Abstract: Exemplary techniques for analytics-driven hybrid concurrency control in clouds are disclosed that include a hybrid resource allocation module that can concurrently utilize an optimistic allocation scheme alongside a pessimistic allocation scheme. Machine learning techniques utilizing previous activity history of applications can be used to train a cluster model that is integrated by a hybrid resource allocation module to classify applications in either a pessimistic cluster or an optimistic cluster that identifies under which scheme requests from the applications will be processed.

Abstract: 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.

Abstract: Exemplary techniques for analytics-driven hybrid concurrency control in clouds are disclosed that include a hybrid resource allocation module that can concurrently utilize an optimistic allocation scheme alongside a pessimistic allocation scheme. Machine learning techniques utilizing previous activity history of applications can be used to train a cluster model that is integrated by a hybrid resource allocation module to classify applications in either a pessimistic cluster or an optimistic cluster that identifies under which scheme requests from the applications will be processed.

Abstract: Exemplary techniques for analytics-driven hybrid concurrency control in clouds are disclosed that include a hybrid resource allocation module that can concurrently utilize an optimistic allocation scheme alongside a pessimistic allocation scheme. Machine learning techniques utilizing previous activity history of applications can be used to train a cluster model that is integrated by a hybrid resource allocation module to classify applications in either a pessimistic cluster or an optimistic cluster that identifies under which scheme requests from the applications will be processed.

Abstract: A method in a server end station is described. The method includes performing an iteration of a rebalancing computation by selecting a set of one or more service sets for rebalancing, wherein the selecting the set of one or more service sets is based on service set constraints and host constraints; generating candidate solutions, wherein each candidate solution includes a randomized one-to-one mapping of each of the service sets to one of the hosts; performing one or more crossover operations on the candidate solutions; performing one or more mutation operations on the additional candidate solutions; selecting as a solution one of the candidate solutions that has a best fitness score, wherein a fitness score for a candidate solution is calculated based on the distribution of resources resulting from and number of migrations needed for the candidate solution; and repeating the iteration of the rebalancing computation an additional number of times.

Abstract: According to one embodiment, a method in a cloud infrastructure for multi-level threshold service level agreement (SLA) violation mitigation. The method includes generating a model for an engineered feature (eF); determining thresholds T1, T2, and a maximum threshold T? for the eF based on the model; estimating a value of the eF based on metrics; responsive to determining that the value of the eF exceeds T?, modifying the values of T1 and T2 and modifying the estimation frequency and sending to a cloud orchestrator a message indicating that an SLA violation of type T? has occurred.

Abstract: Exemplary techniques for analytics-driven hybrid concurrency control in clouds are disclosed that include a hybrid resource allocation module that can concurrently utilize an optimistic allocation scheme alongside a pessimistic allocation scheme. Machine learning techniques utilizing previous activity history of applications can be used to train a cluster model that is integrated by a hybrid resource allocation module to classify applications in either a pessimistic cluster or an optimistic cluster that identifies under which scheme requests from the applications will be processed.

Abstract: According to one embodiment, a method in a cloud infrastructure for multi-level threshold service level agreement (SLA) violation mitigation. The method includes generating a model for an engineered feature (eF); determining thresholds T1, T2, and a maximum threshold T? for the eF based on the model; estimating a value of the eF based on metrics; responsive to determining that the value of the eF exceeds T?, modifying the values of T1 and T2 and modifying the estimation frequency and sending to a cloud orchestrator a message indicating that an SLA violation of type T? has occurred.

Abstract: 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.

Abstract: A method in a server end station is described. The method includes performing an iteration of a rebalancing computation by selecting a set of one or more service sets for rebalancing, wherein the selecting the set of one or more service sets is based on service set constraints and host constraints; generating candidate solutions, wherein each candidate solution includes a randomized one-to-one mapping of each of the service sets to one of the hosts; performing one or more crossover operations on the candidate solutions; performing one or more mutation operations on the additional candidate solutions; selecting as a solution one of the candidate solutions that has a best fitness score, wherein a fitness score for a candidate solution is calculated based on the distribution of resources resulting from and number of migrations needed for the candidate solution; and repeating the iteration of the rebalancing computation an additional number of times.

Abstract: A ceiling arrangement for clean rooms has profiled beams with end faces and connecting members having at least two end faces that are connectors for the end faces of the profiled beams. The end faces of the profiled beams are connected to the connectors of the connecting members to form a flush connection. A sealing member for sealing each one of the flush connections is provided. For each one of the flush connections the end face of the connecting member or the end face of the profiled beam has a groove in which the sealing member is received.

Abstract: A clean room ceiling structure includes a support structure and filter units with a filter frame and a filter secured in the filter frame. The filter frames are connected to the support structure. The filter units have sealing fluid grooves for sealing the filter units relative to the support structure.