Abstract: A method, system and computer program product for providing dual mode view of cloud computing environment. User request for performing operation on entity of the environment while in sandbox view being intercepted by client and sent to server with sandbox token obtained therefrom responsive to user switch from production view to sandbox view. Validation of sandbox view by server being performed using the sandbox token. Data structure comprising identification as production/sandbox of each entity of the environment and each sandbox modified production entity, and datastore of all entities of the environment and data thereof, being used for processing the request and recording results thereof. Sandbox entities being filtered out of production view, and sandbox modified production entities being filtered out of sandbox view.

Abstract: Example implementations disclosed herein relate to a system for generating an element identifier comprising an action engine to receive an action associated with an element of a structured document and to measure a metric associated with the received action. The system may further comprise an element engine to determine whether the element comprises a targeted element and, in response to determining that the element comprises the targeted element, generate a unique identifier according to a modified attribute of the element. The system may further comprise a log engine to create a log record comprising the unique identifier and the metric associated with the received action.

Abstract: Examples disclosed herein relate to a system for generating an element identifier comprising an action engine to identify at least one event listener associated with a significant event in an application, receive an action associated with an element of the application, generate a composite event sequence comprising a plurality of event listeners associated with the action, and determine whether the composite event sequence is associated with the at least one significant event. The system may further comprise a metric engine to measure a performance metric associated with the received action and create a log record according to the composite event sequence comprising the performance metric.

Abstract: Examples disclosed herein relate to a system for generating an element identifier comprising an action engine to identify at least one event listener associated with a significant event in an application, receive an action associated with an element of the application, generate a composite event sequence comprising a plurality of event listeners associated with the action, and determine whether the composite event sequence is associated with the at least one significant event. The system may further comprise a metric engine to measure a performance metric associated with the received action and create a log record according to the composite event sequence comprising the performance metric.

Abstract: Example implementations disclosed herein relate to a system for generating an element identifier comprising an action engine to receive an action associated with an element of a structured document and to measure a metric associated with the received action. The system may further comprise an element engine to determine whether the element comprises a targeted element and, in response to determining that the element comprises the targeted element, generate a unique identifier according to a modified attribute of the element. The system may further comprise a log engine to create a log record comprising the unique identifier and the metric associated with the received action.

Abstract: Systems, methods, and machine-readable and executable instructions are provided for detecting abnormal behavior. Detecting abnormal behavior can include receiving a mean at a previous time interval, a sum of squares at the previous time interval, and a first sample of a metric at a current time interval from a system and adjusting a first weight and a second weight at the current time interval to the first sample and a system change report. Detecting abnormal behavior can also include calculating a mean and a standard deviation of the metric at the current time interval by assigning the first sample the adjusted first weight and by assigning the mean and the sum of squares at a previous time interval the adjusted second weight and detecting abnormal behavior by comparing the first sample to an outlier value based on the mean and the standard deviation at the previous time interval.