TECHNICAL FIELD

The subject matter described herein relates to enhanced techniques for minimizing damage to data centers using machine learning.

BACKGROUND

Data centers are susceptible to various types of incidents which can negatively affect the data being stored therein as well as the related computing equipment. In particular, software malfunctions can cause the overall performance of a data center to be impacted so as to result in data loss (without the ability to restore) or service level agreement (SLA) performance issues. To minimize the risks associated with such incidents, data centers often have circuit breaker mechanisms to prevent data loss and to cut off malfunctioning nodes (e.g., computers, servers, etc.) after such failures are detected. These circuit breakers can be triggered, for example, if operation (e.g., transaction, API, etc.) failures hit a certain pre-defined level. The circuit breakers can also be triggered based on traffic monitoring and, in some cases, can cause data traffic to be rerouted to a different node within the data center.

SUMMARY

In a first aspect, calls received by a data center that are associated with a request are monitored. Features are subsequently extracted from the monitored calls so that a machine learning model may use such features to determine that the request will cause the data center to malfunction. The machine learning model can be trained using data derived from a transaction log for the data center. At least one correction action to prevent the data center from malfunctioning can then be initiated in response to such determination.

The corrective action can take many forms including activating additional nodes within the data center to handle the request and future requests and/or limiting or throttling subsequent calls or requests.

The machine learning model can take many forms including one or more of: a logistic regression model, a neural network, a support vector machine, or a random forest model. In some variations, the machine learning model outputs a classification can indicate whether the request is good or bad. In other variations, the machine learning model can output a numerical score over a pre-defined range (e.g., 0 to 100, −1 to +1, etc.).

The features that are extracted can include one or more of: pathway, implicated server, Hypertext Transfer Protocol (HTTP) status code, process time, central processing unit (CPU) consumption, processing time, number of exceptions, memory consumption, or number of data access calls.

Non-transitory computer program products (i.e., physically embodied computer program products) are also described that store instructions, which when executed by one or more data processors of one or more computing systems, cause at least one data processor to perform operations herein. Similarly, computer systems are also described that may include one or more data processors and memory coupled to the one or more data processors. The memory may temporarily or permanently store instructions that cause at least one processor to perform one or more of the operations described herein. In addition, methods can be implemented by one or more data processors either within a single computing system or distributed among two or more computing systems. Such computing systems can be connected and can exchange data and/or commands or other instructions or the like via one or more connections, including but not limited to a connection over a network (e.g., the Internet, a wireless wide area network, a local area network, a wide area network, a wired network, or the like), via a direct connection between one or more of the multiple computing systems, etc.

The subject matter described herein provides many technical advantages. For example, the current subject matter provides enhanced techniques for protecting data centers when software malfunctions.

The details of one or more variations of the subject matter described herein are set forth in the accompanying drawings and the description below. Other features and advantages of the subject matter described herein will be apparent from the description and drawings, and from the claims.

