Serverless is a computing model where the infrastructure orchestration is managed by the service provider. Amazon Web Services (AWS) offers a serverless computing service named Lambda (or AWS Lambda). It allows deploying a function on AWS by executing this one on a serverless environment. The analysis of RNA-Seq data needs a large amount of computational resources, and it is really time-consuming. Therefore, the solutions based on serverless computing could offer a tangible benefit, being well-prepared to the parallelization. In this paper, we proposed a serverless solution for RNA-Seq data analysis, by focusing the attention on the mapping/alignment of sequencing reads to a reference genome, in that it is the more computationally expensive step within an RNA-Seq pipeline. We deployed our solution on an in-house serverless environment, useful to overcome the limits of a traditional AWS Lambda configuration. The tests have been conducted on 16 Paired-end sequencing samples, by using the Ensembl’s GRCh38 (release 84, Homo sapiens) as reference genome. Furthermore, we also estimated the probable time needed to map 50, 100, 250, 500, and 1000 samples based on our 16 ones. The proposed solution has been compared with local analysis performed by using two different workstations. We demonstrate an obvious advantage for our use-case. Results showed that a serverless solution is only suitable for highly parallel computations or for a lot of number of independent atomic operations, while it is not recommended for a single atomic operation that requires a lot amount of CPU and/or memory.

Summary: RNA-seq analysis usually requires large computing infrastructures. NGScloud is a bioinformatic system developed to analyze RNA-seq data using the cloud computing services of Amazon that permit the access to ad hoc computing infrastructure scaled according to the complexity of the experiment, so its costs and times can be optimized. The application provides a user-friendly front-end to operate Amazon's hardware resources, and to control a workflow of RNA-seq analysis oriented to non-model species, incorporating the cluster concept, which allows parallel runs of common RNA-seq analysis programs in several virtual machines for faster analysis. Availability and implementation: NGScloud is freely available at https://github.com/GGFHF/NGScloud/. A manual detailing installation and how-to-use instructions is available with the distribution.

The prosperity of cloud computing offers common infrastructures to a wide range of applications. Understanding these applications' workload behaviors is the premise of designing, managing, and optimizing cloud systems. Considering the heterogeneity and diversity of cloud workloads, for the sake of fairness, cloud benchmarks must be able to accurately replicate their behaviors in cloud systems, including both the usages of cloud resources and the micro-architectural behaviors beyond the virtualization layer. Furthermore, workloads spanning long durations are usually required to achieve representativeness in evaluation. Hence the more challenging issue is to significantly reduce the evaluation duration while still preserving their workload characteristics. This paper presents our efforts towards generating cloud workloads of diverse behaviors and reducible durations. Our benchmark tool, CloudMix, employs a repository of reducible workload blocks (RWBs) as the high level abstraction of workload behaviors, including usages of the two most important cloud resources (CPU and memory) and their pairing micro-architectural operations. CloudMix further introduces an efficient methodology to combine RWBs to synthesize and replicate diverse cloud workloads in real-world traces. The effectiveness of CloudMix is demonstrated by generating a variety of reducible workloads according to a Google cluster trace and by applying these workloads in job scheduling optimization on Hadoop YARN. The evaluation results show: (i) when the workload durations are reduced by 100 times, the replication errors of workload behaviors are smaller than 2.08%; (ii) when providing fast evaluations (workload durations are reduced by 10 to 100 times) to recommend the optimal setting in YARN job scheduling, the performance degradation in the recommended setting is just 0.69% compared to that of the actual optimal setting. CloudMix is publicly available from the project home page http://prof.ict.ac.cn/BigDataBench/multi tenancy/.

Summary In this work, a signal‐extraction algorithm pertaining to the Cherenkov Telescope Array's real‐time‐analysis pipeline has been parallelised using SSE, POSIX Threads and CUDA. Because of the observatory's constraints, the online analysis has to be conducted on site, on hardware located at the telescopes, and compels a search for efficient computing solutions to handle the huge amount of measured data. This work is framed in a series of studies which benchmark several algorithms of the real‐time‐analysis pipeline on different architectures to gain an insight into the suitability and performance of each platform.

Large-scale interactive services usually divide requests into multiple sub-requests and distribute them to a large number of server components for parallel execution. Hence the tail latency (i.e. The slowest component's latency) of these components determines the overall service latency. On a cloud platform, each component shares and competes node resources such as caches and I/O bandwidths with its co-located jobs, hence inevitably suffering from their performance interference. In this paper, we study the short-running jobs in a 12k-node Google cluster to illustrate the dynamic resource demands of these jobs, resulting in both individual components' latency variability over time and across different nodes and hence posing a major challenge to maintain low tail latency. Given this motivation, this paper introduces a dynamic and interference-aware scheduler for large-scale, parallel cloud services. At each scheduling interval, it collects workload and resource contention information of a running service, and predicts both the component latency on different nodes and the overall service performance. Based on the predicted performance, the scheduler identifies straggling components and conducts near-optimal component-node allocations to adapt to the changing workloads and performance interferences. We demonstrate that, using realistic workloads, the proposed approach achieves significant reductions in tail latency compared to the basic approach without scheduling.

Modern latency-critical online services often rely on composing results from a large number of server components. Hence the tail latency (e.g. The 99th percentile of response time), rather than the average, of these components determines the overall service performance. When hosted on a cloud environment, the components of a service typically co-locate with short batch jobs to increase machine utilizations, and share and contend resources such as caches and I/O bandwidths with them. The highly dynamic nature of batch jobs in terms of their workload types and input sizes causes continuously changing performance interference to individual components, hence leading to their latency variability and high tail latency. However, existing techniques either ignore such fine-grained component latency variability when managing service performance, or rely on executing redundant requests to reduce the tail latency, which adversely deteriorate the service performance when load gets heavier. In this paper, we propose PCS, a predictive and component-level scheduling framework to reduce tail latency for large-scale, parallel online services. It uses an analytical performance model to simultaneously predict the component latency and the overall service performance on different nodes. Based on the predicted performance, the scheduler identifies straggling components and conducts near-optimal component-node allocations to adapt to the changing performance interferences from batch jobs. We demonstrate that, using realistic workloads, the proposed scheduler reduces the component tail latency by an average of 67.05% and the average overall service latency by 64.16% compared with the state-of-the-art techniques on reducing tail latency.

SUMMARY The complexity of computing systems introduces a few issues and challenges such as poor performance and high energy consumption. In this paper, we first define and model resource contention metric for high performance computing workloads as a performance metric in scheduling algorithms and systems at the highest level of resource management stack to address the main issues in computing systems. Second, we propose a novel autonomic resource contention‐aware scheduling approach architected on various layers of the resource management stack. We establish the relationship between distributed resource management layers in order to optimize resource contention metric. The simulation results confirm the novelty of our approach.Copyright © 2013 John Wiley & Sons, Ltd.

In this paper, several versions of a signal extraction algorithm, pertaining to the entry stage of the Cherenkov Telescope Array's Real Time Analysis pipeline, were implemented and optimised using SSE2, POSIX threads and CUDA. Results of this proof of concept let us gain an insight into the suitability of each platform, and the performance each one can deliver, to carry out this particular task.

SUMMARY Virtual Screening (VS) methods can considerably aid drug discovery research, predicting how ligands interact with drug targets. BINDSURF is an efficient and fast blind VS methodology for the determination of protein binding sites, depending on the ligand, using the massively parallel architecture of graphics processing units(GPUs) for fast unbiased prescreening of large ligand databases. In this contribution, we provide a performance/cost model for the execution of this application on both local system and public cloud infrastructures. With our model, it is possible to determine which is the best infrastructure to use in terms of execution time and costs for any given problem to be solved by BINDSURF. Conclusions obtained from our study can be extrapolated to other GPU‐based VS methodologies.Copyright © 2013 John Wiley & Sons, Ltd.

The cloud computing paradigm provides the "illusion" of infinite resources and, therefore, becomes a promising candidate for large-scale data-intensive computing. In this paper, we explore experiment-driven performance models for data-intensive workloads executing in an infrastructure-as-a-service (IaaS) public cloud. The performance models help in predicting the workload behaviour, and serve as a key component of a larger framework for resource provisioning in the cloud. We determine a suitable prediction technique after comparing popular regression methods. We also enumerate the variables that impact variance in the workload performance in a public cloud. Finally, we build a performance model for a multi-tenant data service in the Amazon cloud. We find that a linear classifier is sufficient in most cases. On a few occasions, a linear classifier is unsuitable and non-linear modeling is required, which is time consuming. Consequently, we recommend that a linear classifier be used in training the performance model in the first instance. If the resulting model is unsatisfactory, then non-linear modeling can be carried out in the next step.

This article gathers the foundational premises of the MEDIANET project as well as intermediate results obtained along its firsts two years. MEDIANET is a Spanish project founded by the Comunidad de Madrid government, which strives for a significant scientific advance in the future media Internet where important advances are necessary to allow end-users to perceive a good quality of experience. The network technologies objectives consist of the definition and validation of new proposals for the efficient transport of high bandwidth, real-time data flows in a decentralized way where the network provides mechanisms to seamlessly request and configure devices to increase the quality of experience perceived by end-users. Furthermore, new experiences with layer 2 networks and a cross-layer design will be tested with high bandwidth demanding media services. An important objective is to develop, evaluate in depth and implement on diverse platforms, a new low latency transparent bridge protocol based in on-demand path set up, suitable for campus and data center networks. The global result will be an integrated and independent advancement in future media Internet protocols, algorithms, switching architectures and standards.

Cloud computing, with its support for elastic resources that are available on an on-demand, pay-as-you-go basis, is an attractive platform for hosting Web-based services that have variable demand, yet consistent performance requirements. Effective service management is mandatory in order for services running in the cloud, which we call elastic services, to be cost-effective. In this paper we describe a management framework to facilitate elasticity of resource consumption by services in the cloud. We extend our framework for services management with the necessary concepts and properties to support elastic services. A prototype implementation is described.

Grid Computing has proven to be a solution for big workflow execution, especially in Bioinformatics. However, Grid nature itself introduces overheads that make its use in many cases an unfeasible solution if considering wall-time. Different heuristics such as list scheduling, agglomeration and replication are available for optimizing workflow execution. In particular, the replication heuristics have been previously used in heterogeneous environments with good results. In this work, we analyze their use for workflow scheduling on Grid infrastructures. In particular, we study its applications to an intree workflow, generated by the distribution of the CD-HIT application. The experiments were conducted on a testbed made of resources from two different grids and results show a significant reduction of the workflow execution time.

Bioinformatics is demanding more computational resources day after day. The problems proposed by this area are growing in such complexity that traditional computing systems are not able to face them. For solving complex problems which can be divided in tasks with dependencies, a workflow management system must be employed. In this paper, we introduce the use of the workflow management of the GridWay metascheduler for running a Bioinformatics application which implements a complex algorithm performing protein clustering in order to obtain non-redundant protein databases. The use of a general purpose meta-scheduling system will provide the application the fault-tolerance and advance scheduling capabilities needed to execute on a highly dynamic, heterogeneous and faulty environment. The execution results on a production Grid (the EGEE infrastructure) shows the dramatic impact of remote queue waiting times on the application performance; and the critical need of efficient re-scheduling capabilities.