Abstract: Techniques are described herein that are capable of performing an aggregation-based determination of resiliency of a cloud computing service to chaos events. Chaos events are executed against a cloud computing service during a time frame that includes multiple time periods. For each chaos event, service health indexes (SHIs) that correspond to the time periods are calculated. Each SHI indicates an extent to which indicators, which represent respective measurements of performance of the cloud computing service during the respective time period, satisfy respective criteria. The SHIs for each chaos event are analyzed to provide a respective resiliency score. At least some of the resiliency scores of the respective chaos events are aggregated to provide an aggregated resiliency score, which represents resiliency of the cloud computing service to the chaos events.

Abstract: Techniques described herein are directed to the intelligent validation of network-based services via a proxy. The proxy is communicatively coupled to a first network-based service and a second network-based service. The proxy is utilized to validate the functionality of the first network-based service with respect to the second network-based service. The proxy initially operates in a first mode in which the proxy monitors and analyzes the transactions between the first and second network-based services and learns the behavior of the second network-based service. The proxy then operates in a second mode in which the proxy simulates the learned behavior of the second network-based service. When operating in the second mode, requests initiated by the first network-based service and intended for the second network-based service are provided to the proxy, and the proxy generates a response to the request in accordance with the learned behavior of the second network-based service.

Abstract: Techniques are described herein that are capable of performing an aggregation-based determination of resiliency of a cloud computing service to chaos events. Chaos events are executed against a cloud computing service during a time frame that includes multiple time periods. For each chaos event, service health indexes (SHIs) that correspond to the time periods are calculated. Each SHI indicates an extent to which indicators, which represent respective measurements of performance of the cloud computing service during the respective time period, satisfy respective criteria. The SHIs for each chaos event are analyzed to provide a respective resiliency score. At least some of the resiliency scores of the respective chaos events are aggregated to provide an aggregated resiliency score, which represents resiliency of the cloud computing service to the chaos events.

Abstract: Techniques are described herein that are capable of performing an aggregation-based determination of resiliency of a cloud computing service to chaos events. Chaos events are executed against a cloud computing service during a time frame that includes multiple time periods. For each chaos event, service health indexes (SHIs) that correspond to the time periods are calculated. Each SHI indicates an extent to which indicators, which represent respective measurements of performance of the cloud computing service during the respective time period, satisfy respective criteria. The SHIs for each chaos event are analyzed to provide a respective resiliency score. At least some of the resiliency scores of the respective chaos events are aggregated to provide an aggregated resiliency score, which represents resiliency of the cloud computing service to the chaos events.

Abstract: A computer system is provided that includes a cloud platform that includes a plurality of nodes. Each node includes a processor configured to run virtual machines. The cloud platform includes a fault condition injection engine configured to generate fault conditions on selected nodes of the plurality of nodes. The computer system further includes a user interface system configured to receive user input of fault condition experimentation parameters from a user for a target virtual machine associated with the user. The cloud platform allocates a set of nodes of the plurality of nodes for a controlled sandbox environment configured to run the target virtual machine of the user. The fault condition injection engine generates fault conditions on the allocated set of nodes based on the fault condition experimentation parameters.

Abstract: A computer system is provided that includes a cloud platform that includes a plurality of nodes. Each node includes a processor configured to run virtual machines. The cloud platform includes a fault condition injection engine configured to generate fault conditions on selected nodes of the plurality of nodes. The computer system further includes a user interface system configured to receive user input of fault condition experimentation parameters from a user for a target virtual machine associated with the user. The cloud platform allocates a set of nodes of the plurality of nodes for a controlled sandbox environment configured to run the target virtual machine of the user. The fault condition injection engine generates fault conditions on the allocated set of nodes based on the fault condition experimentation parameters.