The Insider's Guide to Next-Generation BPM
By Jason Stowe, Founder and CEO, Cycle Computing , 12/14/2009
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Editor's note: Missed our SOA in Action Virtual Conference? Replay sessions on demand here.
Cloud computing promises a lot: access to large-scale virtualized computing and storage, 24/7 availability and costs associated solely with the resources used.
The cloud is recognized to have started in 2006 with Amazon Web Service's (AWS) introduction of the Elastic Compute Cloud (Amazon EC2). Incredibly innovative, this service, when combined with the Simple Storage Service (Amazon S3), enabled developers to start virtual machine images and consume storage as a service, where billing was done on per-hour and per-gigabyte rates.
These services were the start of what is now recognized as Infrastructure-as-a-Service (IaaS). Currently, managed hosting companies, including GoGrid and Rackspace, are integrating IaaS capabilities to provision external resources dynamically. It's expected that more providers will follow Amazon's lead.
Efficiency challenges for data centers
In contrast, traditional internal data centers have been grappling with record-high
energy prices, fueling the demand for efficiency within an organization's infrastructure.
According to statistics from the Uptime Institute, a 451 Group company, the
average data center is 10 percent utilized, and therefore 90 percent idle.
Additionally, only about 3 percent of a data center's electricity consumption is spent crunching data. The remainder is spent on idleness, redundancy and other energy overhead. Data center electricity use is growing at about 12 percent per year, which creates the need to add about 1,000 megawatts of electricity-production capacity annually. This is equal to building two large 500-megawatt power plants at a cost of $1-$2 billion each construct.
All of these costs and inefficiencies could be saved by increasing utilization, saving energy and, in the process, saving money. Prior to the advent of virtualization and the cloud, this inefficiency was a requisite for decoupling applications from each other and enabling reliability through redundancy. Critical applications had a production and disaster-recovery environment, both fully provisioned in separate locations to ensure that uptime could be guaranteed even with outages and failure.
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Best Practices in Moving Processes to the Clouds
How Can the Cloud Fit Into Your Applications Strategy?
Cloud Data Integration Best Practices
The Economics of Cloud Computing
Data Services Insight: Successful implementation of Canonical Information Models
The new frontier for life sciences & beyond: Specializing HPC clusters in the cloud: Part 2 
The new frontier for life sciences & beyond: Specializing HPC clusters in the cloud: Part 1
Disaster recovery virtualization vs. cloud
Should You Send Workload Automation to the Cloud?
Cloud Computing: Why we Need to go Back to the Future
ebizQ editorial highlights and updates, compiled by Site Editor Anne Stuart
March 10, 2011
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Date:Apr 07, 2010
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