There is no argument that there is a lot of data in the networks on the web, and it is evident that the number is only growing. Data overload in the cloud is a major issue for all cloud service providers. Because of the position that they are in, it is likely that they will experience overload at one point or another. It is imperative that service providers understand the implications this brings and learn how to mitigate these issues.
What is Oversubscription
Oversubscribing your cloud means that the cloud service provider is selling more resources than are available. While it may look like there are enough resources available on average, peak usage times may not be able to handle the additional demand. When this happens, an overload occurs. This slows down your applications and, if severe enough, can cause the environment to stop processing data altogether.
There are several types of cloud data overload. The two main types of include:
- End user oversubscription occurs when the user does not deploy an instance with enough memory to handle demands of the virtual machine
- Provider oversubscription occurs when the service provider doesnt provide enough memory to the instance
In this post, I will discuss oversubscription because of circumstances the service provider can mitigate. Knowing the negative implications data overload can bring to your cloud environment, it is equally important to learn how to avoid issues that can cause this. Pairing virtual machines with the proper storage space will be imperative to preventing overload from your side of the cloud environment.
Examining CPU, memory and disk usage will play an instrumental part for your team as you determine the distribution of data in your environment. Looking at the distribution of data, you can not only determine how many resources you are currently using, but you can also see how much data, on average, is used per application. This data can give you a more accurate picture of how many virtual machines your infrastructure can handle.
Another way to mitigate data overload is to put data security measures into place. Learning how to secure your cloud data in a secondary backup location will help reduce the amount of memory that is being held or accessed in your cloud environment. This reduces the amount of traffic into and out of your cloud, therefore also reducing potential data overload. Encouraging your end users to move non-critical or archived data into a secondary location will be instrumental in reducing the data in your cloud. An illustration is an accounting firm that needs to keep archived files for seven years. They can migrate these archives to a secondary location in order to free space for data that needs to be processed at present.
Monitoring and Alerts
Alternatively, you can set up an alert system within your cloud monitoring tool that will mark a given threshold in your environment. Once this threshold is reached, the system alerts your team to a potential overload. With proper monitoring, the overload risk can be mitigated. These alerts need to be able to identify the threshold based on normal cloud data behavior. Once the tech team receives a distress signal from your cloud environment, you can put teams in place to take necessary corrective actions.
It is possible to control overload once it has occurred. For example, an overloaded virtual machine can be migrated to another location or turned off completely. While this could potentially cause delays in your environment just before the migration, it can lighten the demands on your infrastructure.
There is no argument that cloud data overload is an issue for any service provider. Understanding why and how this phenomenon occurs is the first step to learning how to mitigate the issue and provide a more cohesive cloud environment for your end users. Cloud data mitigation is an essential function of your cloud environment and a necessary step you must take to remain a credible, viable solution for your end users in your market.