Red Hat (United Kingdom)

ObjectivesThe use of cost-effectiveness methods to support policy decisions has become well established, but difficulties can arise when evaluating a new treatment that is indicated to be used in combination with an established backbone treatment. If the latter has been priced close to the decision maker’s willingness-to-pay threshold, this may mean that there is no headroom for the new treatment to demonstrate value, at any price, even if the combination is clinically effective. Without a mechanism for attributing value to component treatments within a combination therapy, the health system risks generating negative funding decisions for combinations of proven clinical benefit to patients. The aim of this work was to define a value attribution methodology, which could be used to allocate value between the components of any combination treatment. MethodsThe framework is grounded in the standard decision rules of cost-effectiveness analysis and provides solutions according to key features of the problem: perfect/imperfect information about component treatment monotherapy effects and balanced/unbalanced market power between their manufacturers. ResultsThe share of incremental value varies depending on whether there is perfect/imperfect information and balance/imbalance of market power, with some scenarios requiring the manufacturers to negotiate a share of the incremental value within a range defined by the framework. ConclusionsIt is possible to define a framework that is independent of price and focuses on benefits expressed as quality-adjusted life-year gains (and/or quality-adjusted life-year equivalents for cost savings), a standard metric used by many health technology assessment agencies to evaluate novel treatments.

This paper presents Unikernel Linux (UKL), a path toward integrating unikernel optimization techniques in Linux, a general purpose operating system. UKL adds a configuration option to Linux allowing for a single, optimized process to link with the kernel directly, and run at supervisor privilege. This UKL process does not require application source code modification, only a re-link with our, slightly modified, Linux kernel and glibc. Unmodified applications show modest performance gains out of the box, and developers can further optimize applications for more significant gains (e.g. 26% throughput improvement for Redis). UKL retains support for co-running multiple user level processes capable of communicating with the UKL process using standard IPC. UKL preserves Linux's battle-tested codebase, community, and ecosystem of tools, applications, and hardware support. UKL runs both on bare-metal and virtual servers and supports multi-core execution. The changes to the Linux kernel are modest (1250 LOC).

The COVID‐19 pandemic raised acute awareness regarding inequities and inequalities and poor clinical outcomes amongst ethnic minority groups. Studies carried out in North America, the UK and Australia have shown a relatively high burden of asthma and allergies amongst ethnic minority groups. The precise reasons underpinning the high disease burden are not well understood, but it is likely that this involves complex gene–environment interaction, behavioural and cultural elements. Poor clinical outcomes have been related to multiple factors including access to health care, engagement with healthcare professionals and concordance with advice which are affected by deprivation, literacy, cultural norms and health beliefs. It is unclear at present if allergic conditions are intrinsically more severe amongst patients from ethnic minority groups. Most evidence shaping our understanding of disease pathogenesis and clinical management is biased towards data generated from white population resident in high‐income countries. In conjunction with standards of care, it is prudent that a multi‐pronged approach towards provision of composite, culturally tailored, supportive interventions targeting demographic variables at the individual level is needed, but this requires further research and validation. In this narrative review, we provide an overview of epidemiology, sensitization patterns, poor clinical outcomes and possible factors underpinning these observations and highlight priority areas for research.

Serverless computing is a recent paradigm to program the cloud. It allows to execute user-defined functions at scale, on demand, and in a pay-per-use manner. This paper reports on adapting the Unix shell for serverless. Our software, called the serverless shell (sshell), runs shell scripts on a serverless platform much like with a regular computer. It permits to reuse an existing code base while benefiting from the massive power of serverless and paying only for the resources used. sshell is built around a small set of components that includes a new inter-process communication layer for serverless. We evaluate it in AWS Lambda using several microbenchmarks and a large-scale application. Our results show that sshell achieves comparable or better performance than a high-end server. Moreover, it can be faster and more cost-efficient than a cluster-based solution to mine large datasets.

The deployment of the components of distributed systems is now often very dynamic - server-side components are virtualised so they can be dynamically deployed on a range of platforms including public and private clouds, while users expect to be able to install clients on devices from phones to tablets. This can introduce security problems that place data at risk. This paper describes a new method for modeling the security of a distributed application and generating the set of possible deployment options that meet the overall security requirements. The model encompasses the entities that influence the security of a distributed system: data, services networks and platforms (e.g. Clouds, devices and "things"). The paper describes the method and how it can be used to answer a range of security questions, using a set of case studies including federated clouds and mobile clients.

The Arjuna transaction system began life in the mid 1980s as an academic project to examine the use of object-oriented techniques in the development of fault-tolerant distributed systems. Twenty five years later, it is an integral part of the JBoss application sever middleware from Red Hat. This journey from an academic to a commercial environment has been neither easy nor smooth but it has been interesting from many different perspectives. This paper gives an overview of this journey and discusses key lessons learned.

In June 2006, the Mortgage Industry Maintenance Organization (MISMO) began an initiative to help facilitate the electronic exchange of business rules between trading partners in the mortgage vertical. The Business Rules Exchange Workgroup (BREW) was subsequently formed to generate a charter and lead the adoption within the industry. BREW set out to create a proof-of concept (POC) to solve an often mentioned need for the exchange of rules: loan application pricing. A modified Production Rules (PR) extension to the working draft of the Rules Interchange Format (RIF) core design was developed by ILog and the well established MISMO schema was used as the ontology to share and execute a ruleset among ILOG JRules & JBOSS Rules in a distributed environment. A ruleset consisting of pricing rules were created using a decision table. ILOG Business Rules Engine(BRE) serialized these rules into a RIF ruleset. Using web services over HTTP , this ruleset was made available for consumption via web services allowing a client tool to consume the ruleset and push to a JBOSS Rules engine that had been exposed as a web service. Once the two systems shared the same ruleset, data payloads could then be sent to each rules engine for rule execution.