Bug-Out Bags and Cloud Architecture Considerations

BuckWoody — Fri, 20 Jan 2012 17:00:58 GMT

I served in the U.S. Military for a while, and as part of my training we had to maintain a “Bug-Out Bag”, which was a large duffle-bag full of certain items that we could live on/fight with in an emergency. I’ve carried the spirit of that idea forward with me into civilian life, in Florida and especially here in the Pacific Northwest.

In Florida we dealt with the threat of hurricanes - I went through four of those in one year that hit my area. You’re without power, it floods quickly, and it gets wicked hot. You roof might be gone, whatever. Here in the Pacific Northwest, I live near one of the largest volcano's in the world, we have flooding, and recently we were hit with an ice-storm. Now I’ve lived all over the world, from Alaska to North Dakota and even near the Kamchatka Peninsula in Russia, and I can handle the snow. But ice - that’s a toughie no matter where you live. We had so much that it split my little pine tree in front of the house in half.

We lost power - although I think the folks at Puget Sound Energy did an amazing job at getting us back up in less than 24 hours, but we weren’t worried anyway. That bug-out bag mentality carried forward to a “second pantry” we keep in the garage.

We have a large plastic box (that will fit in the back of the Subaru) with dried goods like pasta, and canned goods and even a little cook stove. We have 25 gallons of clean water in Jerry-Cans. We have batteries, candles and matches. And we have flashlights around every door. We use supplies from the “pantry” to fill our house pantry, and then refill the emergency one from the grocery store. That way everything is fresh, rotated, and we can “bug-out” here at home or on the road.

So what does this have to do with Distributed Computing Architectures?

It’s the thought process. In both the military and civilian life, I’ve done a few things:

Sat down and thought carefully about exactly what I need. Did I include a can-opener? A small shovel to dig out of whatever I got stuck in? Then I weed out what I *really* don’t need.
Put those things into a small, manageable container.
Tried them - even when (especially when) I didn’t have an emergency
Tweaked the process to see what I could do better.

Have you done this when you moved an app to the “cloud”? Each of these has a computing parallel - do you know what you would do if you couldn’t access the Distributed Computing Environment?

I’ve found these thoughts are actually a great place to start - keeps the process simplified from the start, and gives you a sense of assurance when you’re asked if you can recover from an emergency.

SQL Azure Use Case: Shared Storage Application

BuckWoody — Tue, 26 Apr 2011 13:33:50 GMT

This is one in a series of posts on when and where to use a distributed architecture design in your organization's computing needs. You can find the main post here: http://blogs.msdn.com/b/buckwoody/archive/2011/01/18/windows-azure-and-sql-azure-use-cases.aspx

Description:

On-premise data will be a part of computing for quite some time – perhaps permanently. Bandwidth requirements, security, or even financial considerations for large data sets often dictate that relational (on non-relational) systems will be maintained locally in many organizations, especially in enterprise computing.

But distributed data systems are useful in many situations. Organizations may wish to store a portion of data off-site, either for sharing the data with other applications (including web-based applications) or as a supplement to a High-Availability and Disaster Recovery (HADR) strategy.

Implementation:

SQL Azure can be used to add an additional option to an HADR strategy by copying off portions (or all) of an on-premise database system.

In this arrangement, on-premise systems remain as they are. Data is replicated using many technologies, such as SQL Server Integration Services (SSIS), scripts, or Microsoft’s Sync Framework to a SQL Azure database. This data can be kept “cold”, meaning that a manual process is required to bring the data back, or as a “warm” standby using connection string management in the application.

Recently we architected a solution where a company kept a rolling two-week window of data replicated to SQL Azure using the Sync Framework. The application, a compiled EXE running on user’s systems, had a “switch connections” button, that allowed the users to take a laptop to another location, select that option, and continue working from anywhere they had Internet connectivity. This required forethought and planning, and did not replace their primary HADR systems, but it did allow them to continue operations in the case of a severe outage at multiple sites. Since they are an emergency services provider, this gave them the highest redundancy.

Another option is to amalgamate data from disparate sources.

In this arrangement, two or more data services (one of which is SQL Azure) are accessed by a single program. The program queries each system independently, and using LINQ a single query can work across all of the data, assuming there is some sort of natural or artificial “key” that can join the data sets together. The user programs simply view this single data set as a single data source, unaware of the underlying data sets. This allows great flexibility and agility in the downstream program. The upstream data sources can change as long as the elements are kept consistent.

There are performance and security implications to amalgamated data systems, but if architected carefully they provide multiple benefits. A few of of these are that other systems can access the individual data sources, reporting is simplified and standardized, and multiple copies of data are eliminated.

Resources:

You can read more about the Sync Framework and SQL Azure here: http://social.technet.microsoft.com/wiki/contents/articles/sync-framework-sql-server-to-sql-azure-synchronization.aspx

If you are new to LINQ, you can find more resources on it here: http://msdn.microsoft.com/en-us/library/bb308959.aspx

Buck Woody : Concepts, Cloud, Disaster Recovery, Azure

Bug-Out Bags and Cloud Architecture Considerations

SQL Azure Use Case: Shared Storage Application