A significant open issue in cloud computing is the real performance of the infrastructure. Few, if any, cloud providers or technologies offer quantitative performance guarantees. Regardless of the potential advantages of the cloud in comparison to enterprise-deployed applications, cloud infrastructures may ultimately fail if deployed applications cannot predictably meet behavioral requirements. In this paper, we present the results of comprehensive performance experiments we conducted on Windows Azure from October 2009 to February 2010. In general, we have observed good performance of the Windows Azure mechanisms, although the average 10 min VM startup time must be accounted for in application design. We also present performance and reliability observations and analysis from our deployment of a large-scale scientific application hosted on Azure, called ModisAzure, that show unusual and sporadic VM execution slowdown of over 4&#xd7; in some cases and affected up to 16&#x25; of task executions at times. In addition to a detailed performance evaluation of Windows Azure, we provide recommendations for potential users of Windows Azure based on these early observations. Although the discussion and analysis is tailored to scientific applications, the results are broadly applicable to the range of existing and future applications running in Windows Azure.

Zach Hill

Jie Li

Ming Mao

Arkaitz Ruiz-Alvarez

Marty Humphrey

Early Observations on the Performance of Windows Azure

Ivona Brandic

Ioan Raicu

Special Issue on Science-Driven Cloud Computing

We report on attempts to put an astronomical database &#x2013; the Sloan Digital Sky Survey science archive &#x2013; in the cloud. We find that it is very frustrating to impossible at this time to migrate a complex SQL Server database into current cloud service offerings such as Amazon (EC2) and Microsoft (SQL Azure). Certainly it is impossible to migrate a large database in excess of a TB, but even with (much) smaller databases, the limitations of cloud services make it very difficult to migrate the data to the cloud without making changes to the schema and settings that would degrade performance and/or make the data unusable. Preliminary performance comparisons show a large performance discrepancy with the Amazon cloud version of the SDSS database. These difficulties suggest that much work and coordination needs to occur between cloud service providers and their potential clients before science databases &#x2013; not just large ones but even smaller databases that make extensive use of advanced database features for performance and usability &#x2013; can successfully and effectively be deployed in the cloud. We describe a powerful new computational instrument that we are developing in the interim &#x2013; the Data-Scope &#x2013; that will enable fast and efficient analysis of the largest (petabyte scale) scientific datasets.

Ani Thakar

Alex Szalay

Ken Church

Andreas Terzis

Large Science Databases &#x2013; Are Cloud Services Ready for Them?

In this paper, we present a programming and runtime framework that enables the autonomic management of complex application workflows on hybrid computing infrastructures. The framework is designed to address system and application heterogeneity and dynamics to ensure that application objectives and constraints are satisfied. The need for such autonomic system and application management is becoming critical as computing infrastructures become increasingly heterogeneous, integrating different classes of resources from high-end HPC systems to commodity clusters and clouds. For example, the framework presented in this paper can be used to provision the appropriate mix of resources based on application requirements and constraints. The framework also monitors the system/application state and adapts the application and/or resources to respond to changing requirements or environment. To demonstrate the operation of the framework and to evaluate its ability, we employ a workflow used to characterize an oil reservoir executing on a hybrid infrastructure composed of TeraGrid nodes and Amazon EC2 instances of various types. Specifically, we show how different applications objectives such as acceleration, conservation and resilience can be effectively achieved while satisfying deadline and budget constraints, using an appropriate mix of dynamically provisioned resources. Our evaluations also demonstrate that public clouds can be used to complement and reinforce the scheduling and usage of traditional high performance computing infrastructure.

Hyunjoo Kim

Yaakoub el-Khamra

Ivan Rodero

Shantenu Jha

Manish Parashar

Autonomic Management of Application Workflows on Hybrid Computing Infrastructure

Today, our picture of the Universe radically differs from that of just over a decade ago. We now know that the Universe is not only expanding as Hubble discovered in 1929, but that the rate of expansion is accelerating, propelled by mysterious new physics dubbed &#x201c;Dark Energy&#x201d;. This revolutionary discovery was made by comparing the brightness of nearby Type Ia supernovae (which exploded in the past billion years) to that of much more distant ones (from up to seven billion years ago). The reliability of this comparison hinges upon a very detailed understanding of the physics of the nearby events. To further this understanding, the Nearby Supernova Factory (SNfactory) relies upon a complex pipeline of serial processes that execute various image processing algorithms in parallel on &#x7e;10 TBs of data. This pipeline traditionally runs on a local cluster. Cloud computing [Above the clouds: a Berkeley view of cloud computing, Technical Report UCB/EECS-2009-28, University of California, 2009] offers many features that make it an attractive alternative. The ability to completely control the software environment in a cloud is appealing when dealing with a community developed science pipeline with many unique library and platform requirements. In this context we study the feasibility of porting the SNfactory pipeline to the Amazon Web Services environment. Specifically we: describe the tool set we developed to manage a virtual cluster on Amazon EC2, explore the various design options available for application data placement, and offer detailed performance results and lessons learned from each of the above design options.

Keith R. Jackson

Krishna Muriki

Lavanya Ramakrishnan

Karl J. Runge

Rollin C. Thomas

Performance and Cost Analysis of the Supernova Factory on the Amazon AWS Cloud

Brown Adrian

Book Review

Text analysis tools are nowadays required to process increasingly large corpora which are often organized as small files (abstracts, news articles, etc.). Cloud computing offers a convenient, on-demand, pay-as-you-go computing environment for solving such problems. We investigate provisioning on the Amazon EC2 cloud from the user perspective, attempting to provide a scheduling strategy that is both timely and cost effective. We derive an execution plan using an empirically determined application performance model. A first goal of our performance measurements is to determine an optimal file size for our application to consume. Using the subset-sum first fit heuristic we reshape the input data by merging files in order to match as closely as possible the desired file size. This also speeds up the task of retrieving the results of our application, by having the output be less segmented. Using predictions of the performance of our application based on measurements on small data sets, we devise an execution plan that meets a user specified deadline while minimizing cost.

Gabriela Turcu

Ian Foster

Svetlozar Nestorov

Reshaping Text Data for Efficient Processing on Amazon EC2

Today, Cloud computing proposes an attractive alternative to building large-scale distributed computing environments by which resources are no longer hosted by the scientists' computational facilities, but leased from specialised data centres only when and for how long they are needed. This new class of Cloud resources raises new interesting research questions in the fields of resource management, scheduling, fault tolerance, or quality of service, requiring hundreds to thousands of experiments for finding valid solutions. To enable such research, a scalable simulation framework is typically required for early prototyping, extensive testing and validation of results before the real deployment is performed. The scope of this paper is twofold. In the first part we present GroudSim, a Grid and Cloud simulation toolkit for scientific computing based on a scalable simulation-independent discrete-event engine. GroudSim provides a comprehensive set of features for complex simulation scenarios from simple job executions on leased computing resources to file transfers, calculation of costs and background load on resources. Simulations can be parameterised and are easily extendable by probability distribution packages for failures which normally occur in complex distributed environments. Experimental results demonstrate the improved scalability of GroudSim compared to a related process-based simulation approach. In the second part, we show the use of the GroudSim simulator to analyse the problem of dynamic provisioning of Cloud resources to scientific workflows that do not benefit from sufficient Grid resources as required by their computational demands. We propose and study four strategies for provisioning and releasing Cloud resources that take into account the general leasing model encountered in today's commercial Cloud environments based on resource bulks, fuzzy descriptions and hourly payment intervals. We study the impact of our techniques to the overall execution time, overall cost and cost per unit of saved time with respect to various instance types offered by the Amazon EC2.

Simon Ostermann

Kassian Plankensteiner

Radu Prodan

Using a New Event-Based Simulation Framework for Investigating Resource Provisioning in Clouds

Cloud computing, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. To study the effects of moving parallel scientific applications onto the cloud, we deployed several benchmark applications like matrix&#x2013;vector operations and NAS parallel benchmarks, and DOUG (Domain decomposition On Unstructured Grids) on the cloud. DOUG is an open source software package for parallel iterative solution of very large sparse systems of linear equations. The detailed analysis of DOUG on the cloud showed that parallel applications benefit a lot and scale reasonable on the cloud. We could also observe the limitations of the cloud and its comparison with cluster in terms of performance. However, for efficiently running the scientific applications on the cloud infrastructure, the applications must be reduced to frameworks that can successfully exploit the cloud resources, like the MapReduce framework. Several iterative and embarrassingly parallel algorithms are reduced to the MapReduce model and their performance is measured and analyzed. The analysis showed that Hadoop MapReduce has significant problems with iterative methods, while it suits well for embarrassingly parallel algorithms. Scientific computing often uses iterative methods to solve large problems. Thus, for scientific computing on the cloud, this paper raises the necessity for better frameworks or optimizations for MapReduce.

Satish Narayana Srirama

Oleg Batrashev

Pelle Jakovits

Eero Vainikko

Scalability of Parallel Scientific Applications on the Cloud

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Scientific Programming provides a forum for research results in, and practical experience with, software engineering environments, tools, languages, and models of computation aimed specifically at supporting scientific and engineering computing.

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Chief Editor&nbsp;Professor Tramontana&nbsp;is based at the University of Catania and his research primarily concerns the areas of software engineering and distributed systems.

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Scientific Programming

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