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The technology to deliver this capability largely exists today. It is just up to a vendor to package it up properly, customize it for the relevant operating environments, and make it available. A form of it will likely be available through manual snapshots by the end of 2003. System administrators have been using Tripwire (www.tripwire.org) and similar packages to generate checksums and digital fingerprints of system configurations in security circles, looking for changes that may have resulted from intrusions. Expect to see more widespread use of configuration management tools like Cfengine (www.cfengine.org) to automate the process and rulesets to create, edit, and distribute configuration information.
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The SETI (Search for Extraterrestrial Intelligence) at Home project (http:// setiathome.ssl.berkeley.edu) has been doing gridlike work for many years. SETI@home takes data from radio telescopes that are scanning the skies, breaks it up into small pieces, and parcels those pieces out to users across the Internet who have signed up to take part, and who dedicate their PCs to the effort, when they aren t doing anything else. Anyone who finds evidence of extraterrestrial intelligence in the data that her computer analyzes expects a significant reward, and most likely some serious notoriety. Nobody has collected so far. With nearly one-and-a-half million CPU-years logged so far, this effort has been called the largest concentrated effort of computing power in history. SETI@home is the best example of grid computing that we have been able to find. By breaking up huge amounts of data into bite-sized pieces, and sharing them among millions of computers, they have achieved the same goals as the developers of grid computing initiatives. The grid is seen by some as the next step in the evolution of computing: a model where all computers in a particular logical or physical region (a data center, a company, a country, or perhaps the whole world) are joined together to share computing resources. Systems with idle resources would make those resources available to the rest of the grid. Proponents of grid computing often speak of a utility model for computing, where you could plug an appropriate device into the wall, just as you would plug in a table lamp to get electricity, and you would receive computing cycles. That appropriate device would probably be a computer, without a great deal of processing power, but just enough to know how to successfully connect to the grid and communicate with it. Before grid computing can achieve wider acceptance, there are many hurdles that will need to be cleared, the biggest of which is probably security. If someone else can run a process on my system, who s to say that the process won t work some mischief on my system and steal passwords or install some form of malware At the same time, if my process runs on some anonymous
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system, who s to say that the other system won t in some way affect (either innocently or maliciously) the running of that process and cause it to return bad data, or to snoop on what the process is doing One argument against the grid is that today s computers already have far more processing power than their owners actually need, and that PDAs and portable phones will likely follow suit before too long. With that in mind, grid architectures may only be useful for a handful of the most overwhelmingly processing-intensive applications, such as weather forecasting, factoring prime numbers, and SETI@home-like projects. Another important concern that must be addressed before grid computing can achieve wide acceptance is accurate process billing. If you are running a process on my computer, I would certainly expect you to pay me for the resources you are consuming. If grid resources become truly worldwide, political questions come into play as well: What if a process sent from one country is run on a computer in a country where the process is illegal in some way, and how can these concerns be policed Is it appropriate for one company to run processes on computers owned by a competitor Policy and resource allocation, as discussed in 19, will prove to be a fundamental enabler for large-scale grid computing. Early-adopting commercial entities must begin to implement small grids internally to make better use of their existing computing resources. To be fair, at this writing there are very few commercial applications that can take advantage of the grid. New techniques must be developed to encourage the production of parallel applications, as well as distributed applications for highly partitioned computing. Grid computing today is centered on computational tasks, but there s no reason the concept of local clusters of resources, dynamically allocated and provisioned, can t be applied to storage as well. Consider the problem of creating temporary, large storage for media files like high-quality digital images or video clips. If you want to share your child s soccer pictures with the other members of the team, there s probably no reason to point everyone at an archive site when a grid storage node would provide lower latency, being closer to any point of presence for any ISP used by team parents. From an availability perspective, grid computing could be the ultimate extension of clustering and replication techniques such as those covered in 15, Local Clustering and Failover. If computers and computing power become true commodities like electricity, then the availability of a single computer becomes irrelevant. All computers become instantly replaceable by other computers. If a critical process is being run on the grid, perhaps it would be run on multiple computers at the same time. If one of those computers failed to respond, others would complete the task anyway. The user might never even know. For more information on grid computing, visit www.gridforum.org.
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