Using Workload Prediction and Federation to Increase Cloud Utilization

The wide-spread adoption of cloud computing has changed how large-scale computing infrastructure is built and managed. Infrastructure-as-a-Service (IaaS) clouds consolidate different separate workloads onto a shared platform and provide a consistent quality of service by overprovisioning capacity. This additional capacity, however, remains idle for extended periods of time and represents a drag on system efficiency.The smaller scale of private IaaS clouds compared to public clouds exacerbates overprovisioning inefficiencies as opportunities for workload consolidation in private clouds are limited. Federation and cycle harvesting capabilities from computational grids help to improve efficiency, but to date have seen only limited adoption in the cloud due to a fundamental mismatch between the usage models of grids and clouds. Computational grids provide high throughput of queued batch jobs on a best-effort basis and enforce user priorities through dynamic job preemption, while IaaS clouds provide immediate feedback to user requests and make ahead-of-time guarantees about resource availability.We present a novel method to enable workload federation across IaaS clouds that overcomes this mismatch between grid and cloud usage models and improves system efficiency while also offering availability guarantees. We develop a new method for faster-than-realtime simulation of IaaS clouds to make predictions about system utilization and leverage this method to estimate the future availability of preemptible resources in the cloud. We then use these estimates to perform careful admission control and provide ahead-of-time bounds on the preemption probability of federated jobs executing on preemptible resources. Finally, we build an end-to-end prototype that addresses practical issues of workload federation and evaluate the prototype's efficacy using real-world traces from big data and compute-intensive production workloads

Association of Upper Gastrointestinal Surgery of Great Britain and Ireland (AUGIS)/Perioperative Quality Initiative (POQI) consensus statement on intraoperative and postoperative interventions to reduce pulmonary complications after oesophagectomy

BACKGROUND: Pulmonary complications are the most common morbidity after oesophagectomy, contributing to mortality and prolonged postoperative recovery, and have a negative impact on health-related quality of life. A variety of single or bundled interventions in the perioperative setting have been developed to reduce the incidence of pulmonary complications. Significant variation in practice exists across the UK. The aim of this modified Delphi consensus was to deliver clear evidence-based consensus recommendations regarding intraoperative and postoperative care that may reduce pulmonary complications after oesophagectomy. METHODS: With input from a multidisciplinary group of 23 experts in the perioperative management of patients undergoing surgery for oesophageal cancer, a modified Delphi method was employed. Following an initial systematic review of relevant literature, a range of anaesthetic, surgical, and postoperative care interventions were identified. These were then discussed during a two-part virtual conference. Recommendation statements were drafted, refined, and agreed by all attendees. The level of evidence supporting each statement was considered. RESULTS: Consensus was reached on 12 statements on topics including operative approach, pyloric drainage strategies, intraoperative fluid and ventilation strategies, perioperative analgesia, postoperative feeding plans, and physiotherapy interventions. Seven additional questions concerning the perioperative management of patients undergoing oesophagectomy were highlighted to guide future research. CONCLUSION: Clear consensus recommendations regarding intraoperative and postoperative interventions that may reduce pulmonary complications after oesophagectomy are presented

Widespread horse-based mobility arose around 2200 BCE in Eurasia.

Horses revolutionized human history with fast mobility1. However, the timeline between their domestication and their widespread integration as a means of transport remains contentious2-4. Here we assemble a collection of 475 ancient horse genomes to assess the period when these animals were first reshaped by human agency in Eurasia. We find that reproductive control of the modern domestic lineage emerged around 2200 BCE, through close-kin mating and shortened generation times. Reproductive control emerged following a severe domestication bottleneck starting no earlier than approximately 2700 BCE, and coincided with a sudden expansion across Eurasia that ultimately resulted in the replacement of nearly every local horse lineage. This expansion marked the rise of widespread horse-based mobility in human history, which refutes the commonly held narrative of large horse herds accompanying the massive migration of steppe peoples across Europe around 3000 BCE and earlier3,5. Finally, we detect significantly shortened generation times at Botai around 3500 BCE, a settlement from central Asia associated with corrals and a subsistence economy centred on horses6,7. This supports local horse husbandry before the rise of modern domestic bloodlines

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

MyMap als Werkzeug im GW-Unterricht. GW-Unterricht|GW-Unterricht 141|

Dieser Beitrag sowie das angeführte Unterrichtsbeispiel verfolgen verschiedene Ziele: Das sind die computergestützte Kartenerstellung, die Kartenmanipulation, die Kartenkritik und damit verbunden die Sensibilisierung zu kritischem Kartenlesen in verschiedenen Medien

Using trustworthy simulation to engineer cloud schedulers,”

Abstract-In recent years, researchers have contributed promising new techniques for allocating cloud resources in more robust, efficient, and ecologically sustainable ways. Unfortunately, the wide-spread use of these techniques in production systems has, to date, remained elusive. One reason for this is that the state of the art for investigating these innovations at scale often relies solely on model-driven simulation. Production-grade cloud software, however, demands certainty and precision for development and business planning that only comes from validating simulation against empirical observation. In this work, we take an alternative approach to facilitating cloud research and engineering in order to transition innovations to production deployment faster. In particular, we present a new methodology that complements existing model-driven simulation with platform-specific and statistically trustworthy results. We simulate systems at scales and on time frames that are testable, and then, based on the statistical validation of these simulations, investigate scenarios beyond those feasibly observable in practice. We demonstrate the approach by developing an energy-aware cloud scheduler and evaluating it using production and synthetic traces in faster than real time. Our results show that we can accurately simulate a production IaaS system, ease capacity planning, and expedite the reliable development of its components and extensions

Reducing the drop-out rate of a technical oriented course by introducing Problem Based Learning – a first concept

At the University of Applied Sciences (UAS) Technikum Wien one of the most difficult courses in the Bachelor degree program of Computer Science is “Database Systems and Database Design”. Together with “Advanced Computer Programming”, this course accounts for the high drop-out rate in the degree program. For this reason, this course was chosen for a redesign, in line with the research project QUADRO (Measures to increase quality of teaching and to reduce drop-out rates) promoted by the City of Vienna – MA 27 (EU strategy and promote economic development). As the authors have already gained experience in Problem Based Learning (PBL), they saw an opportunity to improve students’ database knowledge by changing the teaching method to Problem Based Learning (PBL). The proposed paper first explains the current situation, identifies its drawbacks and difficulties. In a second step, it describes the new method, shows the students’ feedback after the first semester and the resulting changes in the concept