To Move or Not To Move: The Economics of Cloud Computing

Abstract Cloud-based hosting promises cost advantages over conventional in-house (on-premise) application deployment. One important question when considering a move to the cloud is whether it makes sense for 'my' application to migrate to the cloud. This question is challenging to answer due to following reasons. Although many potential benefits of migrating to the cloud can be enumerated, some benefits may not apply to my application. Also, there can be multiple ways in which an application might make use of the facilities offered by cloud providers. Answering these questions requires an in-depth understanding of the cost implications of all the possible choices specific to 'my' circumstances. In this study We identify an initial set of key factors affecting the costs of a deployement choice. Using benchmarks representing two different applications (TPC-W and TPC-E) we investigate the evolution of costs for different deployment choices. We show that application characteristics such as workload intensity, growth rate, storage capacity and software licensing costs produce complex combined effect on overall costs. We also discuss issues regarding workload variance and horizontal partitioning

Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis

Correction: vol 7, 13205, 2016, doi:10.1038/ncomms13205Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in Bone-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2) = 0.18, P value = 0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.Peer reviewe