With everybody jumping on the cloud computing bandwagon lately, developers and architects need to spend extra time analyzing applications that can become good candidates for migration. It is wrong to believe that every legacy application can be easily migrated from the traditional on-premise infrastructure to any cloud computing environment. Therefore such migration efforts should be approached carefully and systematically.

Let's look at couple of issues that you may face when trying to migrate legacy applications to the cloud.

Client-Server Applications

Client-server applications are characterized with tight coupling between the business logic and the data tier. Most of the times the business logic is implemented as stored procedures in the database and pulling it ut can be a substantial effort. In addition such application establish a sticky session between the client and the server, which violates common cloud architecture patterns and complicates the migration process.

The obvious approach for migrating client-server applications to the cloud is to gradually abstract the business logic in a service layer and deploy the latter to the cloud. The cleaned up data tier can still be hosted on the current infrastructure until time comes to either migrate the data or retire it. At a high level your should follow these steps:

• Identify the business services that are exposed to the clients
• Implement those services as a separate business layer
• Deploy the new business layer on a cloud enabled infrastructure (either IaaS or PaaS)
• Implement a thin client layer on top of the services (in certain cases you may be able to modify the existing clients to connect to the services instead the data tier)
• Roll-out the new client among your users
• Retire the business logic in the data tier

This approach provides smooth migration because it postpones the data migration, a highly critical business component, to a later stage and in the mean time the organization is gaining important knowledge and discovers potential issues with the cloud technologies.

Scheduled Tasks

Scheduled tasks or batch jobs are another legacy application pattern that can introduce some challenges when migrating to the cloud. The premise of such applications is that they are triggered either at certain intervals or by a new batch of data that gets delivered. Majority of the times the latter approach involves transfers of files between machines. Two things that are at the core of such applications contradict with the modern cloud architectural patterns:

• The reliance on always-up machines that will trigger the execution at certain intervals
• The reliance on always-available file system used for file exchange

Functionality that such applications provide is easily achieved through the queue-centric workflow application pattern as described by Bill Wilder in his book Cloud Architecture Patterns. However, redesigning those legacy applications to use message queues can be substantial implementation effort. Hence you should approach the migration in phases. For jobs that rely on file transfers you can use these steps:

• Change the jobs to use cloud storage instead local file systems
• Add functionality at the delivery side to drop a message in the queue in addition to dropping the file
• Remove the polling functionality in the processing job and instead use the message in the queue as a triggering mechanism

For the scheduled tasks you need to change the implementation to use messages in the queue instead time intervals to trigger the tasks.

You can achieve additional benefits if you add Map-Reduce as part of your modern application design. 

Scale Up Applications

Last but not least is the type of applications that rely on additional local resources in order to handle increased loads. Such resources can be CPU speed, memory or disk storage. Unfortunately such applications are hard to migrate to the cloud unless they get redesigned to use horizontal instead vertical scaling. Most of the times such challenges are imposed at the data tier of the applications and can be solved through data-sharding.

The process for migration involves:

• Analyzing the data and potential de-normalization
• Identifying the shard key
• Splitting the data amongst the shards

As bottom line the gains for the organization in the above mentioned migration approaches are:

• Improved (and more cloud-ready) application architecture
• Enabled economies of scale at the different tiers of the application

However the biggest benefits is the cloud computing knowledge that the organization gains throughout the process.