERDAS imaging software viewer was used for cropping into correct resolution pixels. based on that cropped image used for image classification with preprocessing for applying filters for enhancement. And with the convolution neural network model, required to train the sample images of the same pixels, was collected from the group of objects that were cropped. Then we needed to use image sample areas to train the model with learning rate and epoch rate to improve object detection accuracy using the Jupyter notebook tool with tensorflow and machine learning model produce the accuracy rate of 90.78%

SEARCH FOR HARMONIZED KEYWORDS USING THE VOTED LAB FEATURE AND ALLOW RE CRYPTOSYSTEM FOR ELECTRONIC HEALTH CLOUDS

The searchable file encryption (SE) plan is really a technology to include security protection and favorable operability functions together, which could play a huge role within the e-health record system. A digital health record product is a singular application which will bring great convenience in healthcare. Within this paper, we introduce a singular cryptographic primitive named as conjunctive keyword search with designated tester and timing enabled proxy re-file encryption function, which is a type of a period-dependent SE plan. We design a singular searchable file encryption plan supporting secure conjunctive keyword search and approved delegation function. In contrast to existing schemes, the work is capable of timing enabled proxy re-file encryption with effective delegation revocation. The security and privacy from the sensitive private information would be the major concerns from the users that could hinder further development and broadly adoption from the systems. We formulate a method model along with a security model for that suggested Re-deck plan to exhibit that it's a competent plan demonstrated secure within the standard model. The comparison and extensive simulations show it features a low computation and storage overhead. It might enable patients to delegate partial access legal rights to other people to function search functions over their records inside a short time period. The size of the timeframe for that delegate to look and decrypt the delegator’s encrypted documents could be controlled

Oncogenic EGFR Represses the TET1 DNA Demethylase to Induce Silencing of Tumor Suppressors in Cancer Cells

SummaryOncogene-induced DNA methylation-mediated transcriptional silencing of tumor suppressors frequently occurs in cancer, but the mechanism and functional role of this silencing in oncogenesis are not fully understood. Here, we show that oncogenic epidermal growth factor receptor (EGFR) induces silencing of multiple unrelated tumor suppressors in lung adenocarcinomas and glioblastomas by inhibiting the DNA demethylase TET oncogene family member 1 (TET1) via the C/EBPα transcription factor. After oncogenic EGFR inhibition, TET1 binds to tumor suppressor promoters and induces their re-expression through active DNA demethylation. Ectopic expression of TET1 potently inhibits lung and glioblastoma tumor growth, and TET1 knockdown confers resistance to EGFR inhibitors in lung cancer cells. Lung cancer samples exhibited reduced TET1 expression or TET1 cytoplasmic localization in the majority of cases. Collectively, these results identify a conserved pathway of oncogenic EGFR-induced DNA methylation-mediated transcriptional silencing of tumor suppressors that may have therapeutic benefits for oncogenic EGFR-mediated lung cancers and glioblastomas

Differentially Evolved Genes of Salmonella Pathogenicity Islands: Insights into the Mechanism of Host Specificity in Salmonella

BACKGROUND: The species Salmonella enterica (S. enterica) includes many serovars that cause disease in avian and mammalian hosts. These serovars differ greatly in their host range and their degree of host adaptation. The host specificity of S. enterica serovars appears to be a complex phenomenon governed by multiple factors acting at different stages of the infection process, which makes identification of the cause/s of host specificity solely by experimental methods difficult. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we have employed a molecular evolution and phylogenetics based approach to identify genes that might play important roles in conferring host specificity to different serovars of S. enterica. These genes are 'differentially evolved' in different S. enterica serovars. This list of 'differentially evolved' genes includes genes that encode translocon proteins (SipD, SseC and SseD) of both Salmonella pathogenicity islands 1 and 2 encoded type three secretion systems, sptP, which encodes an effector protein that inhibits the mitogen-activated protein kinase pathway of the host cell, and genes which encode effector proteins (SseF and SifA) that are important in placing the Salmonella-containing vacuole in a juxtanuclear position. CONCLUSIONS/SIGNIFICANCE: Analysis of known functions of these 'differentially evolved genes' indicates that the products of these genes directly interact with the host cell and manipulate its functions and thereby confer host specificity, at least in part, to different serovars of S. enterica that are considered in this study

The firefly technique with courtship training optimized for load balancing independent parallel computer task scheduling in cloud computing

Load Balancing strategies is essential provisioning towards cloud environments is a hard challenge due of the situation dynamic nature and varying additional actions selves. Load balancing approaches are spoken at a data center levels in just this investigation. A much more effective source management technique for virtualizing data centers that raises the quantity of computers to satisfy the necessities of changing assignments requiring relocation is suggested in this study. The anticipated method, termed First-ahead load balancing VM Allocation (FALB), incorporates the optimization unbiased, which constructs a accurate formula of UPMSP with sequential setup weeks back basic feasible. In particular, an enhanced firefly algorithm along with engagement learning is planned. Ultimately, in order to offer numerical solutions inside an appropriate timescale, the suggested algorithm is utilized to calculate the UPMSP with set of images process time. FALB is contrasted the with best solution availability of Cloud centered on VM migration dubbed Local Regression-Minimum Migration Time (LR-MMT) (LR-MMT)