Cloud Computing in Life Sciences R&D Ken Rubenstein, PhD

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1 Cloud Computing in Life Sciences R&D Ken Rubenstein, PhD InsightPharmaReports.com

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3 Cloud Computing in Life Sciences R&D by Ken Rubenstein, PhD Published in April 2010 by Cambridge Healthtech Institute Reproduction prohibited i

4 Insight Pharma Reports is a division of Cambridge Healthtech Institute, a world leader in life science information and analysis through conferences, research reports, and targeted publications. Insight Pharma Reports focus on pharmaceutical R&D the technologies, the companies, the markets, and the strategic business impacts. They regularly feature interviews with key opinion leaders; surveys of the activities, views, and plans of individuals in industry and nonprofit research; and substantive assessments of technologies and markets. Managers at the top 50 pharma companies, the top 100 biopharma companies, and the top 50 vendors of tools and services rely on Insight Pharma Reports as a trusted source of balanced and timely information. Related Reports Next-Generation Sequencing: Solving the Genome by Ken Rubenstein, PhD Bioinformatics and Computational Biology: Bottlenecks and Options by K. John Morrow, Jr., PhD General Manager: Editorial Operations Director: Design Director: Production Director: Marketing Manager: Customer Service: Global Licenses: Corporate Subscriptions: Alfred R. Doig, Jr , adoig@healthtech.com Laurie Sullivan , lsullivan@healthtech.com Tom Norton , tnorton@healthtech.com Ann Handy , ahandy@healthtech.com James Prudhomme , jprudhomme@healthtech.com Rose LaRaia , rlaraia@healthtech.com Jack Valeri , jvaleri@healthtech.com David Cunningham , cunningham@healthtech.com Insight Pharma Reports, 250 First Ave., Suite 300, Needham, MA ii Reproduction prohibited

5 Cloud Computing in Life Sciences R&D by Ken Rubenstein, PhD About the Author Ken Rubenstein, PhD, a biochemist and molecular biologist, received his PhD at the University of Wisconsin and postdoctoral training at the University of Pennsylvania School of Medicine. He was a key innovator and research manager for Syva Company, the diagnostics branch of Syntex Corporation. During his 13 years with Syva, Dr. Rubenstein became vice president, scientific affairs, a function that included strategic planning. Since 1983, he has served as a technology and marketing consultant to biomedical companies and an industry analyst, with more than 40 published studies to his credit. For more information about published Insight Pharma Reports, visit or call Rose LaRaia at A Cambridge Healthtech Institute publication 2010 by Cambridge Healthtech Institute (CHI). This report cannot be duplicated without prior written permission from CHI. Every effort is made to ensure the accuracy of the information presented in Insight Pharma Reports. Much of this information comes from public sources or directly from company representatives. We do not assume any liability for the accuracy or completeness of this information or for the opinions presented. Cambridge Healthtech Institute, 250 First Ave., Suite 300, Needham, MA Phone: Fax: Reproduction prohibited iii

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7 Executive Summary Although Web-hosted applications are not particularly new, during the past few years they have morphed into what is now called cloud computing, which can arguably be considered a major paradigm shift for informatics. Early big iron computation was highly centralized with units in relatively few locations. As these early behemoths evolved into minicomputers and, later, personal computers, informatics became increasingly decentralized. The rise of cloud computing has migrated computation back toward infrastructure centralization, with large clusters of commodity hardware in relatively few physical locations. Early cloud-like applications centered on , relatively simple productivity software, merchandizing, and social networking. In the past few years, several companies, led by Amazon Web Services, have made it possible to run more complex applications in the cloud, including some of great interest to life sciences R&D. This report was motivated by the rapidly growing importance of cloud computing in dealing with the deluge of data raining down on life science R&D organizations from several sources, notably next-generation DNA sequencing systems and -omics tools. At the same time, demand for computationally complex modeling and simulation studies continues to rise dramatically. Limited funding and budgets make it difficult for many organizations to build the infrastructure necessary to keep pace with these demands, and cloud computing offers what appears to many as an attractive alternative to in-house expansion. Following a brief introduction, Chapter 2 of this report covers the evolution of cloud computing and explores the underlying concepts that provide context for deeper understanding of the subject. Chapter 3 focuses on technological aspects of cloud computing as it exists today, and describes the activities of companies active in providing cloud services and related software. The fourth chapter turns to exploration of current and emerging applications of cloud computing. Chapter 5 focuses on market aspects of cloud computing, and includes results from an extensive survey of bioinformatics people concerning their practices and views on the subject. The sixth chapter contains transcripts of interviews with six individuals who have extensive knowledge in the field. Extracts from these interviews have been inserted into the body of the report in their proper context. The final chapter provides general observations and conclusions. Reproduction prohibited v

8 Executive Summary Technology Cloud computing is, arguably, less a technological advance than it is a new business model. The evolution of the subject can be traced back to the early days of computing when time-sharing permitted a number of users to simultaneously tap into centralized hardware. Computer clustering, which came into vogue starting in the 1960s, involves groups of computers linked in networks to emulate a single computer. The clustering concept eventually evolved into the Internet and also morphed into grid computing, which links computers at multiple sites, enabling them to perform a common task. Yet another important underlying concept, virtual computing, enables creation of a simulated computer environment within a given computer or network (e.g., emulating a PC environment on an Apple computer). An important cloud-related development in the software realm came from Google, which developed MapReduce, a program that permits large datasets to be broken into small segments. These can be spread among large numbers of computers without interfering with users ability to query and receive cohesive answers. An open-source adaptation, Hadoop, is currently a key element in bringing cloud computing to the life science sector. Cloud computing actually has diverse definitions, depending on who is doing the defining. For our purposes, it is sufficient to define the concept in terms of features that are commonly associated with the subject by users and observers. 1 These features are resource outsourcing, utility computing, large collections of inexpensive machines, automated resource management, virtualization, and parallel computing. Public clouds offer utility computing in much the same sense that energy companies provide electricity: You pay for what you use. Anyone with Web access and a credit card can order the hardware and software needed to process or store their data, and release them back to the cloud when no longer needed. Given lingering concerns over data security, large companies may choose to implement a private cloud, one that provides many of the advantages of the cloud model via infrastructure contained within their firewall. A third model, the hybrid cloud, allows companies to keep key data within their firewall while extending selective activities out to public clouds. Cloud services divide into four main categories. IaaS (infrastructure-as-a-service), which embodies the essence of cloud computing, allows customers to fully outsource provision of servers, software, data center space, and/or network equipment. PaaS (platform-as-a-service), also known as cloudware, offers a hosted computing platform that allows customers to deploy applications without having to buy and manage the required hardware and underlying software layers. Typically, PaaS provides customers with everything needed to build and deliver cloud-based applications and services. SaaS (software-as-a-service), which originated around the turn of the century, refers to software licensed by a provider to customers on either a contractual or utility basis. The software may reside on the provider s network and get accessed via the Web, or be downloaded to the customer s system and disabled when the contracted use period expires. The fourth main service, cloud storage, employs commodity hardware linked by software to appear as a single storage device. vi Reproduction prohibited

9 Cloud Computing in Life Sciences R&D All major companies that provide computer hardware, software, or both are involved to some degree in cloud computing. Yet the pioneer and overwhelming market leader in the field is Amazon Web Services. The breadth of their service offerings and attractiveness of their pricing structure have made them a prime cloud destination for life science organizations today. Amazon EC2 (Elastic Cloud Compute) allows customers to rent servers on which they can create virtual machines that run their own applications. They offer persistent storage via the Simple Storage Service (S3) and the more elaborate Relational Database Service (RDS). A number of additional services extend the capabilities of these basic ones. An interesting entry, the Amazon Virtual Private Cloud (VPC), provides a bridge between an organization s existing IT infrastructure and the Amazon cloud. VPC allows enterprises to connect their infrastructure to a set of isolated Amazon computational resources via a Virtual Private Network (VPN) connection. Pfizer has opted to go this route. Other large organizations currently compete with Amazon or have positioned themselves for future attempts to capture cloud market share. Google participates in cloud computing via its App Engine platform, which became available to customers in April App Engine provides an environment that permits developers to build new Web applications, generate code, access compute resources, and store data on virtual machines. In October 2008, Microsoft announced its cloud-based operating system, Windows Azure, along with Azure Services, which will permit developers to build and run applications hosted on Microsoft s rapidly growing server collections. In December 2009, Microsoft announced formation of a new internal organization, the Server and Cloud Division, which combines the former Windows Server and Solutions group with the Windows Azure unit. Hewlett-Packard (HP) sells hardware to cloud services providers and offers varied cloud consulting services to customers, with heavy emphasis on security and risk management. HP Cloud Assure consists of HP services and software, including HP Application Security Center, HP Performance Center, and HP Business Availability Center. The services are delivered to customers via the HP Software-as-a-Service facility. IBM is focused mainly on providing the enterprise market with public cloud services specific to a company s workload, hardware for use at the customer site, and consulting/systems integration services to aid customers in building private and hybrid clouds. IBM also has 13 cloud computing centers to enable enterprises, government agencies, and researchers to design, develop, and test applications for use in cloud environments. A number of other large companies, such as AT&T, Yahoo, Sun, and Verizon, are involved in developing and/or providing cloud computing services. A number of smaller companies provide middleware and cloud services to augment and extend large company offerings. Cloud computing has generated a great deal of buzz in the venture capital community as having great upside potential. A number of new companies have formed recently, and the list looks like it will keep growing for the next few years. Following is a brief look at some of the smaller companies, especially those of interest to life science R&D. Reproduction prohibited vii

10 Executive Summary BioTeam and Cloudera provide extensive consulting services to assist organizations in entering cloud computing. Cycle Computing features CycleCloud, a scheduling service for cloud computing. Darkstrand addresses the high-speed networking needs of high-performance computing based cloud computing. GenoLogics focuses its collaborative data-management software platform on biomedical and drug discovery/development applications in the cloud, with emphasis on translational medicine and systems biology in pharma, biotechnology companies, and academic organizations. GenomeQuest provides a cloud computing environment that allows researchers to perform sequence alignment and data mining on next-generation sequencing data. Geospiza develops and sells enterpriseclass software systems for workflow management of genetic analysis. Their GeneSifter Analysis Edition provides end-to-end capability for data-intensive genetic analysis applications including microarrays and next-generation sequencing based transcription. In collaboration with Applied Biosystems, Geospiza now offers GeneSifter for next-generation sequencing in the cloud through Amazon Web Services. Nirvanix is a cloud storage company. Their Storage Delivery Network is a fully managed, highly secure service powered by patent-pending, proprietary technology and infrastructure. ParaScale provides enterprise-level cloud storage resources under the names Hyper-Scale Storage Cloud and Hi-Performance Storage Cloud. The company provides software that can be downloaded and installed on commodity hardware running standard Linux to create a storage cloud. Penguin Computing offers POD (Penguin on Demand), a cloud computing service dedicated specifically to high-performance computing (HPC). Platform Computing specializes in private cloud systems for high-performance computing. RightScale offers a fully automated management software platform that enables cloud computing while maintaining IT control and transparency. Univa UD is a privately held company, founded in 2004, that provides cloud computing management software to a broad array of customers, including data centers and HPC organizations. Cloud computing is still in its early days, and most life science organizations are still proceeding with caution to test feasibility and determine what kinds of applications run best in that mode. Yet, driven by continual acceleration in the rates of data generation and the desire for processor-intensive applications, these organizations continue to increase their cloud utilization and the diversity of applications they run there. Applications Several applications areas appear particularly suitable for cloud computing. Among these are next-generation DNA sequencing, protein docking, modeling and simulation, and data mining. Areas impacted by these applications include drug discovery, personalized medicine, translational medicine, and personal genomics. Given bullish signals for the future of cloud computing in life sciences, we can expect the number and diversity of applications to increase markedly over the next five years. viii Reproduction prohibited

11 Cloud Computing in Life Sciences R&D Much of the interest and early activity in cloud computing for life sciences centers on next-generation sequencing. The quantities of data next-generation sequencers generate are staggering, demands for data processing suffer large peaks and valleys, and rapid growth in utilization requires new investment in infrastructure, especially for smaller labs just entering the field. The rapidly decreasing cost of obtaining sequence data has led to continuing increases in the number of instruments in use and the number of labs using them. Each of these factors aligns with the benefits of cloud computing. A recent paper points out that a single Illumina instrument can generate billion bases of sequence data per run, and by the end of 2009 the figure was expected to increase to billion bases. It is important to note that next-generation sequencing already has bioinformaticians stretched near their limits in keeping up with sequence data itself and with the needs of biologists to convert that data to useful information, which itself creates further computing needs. By the end of 2010, the first thirdgeneration sequencing systems will enter the market. They promise to provide a step up in the rate of data generation, but also may in some instances generate qualitatively different kinds of data, which will present still new informatics challenges. Data storage requirements for next-generation sequencers have been alleviated to some extent by manufacturers who are increasingly processing mounds of raw image data in real time in their machines, and providing users with smaller quantities of processed sequence data to feed their analysis pipelines. Although storage remains a critical issue for sequencers, deriving useful scientific information from sequence is the major challenge at present. Offerings from sequencing systems providers are limited, and users either develop their own software for this purpose, turn to open-source solutions such as the University of Maryland s CloudBurst system for SNP-finding, or access commercial solutions such as those from GenomeQuest and Geospiza. Security is less of an issue in next-generation sequencing than is data transfer, which many organizations still do by physically delivering disks to service providers. Docking studies done in silico have come to play an increasingly important role in both basic research and rational drug design. Several big pharmas are currently doing docking in the cloud. For example, Pfizer s Biotherapeutics and Bioinnovation Center has successfully experimented with protein-protein docking using Amazon Web Services. Other key areas with cloud potential include -omics data analysis and general modeling and simulations. Personalized medicine and clinical studies may well benefit from cloud computing in coming years. Naturally, security issues take greater significance in these areas, especially clinical development, where regulatory concerns must be addressed. Markets For this report, we divide the cloud services user market into three main segments: large pharmaceutical and biotechnology companies; small to medium-sized pharmaceutical and biotechnology companies; and academic and institutional non-profit organizations. Certainly, organizations in all three categories face budgetary challenges in these troubled and uncertain times. Consequently, the cloud computing business model has great appeal across the board. Large pharmaceutical and biotechnology companies are currently experimenting with running a variety of research applications in the cloud. Many are trying to Reproduction prohibited ix

12 Executive Summary identify possible downsides and determine actual cost-effectiveness compared to alternative approaches. Although large companies already have large-scale bioinformatics infrastructure already in place, they have also been shedding R&D staff at a high rate in recent years and relying increasingly on outsourced services. Small to medium-sized pharmaceutical and biotechnology companies, especially those that rely heavily on data-rich next-generation sequencing and -omics technologies for drug discovery and personalized medicine, are key candidates for cloud computing services. Given the relative scarcity of funding in the current economic climate, which forces the need to minimize burn rates, cloud computing becomes especially attractive. Although computing requirements are considerably less than those of larger companies, it appears that growth in cloud utilization is considerably faster in the smaller organizations. The non-profit academic and institutional sector falls into two sub-segments. Large genomic centers and core laboratories, on the one hand, have considerable bioinformatics infrastructure already in place and, like big pharma, are less inclined to become major early users of cloud services. Smaller labs or departments with only a few instruments and little centralized infrastructure are more likely to become early adopters. Amazon Web Services (AWS) is overwhelmingly the market leader for cloud computing in general, as well as for the life science R&D sector. A report in July 2009 estimated AWS annual sales at $250 million to $500 million, and the company itself indicates that its customer base has been growing at 10% per quarter. A number of other large players have the potential to capture at least modest market share from Amazon in coming years. IBM, Google, and Microsoft stand out as candidates. A host of smaller middleware companies stand to fill in the holes in larger company offerings, and these organizations will no doubt continue to expand their cloud-related efforts as the field grows. Conclusions Based on information presented in this report, we offer the following observations and conclusions: Cloud computing for life sciences R&D is growing rapidly but is still in its infancy, with most organizations still in testing-the-water mode. Next-generation DNA sequencing, by virtue of its Moore s law-style growth in data volumes, is the single most important applications area for operation in the cloud. Soon-tobe-released third-generation systems up the data deluge ante even more. As raw DNA sequence becomes ever less expensive, requirements to derive useful information from the data continue to grow along with the associated costs. The utility nature and scalability of the cloud favor its adoption for this purpose. x Reproduction prohibited

13 Cloud Computing in Life Sciences R&D Amazon Web Services has put together a highly impressive package of cloud service offerings for life science R&D with a very attractive pricing structure. Competitors will be hard-pressed to capture significant market share, at least in the short term. Amazon s successes in cloud computing have enabled significant market opportunities for smaller middleware companies to serve special needs of particular user segments. Academic life science researchers are particularly interested in conducting computationally intensive modeling and simulation studies in the cloud. We can expect major growth in this sector in the next several years. Although many bioinformatics people believe that data security is no more at risk, and possibly less so, in the cloud than in-house, large commercial organizations are moving cautiously to protect mission-critical data and intellectual property. Pfizer s adoption of the Amazon Virtual Private Cloud, which permits a company to extend its firewall and other security measures to the cloud, albeit at some cost in operating efficiency, exemplifies the issue. Smaller organizations lacking adequate computational infrastructure to meet current and future needs are the best early candidates for extensive participation in the cloud. However, recent shifts in the basic nature of big pharma R&D favor forthcoming extension to large-scale participation. Based on our survey results, a minority of potential customers either use cloud services routinely or are currently testing the feasibility of doing so. More either plan to enter the cloud or are considering such a plan. Virtually none of our respondents thought their organizations would most likely not get involved with cloud services. Both commercial and academic respondents to the survey offered scalability and collaboration as primary motivations for cloud computing, but the academic sector is most interested in avoiding the purchase of new hardware. Given a choice of five classes of cloud services, commercial users were most interested in workflow management and least interested in software-as-a-service, whereas academic users were least interested in the former and most in the latter. Storage was not among the top selections for either sector. Given a choice among preference for public, private, and hybrid clouds, both sectors expressed significant interest in all three, although commercial users gave most support to hybrid clouds while academics gave top preference to public clouds. Given a selection of areas of possible concern over cloud services, both sectors are most concerned over data security (commercial users more so), and are next-most worried about reliability of cloud systems. Regarding applications for cloud computing, commercial users gave a strong nod to nextgeneration sequencing, which was also a strong preference among academics, although their top choice was modeling and simulation. Reproduction prohibited xi

14 Executive Summary Both commercial and academic respondents anticipate major growth in data processing and storage requirements during the next year, and even more so during the next three years; 3 5-fold increase predictions were not uncommon. The proportion of bioinformatics budgets devoted to cloud computing in both sectors will grow continually and strongly during the next three years. Both commercial and academic users consider cloud computing to be a major paradigm shift for bioinformatics, but also an evolutionary step consistent with trends to increased outsourcing. When asked their belief about Amazon s continuing leadership in cloud computing, most respondents in both sectors chose perhaps over agree or disagree. Survey respondents gave Google and Microsoft a better chance than HP or IBM in giving Amazon a run for its money. xii Reproduction prohibited

15 Table of Contents Chapter 1 Introduction Factors Driving Use of Cloud Computing in Life Sciences R&D Goals and Organization of the Report... 2 Chapter 2 Evolution of Cloud Computing and Technical Background Definition of Cloud Computing Evolution of Cloud Computing... 4 Computer Clusters Key Concepts... 6 High-Performance Computing (HPC)... 6 Virtual Computing... 6 Grid Computing... 7 Utility Computing... 8 MapReduce and Hadoop... 8 Chapter 3 Technology Cloud Models Services Software-as-a-Service Platform-as-a-Service Infrastructure-as-a-Service Cloud Storage Security The Cloud Definition Revisited Approaches of Cloud Computing Providers Approaches of Major Players Amazon Web Services Google Reproduction prohibited xiii

16 Table of Contents Chapter 4 Microsoft Hewlett-Packard (HP) IBM Others Approaches of Small Companies and Specialty Players Cloudera Cycle Computing Darkstrand EMC GenoLogics GenomeQuest Geospiza Gridcore Nirvanix Ocarina Networks NVIDIA ParaScale Penguin Computing Platform Computing RightScale Univa UD Applications Next-Generation Sequencing Docking Studies Omics Data Analysis Personalized Medicine Clinical Studies Chapter 5 Market Dynamics Market Segmentation Service Providers User Survey Respondents and Their Organizations Nature of Respondents Work Activities Involvement in Cloud Computing Reasons for Interest in Cloud Computing Cloud Services of Greatest Interest Cloud-Type Preferences Reasons for Concern over Public and Hybrid Clouds Applications Which Respondents Use or Supervise Likelihood That Selected Applications Will Be Run in the Cloud Anticipated Increases in Data Processing and Storage Requirements Current and Projected Budgets for Life Sciences R&D Cloud Computing xiv Reproduction prohibited

17 Cloud Computing in Life Sciences R&D Chapter 6 User Perceptions about Cloud Computing User Perceptions about Companies Involved in Cloud Computing Interview Transcripts Steven Muskal, PhD, Chief Executive Officer, Eidogen-Sertanty David Dooling, PhD, Assistant Director, Informatics, The Genome Center at Washington University in St. Louis Giles Day, Senior Director, Biotherapeutics Informatics, Pfizer Todd Smith, PhD, Chief Science Officer, Geospiza Ronald Ranauro, President and Chief Executive Officer, Director and Tony Flynn, Chief Marketing Officer, GenomeQuest Michael Schatz, Research Assistant, University of Maryland, Center for Bioinformatics and Computational Biology Chapter 7 General Observations and Conclusions Issues Cost Moving Data Into and Out of the Cloud Security The Future Third-Generation Technologies Other Perspectives on the Future Observations and Conclusions References Company Index with Web Addresses Survey Exhibits Exhibit 5.1. Cloud Computing Involvement of Commercial Respondents Exhibit 5.3. Reasons for Interest in Cloud Computing, Commercial Sector Exhibit 5.4. Reasons for Interest in Cloud Computing, Academic Sector Exhibit 5.5. Main Type of Cloud-Related Service Interest, Commercial Sector Exhibit 5.6. Main Type of Cloud-Related Service Interest, Academic Sector Exhibit 5.7. Cloud-Type Interests, Commercial Sector Exhibit 5.8. Cloud-Type Interests, Academic Sector Exhibit 5.9. Reasons for Concern Over Public and Hybrid Clouds, Commercial Sector Exhibit Reasons for Concern Over Public and Hybrid Clouds, Academic Sector Exhibit Applications Which Respondents Use or Supervise, Commercial Sector Reproduction prohibited xv

18 Table of Contents Exhibit Applications Which Respondents Use or Supervise, Academic Sector Exhibit Likelihood That Applications Will Be Run in the Cloud in Next Three Years, Commercial Sector Exhibit Likelihood That Applications Will Be Run in the Cloud in Next Three Years, Academic Sector Exhibit Anticipated Increases in Data Processing and Storage Requirements During the Next Year, Commercial Sector Exhibit Anticipated Increases in Data Processing and Storage Requirements During the Next Year, Academic Sector Exhibit Anticipated Increases in Data Processing and Storage Requirements During the Next Three Years, Commercial Sector Exhibit Anticipated Increases in Data Processing and Storage Requirements During the Next Three Years, Academic Sector Exhibit Proportion of Life Sciences R&D Informatics Budget Devoted to Cloud Computing, Currently and in the Coming Year; Commercial Sector Exhibit Proportion of Life Sciences R&D Informatics Budget Devoted to Cloud Computing in Three Years, Commercial Sector Exhibit Proportion of Life Sciences R&D Informatics Budget Devoted to Cloud Computing, Currently and in the Coming Year; Academic Sector Exhibit Proportion of Life Sciences R&D Informatics Budget Devoted to Cloud Computing in Three Years, Academic Sector Exhibit User Perceptions about Cloud Computing, Commercial Sector Exhibit User Perceptions about Cloud Computing, Academic Sector Exhibit User Views on I believe that Amazon Web Services will remain the market leader in life sciences R&D-based cloud computing for the foreseeable future, Commercial Sector Exhibit User Views on I believe that Amazon Web Services will remain the market leader in life sciences R&D-based cloud computing for the foreseeable future, Academic Sector Exhibit Competitor Ratings, Commercial Sector Exhibit Competitor Ratings, Academic Sector FIGURES Figure 4.1. Nature of the GenomeQuest Cloud-Based System for Next-Generation Sequencing Figure 4.2. Geospiza s Centralized, Internet-Based Data Center Provides IT Infrastructure and System Access to Both Labs and Users xvi Reproduction prohibited

19 Introduction These new modalities were already straining informatics capacity for universities and business organizations when next-generation DNA sequencing came on the scene several years ago with the introduction of systems from Roche, Illumina, and Applied Biosystems. These short-segment sequencing systems represented a quantum leap in the amounts of data generated and the levels of infrastructure needed to keep pace with the expectations of researchers for whom new vistas had suddenly opened. Given the new requirements at a time when funds have grown ever more scarce, it is not surprising that many organizations find cloud computing s attributes highly attractive. As for electricity, water, and other utilities, the cloud can be switched on or off at will, and users can go from using single servers to large clusters and back again with ease. Driven by Amazon s early success in providing cloud computing services to life science researchers and organizations, other companies, both large and small, have amped up their efforts to capture a piece of what may well become a very large market. As cloud computing was bursting onto the scene, pharmaceutical companies have come to rely increasingly on outsourcing in line with their own versions of flexible scalability. The intersection of the outsourcing trend and economic restrictions with the increasing attractiveness of cloud computing offerings has created a highly dynamic, yet nascent, market Goals and Organization of the Report This report was written in an effort to provide a snapshot of the life sciences R&D cloud computing environment and market, and offer some suggestions as to where they might be heading. Following this brief introduction, the second chapter covers the evolution of cloud computing via examination of its forebear technologies, and also defines many of the terms that are important for people who are working outside the informatics field to understand. Chapter 3 deals with the technological aspects of cloud computing and introduces the companies, both large and small, that are active in the field. Chapter 4 turns to applications of cloud computing with special emphasis on next-generation DNA sequencing and its ever-increasing burden of data needing to be processed and interpreted. The fifth chapter presents a view of cloud computing from the market perspective, including competition among providers and requirements of users. The chapter concludes with results and analysis from an extensive online survey of practices and views of people active in both commercial and academic aspects of bioinformatics. Following Chapter 6, which contains full transcripts of interviews we conducted for this report with six individuals knowledgeable about various aspects of cloud computing, the report concludes with a chapter on general observations, conclusions, and possible future trends. 2 Reproduction prohibited

20 Chapter 2 Evolution of Cloud Computing and Technical Background We start by defining cloud computing and exploring why it has been generating so much excitement in the information technology world in general, and the life sciences in particular. Since readers of this report may not be intimately familiar with the terminology of information technology, further definitions of terms and concepts falling within the scope of cloud computing are provided. This chapter also aims to provide perspective on the evolution of cloud computing Definition of Cloud Computing One survey paper lists 22 definitions of cloud computing, 2 albeit many with considerable overlap. In any event, it seems quite clear that the meaning of cloud computing varies in emphasis, according to who is doing the defining. A May 2009 article in Network World covers the bases quite nicely for our purposes. 3 They first refer to a definition from the Gartner Group, a world-class IT consulting organization, which considers it a style of computing in which massively scalable IT-related capabilities are provided as a service using Internet technologies to multiple external customers. They add: Clouds are marked by self-service interfaces that let customers acquire resources at any time and get rid of them the instant they are no longer needed. Put very simply, scientific or business users who want to run sophisticated applications or store very large datasets no longer have to rely on in-house computing infrastructure. They can simply use a credit card to order hardware and software capability from a cloud services vendor (at present most likely Amazon Web Services), do their work, get their results, and release the compute resources back to the cloud. Furthermore, they can do this in many instances at an extremely attractive price. It is worth pointing out that cloud computing is not a technology per se, but an approach to providing IT services that takes advantage of the growing power of servers together with virtualization technologies that combine many servers into large computing pools and divide single servers into multiple virtual machines that can be spun up and powered down at will. Reproduction prohibited 3