Early Observations on Performance of Google Compute Engine for Scientific Computing

Although Cloud computing emerged for business applications in industry, public Cloud services have been widely accepted and encouraged for scientific computing in academia. The recently available Google Compute Engine (GCE) is claimed to support high-performance and computationally intensive tasks, while little evaluation studies can be found to reveal GCE's scientific capabilities. Considering that fundamental performance benchmarking is the strategy of early-stage evaluation of new Cloud services, we followed the Cloud Evaluation Experiment Methodology (CEEM) to benchmark GCE and also compare it with Amazon EC2, to help understand the elementary capability of GCE for dealing with scientific problems. The experimental results and analyses show both potential advantages of, and possible threats to applying GCE to scientific computing. For example, compared to Amazon's EC2 service, GCE may better suit applications that require frequent disk operations, while it may not be ready yet for single VM-based parallel computing. Following the same evaluation methodology, different evaluators can replicate and/or supplement this fundamental evaluation of GCE. Based on the fundamental evaluation results, suitable GCE environments can be further established for case studies of solving real science problems.Comment: Proceedings of the 5th International Conference on Cloud Computing Technologies and Science (CloudCom 2013), pp. 1-8, Bristol, UK, December 2-5, 201

HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges

High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR

HEPCloud, a New Paradigm for HEP Facilities: CMS Amazon Web Services Investigation

Historically, high energy physics computing has been performed on large purpose-built computing systems. These began as single-site compute facilities, but have evolved into the distributed computing grids used today. Recently, there has been an exponential increase in the capacity and capability of commercial clouds. Cloud resources are highly virtualized and intended to be able to be flexibly deployed for a variety of computing tasks. There is a growing nterest among the cloud providers to demonstrate the capability to perform large-scale scientific computing. In this paper, we discuss results from the CMS experiment using the Fermilab HEPCloud facility, which utilized both local Fermilab resources and virtual machines in the Amazon Web Services Elastic Compute Cloud. We discuss the planning, technical challenges, and lessons learned involved in performing physics workflows on a large-scale set of virtualized resources. In addition, we will discuss the economics and operational efficiencies when executing workflows both in the cloud and on dedicated resources.Comment: 15 pages, 9 figure

High-Performance Cloud Computing: A View of Scientific Applications

Scientific computing often requires the availability of a massive number of computers for performing large scale experiments. Traditionally, these needs have been addressed by using high-performance computing solutions and installed facilities such as clusters and super computers, which are difficult to setup, maintain, and operate. Cloud computing provides scientists with a completely new model of utilizing the computing infrastructure. Compute resources, storage resources, as well as applications, can be dynamically provisioned (and integrated within the existing infrastructure) on a pay per use basis. These resources can be released when they are no more needed. Such services are often offered within the context of a Service Level Agreement (SLA), which ensure the desired Quality of Service (QoS). Aneka, an enterprise Cloud computing solution, harnesses the power of compute resources by relying on private and public Clouds and delivers to users the desired QoS. Its flexible and service based infrastructure supports multiple programming paradigms that make Aneka address a variety of different scenarios: from finance applications to computational science. As examples of scientific computing in the Cloud, we present a preliminary case study on using Aneka for the classification of gene expression data and the execution of fMRI brain imaging workflow.Comment: 13 pages, 9 figures, conference pape

A Unique Multi-Agent-Based Approach for Enhanced QoS Resource Allocation in Multi Cloud Environment while Maintaining Minimized Energy and Maximize Revenue

The use of the multi-cloud data storage in one heterogeneous service is a polynimbus cloud strategy. Cloud computing uses a pay-as-you-go model to deliver services to a variety of end users. Customers can outsource daunting tasks to cloud data centres for processing and producing results, thanks to cloud computing. Cloud computing becomes the popular IT brand that provides various on-demand services over the internet. This technology is devoted to distributing computer and software resources. The proven usefulness of workflows to enforce relevant scientific achievements is the availability of data from advanced scientific tools. Scheduling algorithms are essential in order to automate these strenuous workflows efficiently. A number of new heuristics based on a Cloud resource model have been developed. The majority of these heuristic - based address QoS issues in one or two dimensions. The cloud computing technology offers a decentralised pool of services and resources with various models that are provided to the customers across the Internet in an on-demand, continuously distributed, and pay-per-use model. The key challenge we address in this paper is to maximise revenue while maintaining a minimum consumption of energy with an enhanced QoS for resource allocation. The obtained results from proposed method when compared with the existing state of art methods observed to be novel and better

Small and Medium-Sized Enterprises’ Perceptions of the Use of Cloud Services

Although cloud computing is a rapidly evolving technology and is considered one of the key technological drivers of business digitalisation, it is still a challenge for many businesses to adopt it. Implementing the right cloud services is challenging and requires the right level of knowledge. In addition, the size of the company, its digital maturity and its financial situation are also critical factors, which are particularly relevant for small and medium-sized enterprises. Therefore, in this study, we focus on the situation of small and medium-sized enterprises regarding cloud services. To this end, we conducted qualitative research to examine the studies on cloud services, their trends, research directions, and research areas and to explore the relationship between the publications and their scientific embeddedness

Model of Formation of Ph.D. IC-competence Based on Using the Cloud Services of Scientometric Database Google Scholar

Статтю присвячено проблемі формування інформаційно-комунікаційної компетентності доктора філософії на основі використання хмарних інформаційно-аналітичних сервісів міжнародних наукометричних систем, зокрема міжнародної наукометричної пошукової системи Google Scholar. Обґрунтовано та уточнено поняття "інформаційно- комунікаційна компетентність доктора філософії". Запропоновано модель формування інформаційно-комунікаційної компетентності доктора філософії з використанням хмарних інформаційно-аналітичних сервісів системи Google Scholar у підготовці докторів філософії, яка базується на основних наукових підходах, що використовуються у навчанні дорослих, та складається з чотирьох компонентів: цільового, організаційно-технологічного, змістового та результативно-діагностичного. Виділено групи хмарних сервісів Google Scholar: інформаційно-пошукові сервіси, інформаційно-аналітичні сервіси, додаткові сервіси. Визначено актуальність використання хмарних інформаційно-аналітичних сервісів Google Scholar для інформаційно-аналітичної підтримки науково-педагогічних досліджень.This article deals with the problem of the formation of IC-competence of Ph.D. based on the using of international scientometric systems cloud services, including system Google Scholar. It is justified and specified the concept of "informative-communicative competence of Ph.D." It is defined the model of the formation of informative-communicative competence of Ph.D. through the using of cloud informative-analytical services of the system Google Scholar in training PhD which based on the main scientific approaches in teaching adults. The model consists of five components: target, organizational technological, effective- diagnostic and contents. The groups of cloud services of Google Scholar are chosen: information and search services, information and analytic services, additional services. It is determined the using of cloud informative and analytical services of Google Scholar for informative and analytical support of scientific and educational research

Effectiveness of Cloud Services for Scientific and VoD Applications

Cloud platforms have emerged as the primary data warehouse for a variety of applications, such as DropBox, iCloud, Google Music, etc. These applications allow users to store data in the cloud and access it from anywhere in the world. Commercial clouds are also well suited for providing high-end servers for rent to execute applications that require computation resources sporadically. Cloud users only pay for the time they actually use the hardware and the amount of data that is transmitted to and from the cloud, which has the potential to be more cost effective than purchasing, hosting, and maintaining dedicated hardware. In this dissertation, we look into the efficiency of the cloud Infrastructure-as-a-Service (IaaS) model for two real time high bandwidth applications: A scientific application of short-term weather forecasting and Video on Demand services. We show that, cloud services are efficient in both network and computation for real time scientific application of weather forecasting. We present a related list reordering approach, which reduces the network traffic of serving videos from VoD services and improve the efficiency of caches deployed to serve them. Also, we present transcoding policies to reduce the transcoding workload and present prediction models to maintain performance of providing ABR streaming of VoD services at the client with online transcoding in the cloud