Time for Cloud? Design and implementation of a time-based cloud resource management system

The current pay-per-use model adopted by public cloud service providers has influenced the perception on how a cloud should provide its resources to end-users, i.e. on-demand and access to an unlimited amount of resources. However, not all clouds are equal. While such provisioning models work for well-endowed public clouds, they may not always work well in private clouds with limited budget and resources such as research and education clouds. Private clouds also stand to be impacted greatly by issues such as user resource hogging and the misuse of resources for nefarious activities. These problems are usually caused by challenges such as (1) limited physical servers/ budget, (2) growing number of users and (3) the inability to gracefully and automatically relinquish resources from inactive users. Currently, cloud resource management frameworks used for private cloud setups, such as OpenStack and CloudStack, only uses the pay-per-use model as the basis when provisioning resources to users. In this paper, we propose OpenStack Café, a novel methodology adopting the concepts of 'time' and booking systems' to manage resources of private clouds. By allowing users to book resources over specific time-slots, our proposed solution can efficiently and automatically help administrators manage users' access to resource, addressing the issue of resource hogging and gracefully relinquish resources back to the pool in resource-constrained private cloud setups. Work is currently in progress to adopt Café into OpenStack as a feature, and results of our prototype show promises. We also present some insights to lessons learnt during the design and implementation of our proposed methodology in this paper

Whole-genome sequencing reveals host factors underlying critical COVID-19

Altres ajuts: Department of Health and Social Care (DHSC); Illumina; LifeArc; Medical Research Council (MRC); UKRI; Sepsis Research (the Fiona Elizabeth Agnew Trust); the Intensive Care Society, Wellcome Trust Senior Research Fellowship (223164/Z/21/Z); BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070, BBS/E/D/30002275); UKRI grants (MC_PC_20004, MC_PC_19025, MC_PC_1905, MRNO2995X/1); UK Research and Innovation (MC_PC_20029); the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z); the Edinburgh Clinical Academic Track (ECAT) programme; the National Institute for Health Research, the Wellcome Trust; the MRC; Cancer Research UK; the DHSC; NHS England; the Smilow family; the National Center for Advancing Translational Sciences of the National Institutes of Health (CTSA award number UL1TR001878); the Perelman School of Medicine at the University of Pennsylvania; National Institute on Aging (NIA U01AG009740); the National Institute on Aging (RC2 AG036495, RC4 AG039029); the Common Fund of the Office of the Director of the National Institutes of Health; NCI; NHGRI; NHLBI; NIDA; NIMH; NINDS.Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care or hospitalization after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes-including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)-in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease