Stochastic scheduling and workload allocation : QoS support and profitable brokering in computing grids

Abstract: The Grid can be seen as a collection of services each of which performs some functionality. Users of the Grid seek to use combinations of these services to perform the overall task they need to achieve. In general this can be seen as aset of services with a workflow document describing how these services should be combined. The user may also have certain constraints on the workflow operations, such as execution time or cost ----t~ th~ user, specified in the form of a Quality of Service (QoS) document. The users . submit their workflow to a brokering service along with the QoS document. The brokering service's task is to map any given workflow to a subset of the Grid services taking the QoS and state of the Grid into account -- service availability and performance. We propose an approach for generating constraint equations describing the workflow, the QoS requirements and the state of the Grid. This set of equations may be solved using Mixed-Integer Linear Programming (MILP), which is the traditional method. We further develop a novel 2-stage stochastic MILP which is capable of dealing with the volatile nature of the Grid and adapting the selection of the services during the lifetime of the workflow. We present experimental results comparing our approaches, showing that the . 2-stage stochastic programming approach performs consistently better than other traditional approaches. Next we addresses workload allocation techniques for Grid workflows in a multi-cluster Grid We model individual clusters as MIMIk. queues and obtain a numerical solutio~ for missed deadlines (failures) of tasks of Grid workflows. We also present an efficient algorithm for obtaining workload allocations of clusters. Next we model individual cluster resources as G/G/l queues and solve an optimisation problem that minimises QoS requirement violation, provides QoS guarantee and outperforms reservation based scheduling algorithms. Both approaches are evaluated through an experimental simulation and the results confirm that the proposed workload allocation strategies combined with traditional scheduling algorithms performs considerably better in terms of satisfying QoS requirements of Grid workflows than scheduling algorithms that don't employ such workload allocation techniques. Next we develop a novel method for Grid brokers that aims at maximising profit whilst satisfying end-user needs with a sufficient guarantee in a volatile utility Grid. We develop a develop a 2-stage stochastic MILP which is capable of dealing with the volatile nature . of the Grid and obtaining cost bounds that ensure that end-user cost is minimised or satisfied and broker's profit is maximised with sufficient guarantee. These bounds help brokers know beforehand whether the budget limits of end-users can be satisfied and. if not then???????? obtain appropriate future leases from service providers. Experimental results confirm the efficacy of our approach.Imperial Users onl

The role of Fc receptor-like 6 in innate and adaptive immunity

Fc receptor-like 6 (FCRL6) is an immunoreceptor tyrosine-based inhibitory motif-bearing transmembrane receptor upregulated on human cytotoxic T and NK cells during chronic immune activation. It has been suggested to act as an inhibitory receptor by possibly interacting with human leukocyte antigen-DR (HLA-DR), but its function remains largely unknown. We initially investigated the role of FCRL6 in T cells using its murine counterpart. However, Fcrl6 expression was absent in developing, mature or activated mouse T cells. Therefore, we generated a human FCRL6 (hFCRL6) expressing transgenic mouse to investigate the function of this receptor. The expression of HLA-DR on antigen presenting cells resulted in reduced in vitro proliferation of hFCRL6+ CD4+ T cells. However, we were unable to recapitulate this inhibitory effect of the hFCRL6:HLA-DR axis in an in vivo environment. Further characterisation of mouse immune populations revealed Fcrl6 expression in natural killer (NK) and developing B cells. Analysis of Fcrl6-/- mice showed that the development of these cells as well as T cells and the splenic proportion of most major immune populations remained unaffected by FCRL6 deletion. We observed a reduction in the proportion of splenic macrophages and NK cells in Fcrl6-/- and NK conditional Fcrl6-/- mice, respectively. However, NK cell responses in a chronic retroviral infection model as well as a tumour model remained unaffected by FCRL6 deletion. Similarly, B cell antibody production in response to retroviral infection was also unaffected by FCRL6 deletion. Overall, data obtained here suggested that the mouse ortholog of FCRL6 might not possess the potential immunoregulatory capacity of its human counterpart and thus may have evolved to mediate non-immunoregulatory functions. Further studies will be required to define the role of mouse FCRL6 in NK cells and macrophages in addition to hFCRL6 in NK cells and T cells

Investigating Alternative Synthesis and Impregnation Methods for Silver Nanoparticles

As more traditional antibacterials are used, the number and frequency of multi-drug resistant bacteria increase. This rise in MDR bacteria is seen in many cases around the world, especially in developing countries. Many of these MDR bacterial strains originate and spread via hospitals and equipment. A possible solution is to sanitize the hospital equipment and surfaces frequently. Silver nanoparticles is a beneficial tool that can be used for disinfection of multidrug resistant bacteria. Due to its cost and negative environmental impacts, AgNPs have not been viable for mass production. AgNPs can be attached onto surfaces for a prolonged effect, thus maintaining sanitary environments. This study aims to investigate eco-friendly methods for AgNPs synthesis and impregnation. Two different variables will be tested, the stabilizing and reducing agent, NLE, OLE, and TSC, and the impregnation type, heat via iron (HI), and microwave irradiation (MI). It is hypothesized that the Azadirachta indica leaf extract used to create the AgNPs will produce a more effective solution compared to Olea europaea leaf extract, due to differences in molecular makeup, and that the MI will produce a stronger attachment between the disc and AgNPs, due to a higher energy. A Kruskal Wallis Test H, followed by an adjusted Dunn\u27s post-hoc. The Kruskal-Wallis H test results, (H=56.72, 8 d.f., p=\u3c0.01), showed that there were differences in the test groups, thus indicating to the alternative hypothesis. It is concluded that NLE along with HI are possible alternatives to traditional methods

Deep Learning-Based Object Detection in Wound Images

Developing a deep neural network for wound localization was the first step towards an efficient and fully automated wound healing system. A wound localizer was developed in this research using the YOLOv3 model, and an iOS mobile app was also created with the developed localization algorithm. The developed system can detect the wound and its surrounding tissue and isolate the portion of the localized wound for future care. This will support the segmentation and classification of wound by eliminating a lot of redundant details from photos of wound. A lighter variant of YOLOv3 called tiny-YOLOv3 is used for mobile device video processing. The model is trained and tested on an independently created dataset, designed in collaboration with AZH Wound and Vascular Center, Milwaukee, Wisconsin. Model YOLOv3 is contrasted with model SSD, showing that YOLOv3 gives 93.9% of the mAP value, which is much better than the SSD model (86.4%). These models’ robustness and reliability are shown to be very good when evaluated on a dataset that is publicly available

RL Boltzmann Generators for Conformer Generation in Data-Sparse Environments

The generation of conformers has been a long-standing interest to structural chemists and biologists alike. A subset of proteins known as intrinsically disordered proteins (IDPs) fail to exhibit a fixed structure and, therefore, must also be studied in this light of conformer generation. Unlike in the small molecule setting, ground truth data are sparse in the IDP setting, undermining many existing conformer generation methods that rely on such data for training. Boltzmann generators, trained solely on the energy function, serve as an alternative but display a mode collapse that similarly preclude their direct application to IDPs. We investigate the potential of training an RL Boltzmann generator against a closely related "Gibbs score," and demonstrate that conformer coverage does not track well with such training. This suggests that the inadequacy of solely training against the energy is independent of the modeling modalityComment: Accepted to the NeurIPS 2022 Workshop on Machine Learning in Structural Biolog