In a well-known fable, a group of blind men are asked to describe an elephant. Each encounters a different part of the animal and, not surprisingly, provides a different description. We see a similar degree of confusion in the IT industry today, as terms such as service-oriented architecture, grid, utility computing, on-demand, adaptive enterprise, data center automation, and virtualization are bandied about. As when listening to the blind men, it can be difficult to know what reality lies behind the words, whether and how the different pieces fit together, and what we should be doing about the animal(s) that are being described.
While the practice of outsourcing business functions such as payroll has been around for decades, its realization as online software services has only recently become popular. In the online service model, a provider develops an application and operates the servers that host it. Customers access the application over the Internet using industry-standard browsers or Web services clients. A wide range of online applications, including e-mail, human resources, business analytics, CRM (customer relationship management), and ERP (enterprise resource planning), are available.
Web services aren't just for application integration anymore. Platform and programming language independence, coupled with industry momentum, has made Web services the technology of choice for most enterprise integration projects. Their close relationship with SOA (service-oriented architecture) has also helped them gain mindshare. Consider this definition of SOA: "An architectural style whose goal is to achieve loose coupling among interacting software agents. A service is a unit of work done by a service provider to achieve desired end results for a service consumer. Both provider and consumer are roles played by software agents on behalf of their owners."
I have to admit a great measure of sympathy for the IT populace at large, when it is confronted by the barrage of hype around grid technology, particularly within the enterprise. Individual vendors have attempted to plant their flags in the notionally virgin technological territory and proclaim it as their own, using terms such as grid, autonomic, self-healing, self-managing, adaptive, utility, and so forth. Analysts, well, analyze and try to make sense of it all, and in the process each independently creates his or her own map of this terra incognita, naming it policy-based computing, organic computing, and so on.
The January monthly service quality meeting started normally—around the table were representatives from development, operations, marketing, and product management, and the agenda focused on the prior month’s performance. As usual, customer-impacting incidents and quality of service were key topics, and I was armed with the numbers showing the average uptime for the part of the service that I represent: MSN, the Microsoft family of services that includes e-mail, Instant Messenger, news, weather and sports, etc.
Internet services are becoming more and more a part of our daily lives. We derive value from them, depend on them, and are now beginning to assume their ubiquity as we do the phone system and electricity grid. The implementation of Internet services, though, is an unsolved problem, and Internet services remain far from fulfilling their potential in our world.
In the early ’90s, the Berkeley NOW (Network of Workstations) Project under David Culler posited that groups of less capable machines (running SunOS) could be used to solve scientific and other computing problems at a fraction of the cost of larger computers. In 1994, Donald Becker and Thomas Sterling worked to drive the costs even lower by adopting the then-fledgling Linux operating system to build Beowulf clusters at NASA’s Goddard Space Flight Center. By tying desktop machines together with open source tools such as PVM (Parallel Virtual Machine), MPI (Message Passing Interface), and PBS (Portable Batch System), early clusters—which were often PC towers stacked on metal shelves with a nest of wires interconnecting them—fundamentally altered the balance of scientific computing.
Computing economics are changing. Today there is rough price parity between: (1) one database access; (2) 10 bytes of network traffic; (3) 100,000 instructions; (4) 10 bytes of disk storage; and (5) a megabyte of disk bandwidth. This has implications for how one structures Internet-scale distributed computing: one puts computing as close to the data as possible in order to avoid expensive network traffic.
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