Abstract-Recently introduced spot instances in the AmazonElastic Compute Cloud (EC2) offer lower resource costs in exchange for reduced reliability; these instances can be revoked abruptly due to price and demand fluctuations. Mechanisms and tools that deal with the cost-reliability trade-offs under this schema are of great value for users seeking to lessen their costs while maintaining high reliability. We study how one such a mechanism, namely checkpointing, can be used to minimize the cost and volatility of resource provisioning. Based on the real price history of EC2 spot instances, we compare several adaptive checkpointing schemes in terms of monetary costs and improvement of job completion times. Trace-based simulations show that our approach can reduce significantly both price and the task completion times.

Abstract-With the recent introduction of Spot Instances in the Amazon Elastic Compute Cloud (EC2), users can bid for resources and thus control the balance of reliability versus monetary costs. A critical challenge is to determine bid prices that minimize monetary costs for a user while meeting Service Level Agreement (SLA) constraints (for example, sufficient resource availability to complete a computation within a desired deadline). We propose a probabilistic model for the optimization of monetary costs, performance, and reliability, given user and application requirements and dynamic conditions. Using real instance price traces and workload models, we evaluate our model and demonstrate how users should bid optimally on Spot Instances to reach different objectives with desired levels of confidence.

International audienceRecently introduced spot instances in the Amazon Elastic Compute Cloud (EC2) offer low resource costs in exchange for reduced reliability; these instances can be revoked abruptly due to price and demand fluctuations. Mechanisms and tools that deal with the cost-reliability tradeoffs under this schema are of great value for users seeking to lessen their costs while maintaining high reliability. We study how mechanisms, namely, checkpointing and migration, can be used to minimize the cost and volatility of resource provisioning. Based on the real price history of EC2 spot instances, we compare several adaptive checkpointing schemes in terms of monetary costs and improvement of job completion times. We evaluate schemes that apply predictive methods for spot prices. Furthermore, we also study how work migration can improve task completion in the midst of failures while maintaining low monetary costs. Trace-based simulations show that our schemes can reduce significantly both monetary costs and task completion times of computation on spot instance

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Phenotypic differences in drug responses have been associated with known pharmacogenomic loci, but many remain to be characterized. Therefore, we developed next-generation sequencing (NGS) panels to enable broad and unbiased inspection of genes that are involved in pharmacokinetics (PKs) and pharmacodynamics (PDs). These panels feature repetitively optimized probes to capture up to 114 PK/PD-related genes with high coverage (99.6%) and accuracy (99.9%). Sequencing of a Korean cohort (n = 376) with the panels enabled profiling of actionable variants as well as rare variants of unknown functional consequences. Notably, variants that occurred at low frequency were enriched with likely protein-damaging variants and previously unreported variants. Furthermore, in vitro evaluation of four pharmacogenes, including cytochrome P450 2C19 (CYP2C19), confirmed that many of these rare variants have considerable functional impact. The present study suggests that targeted NGS panels are readily applicable platforms to facilitate comprehensive profiling of pharmacogenes, including common but also rare variants that warrant screening for personalized medicine.