According to one embodiment, a method in a server end station of a cloud for determining whether a service level agreement (SLA) violation has occurred or is expected to occur is described. The method includes receiving one or more insight models from an insight model builder, wherein each insight model is a based on one or more metrics previously collected from a virtualized infrastructure, and wherein each insight model models a particular behavior in the virtualized infrastructure and receiving real time metrics from the virtualized infrastructure. The method further includes for each of the one or more insight models, determining based on the received real time metrics that one or more services on the virtualized infrastructure is in an abnormal state or is expected to enter the abnormal state, wherein the abnormal state occurs when the insight model indicates that the associated modeled behavior violates a predetermined indicator.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method in a server end station of a cloud for determining whether a service level agreement (SLA) violation has occurred or is expected to occur, the method comprising: receiving one or more insight models from an insight model builder, wherein each insight model is a based on one or more metrics previously collected from a virtualized infrastructure, and wherein each insight model models a particular behavior in the virtualized infrastructure; receiving real time metrics from the virtualized infrastructure; for each of the one or more insight models, determining based on the received real time metrics that one or more services on the virtualized infrastructure is in an abnormal state or is expected to enter the abnormal state, wherein the abnormal state occurs when the insight model indicates that the associated modeled behavior violates a predetermined indicator; and sending on a message bus a message indicating that the one or more services is in an abnormal state or is expected to enter the abnormal state. 2. The method of claim 1, wherein the virtualized infrastructure comprises a plurality of server end stations executing one or more service controllers, one or more master workers, and one or more slave workers, wherein each service controller is associated with one or more workloads, wherein each service controller is communicatively coupled with one of the one or more master workers, wherein each master worker controls one or more of the one or more slave workers, wherein the master worker and controlled slave workers execute the workload associated with the service controller that is communicatively coupled with that master worker, and wherein each service controller is associated with a service. 3. The method of claim 2, further comprising: retrieving one or more service level agreements (SLAs) from an SLA store for one or more services; determining that the real time metrics violate at least one of the one or more SLAs; sending on the message bus a message indicating that one or more SLA violations have occurred for the one or more SLAs. 4. The method of claim 3, wherein the SLA store includes one or more SLAs, wherein each SLA includes at least a function of metrics over a period of time, an operator, and a threshold value. 5. The method of claim 3, wherein the message indicating that an SLA violation has occurred, when received by a cloud orchestrator, causes the cloud orchestrator to send a message to the service controller associated with the SLA to resolve the SLA violation by adjusting the parameters of the master and slave workers associated with the service controller. 6. The method of claim 2, wherein an application programming interface (API) is exposed to each workload, and wherein custom metrics are collected from each workload using the API. 7. The method of claim 1, wherein the insight model is an anomaly detection model for modeling whether a service is currently in an abnormal state. 8. The method of claim 7, wherein the insight model is at least one of a normal distribution of a behavior that is modeled using a function computed based on one or more metrics. 9. The method of claim 1, wherein the insight model is a resource prediction model for modeling whether a service is expected to enter an abnormal state. 10. A server end station of a cloud for determining whether a service level agreement (SLA) violation has occurred or is expected to occur, comprising: a processor and a memory, said memory containing instructions executable by the processor whereby the server end station is operative to: receive one or more insight models from an insight model builder, wherein each insight model is a based on one or more metrics previously collected from a virtualized infrastructure, and wherein each insight model models a particular behavior in the virtualized infrastructure; receive real time metrics from the virtualized infrastructure; for each of the one or more insight models, determine based on the received real time metrics that one or more services on the virtualized infrastructure is in an abnormal state or is expected to enter the abnormal state, wherein the abnormal state occurs when the insight model indicates that the associated modeled behavior violates a predetermined indicator; and send on a message bus a message indicating that the one or more services is in an abnormal state or is expected to enter the abnormal state. 11. The server end station of claim 10, wherein the virtualized infrastructure comprises a plurality of server end stations executing one or more service controllers, one or more master workers, and one or more slave workers, wherein each service controller is associated with one or more workloads, wherein each service controller is communicatively coupled with one of the one or more master workers, wherein each master worker controls one or more of the one or more slave workers, wherein the master worker and controlled slave workers execute the workload associated with the service controller that is communicatively coupled with that master worker, and wherein each service controller is associated with a service. 12. The server end station of claim 11, further operative to: retrieving one or more service level agreements (SLAs) from an SLA store for one or more services; determining that the real time metrics violate at least one of the one or more SLAs; sending on the message bus a message indicating that one or more SLA violations have occurred for the one or more SLAs. 13. The server end station of claim 12, wherein the SLA store includes one or more SLAs, wherein each SLA includes at least a function of metrics over a period of time, an operator, and a threshold value. 14. The server end station of claim 13, wherein the message indicating that an SLA violation has occurred, when received by a cloud orchestrator, causes the cloud orchestrator to send a message to the service controller associated with the SLA to resolve the SLA violation by adjusting the parameters of the master and slave workers associated with the service controller. 15. The server end station of claim 11, wherein an application programming interface (API) is exposed to each workload, and wherein custom metrics are collected from each workload using the API. 16. The server end station of claim 10, wherein the insight model is an anomaly detection model for modeling whether a service is currently in an abnormal state. 17. The server end station of claim 16, wherein the insight model is at least one of a normal distribution of a behavior that is modeled using a function computed based on one or more metrics. 18. The server end station of claim 10, wherein the insight model is a resource prediction model for modeling whether a service is expected to enter an abnormal state. 19. A non-transitory computer-readable storage medium having instructions stored therein, wherein the instructions, when executed by a processor of a server end station of a cloud, cause the processor to perform operations for determining whether a service level agreement (SLA) violation has occurred or is expected to occur, the operations comprising: receiving one or more insight models from an insight model builder, wherein each insight model is a based on one or more metrics previously collected from a virtualized infrastructure, and wherein each insight model models a particular behavior in the virtualized infrastructure; receiving real time metrics from the virtualized infrastructure; for each of the one or more insight models, determining based on the received real time metrics that one or more services on the virtualized infrastructure is in an abnormal state or is expected to enter the abnormal state, wherein the abnormal state occurs when the insight model indicates that the associated modeled behavior violates a predetermined indicator; and sending on a message bus a message indicating that the one or more services is in an abnormal state or is expected to enter the abnormal state. 20. The non-transitory computer-readable storage medium of claim 19, wherein the virtualized infrastructure comprises a plurality of server end stations executing one or more service controllers, one or more master workers, and one or more slave workers, wherein each service controller is associated with one or more workloads, wherein each service controller is communicatively coupled with one of the one or more master workers, wherein each master worker controls one or more of the one or more slave workers, wherein the master worker and controlled slave workers execute the workload associated with the service controller that is communicatively coupled with that master worker, and wherein each service controller is associated with a service. 21. The non-transitory computer-readable storage medium of claim 20, the operations further comprising: retrieving one or more service level agreements (SLAs) from an SLA store for one or more services; determining that the real time metrics violate at least one of the one or more SLAs; sending on the message bus a message indicating that one or more SLA violations have occurred for the one or more SLAs. 22. The non-transitory computer-readable storage medium of claim 21, wherein the SLA store includes one or more SLAs, wherein each SLA includes at least a function of metrics over a period of time, an operator, and a threshold value. 23. The non-transitory computer-readable storage medium of claim 22, wherein the message indicating that an SLA violation has occurred, when received by a cloud orchestrator, causes the cloud orchestrator to send a message to the service controller associated with the SLA to resolve the SLA violation by adjusting the parameters of the master and slave workers associated with the service controller. 24. The non-transitory computer-readable storage medium of claim 20, wherein an application programming interface (API) is exposed to each workload, and wherein custom metrics are collected from each workload using the API. 25. The non-transitory computer-readable storage medium of claim 19, wherein the insight model is an anomaly detection model for modeling whether a service is currently in an abnormal state. 26. The non-transitory computer-readable storage medium of claim 25, wherein the insight model is at least one of a normal distribution of a behavior that is modeled using a function computed based on one or more metrics. 27. The non-transitory computer-readable storage medium of claim 19, wherein the insight model is a resource prediction model for modeling whether a service is expected to enter an abnormal state.