Abstract: Embodiments described herein are generally directed to a controller of a managed container service that facilitates autoscaling based on bare metal machines available within a private cloud. According to an example, a CaaS controller of a managed container service monitors a metric of a cluster deployed on behalf of a customer within a container orchestration system. Responsive to a scaling event being identified for the cluster based on the monitoring and an autoscaling policy associated with the cluster, a BMaaS provider associated with the private cloud may be caused to create an inventory of bare-metal machines available within the private cloud. Finally, a bare metal machine is identified to be added to the cluster by selecting among the bare-metal machines based on the autoscaling policy, the inventory and a best fit algorithm configured in accordance with a policy established by or on behalf of the customer.

Abstract: Embodiments described herein are generally directed to a controller of a managed container service that facilitates selection among bare metal machines available within a private cloud. According to an example, a request is received by a Container-as-a-Service controller from a CaaS portal to create a cluster based at least in part on resources of a private cloud of a customer of a managed container service. An inventory of bare-metal machines available within the private cloud is received from a Bare-Metal-as-a-Service (BMaaS) provider associated with the private cloud. A particular bare metal machine is identified for the cluster by selecting among the available bare-metal machines based on cluster information associated with the request, the inventory, and a best fit algorithm configured in accordance with a policy established by the customer.

Abstract: Embodiments described herein are generally directed to a controller of a managed container service that facilitates selection among bare metal machines available within a private cloud. According to an example, a request is received by a Container-as-a-Service controller from a CaaS portal to create a cluster based at least in part on resources of a private cloud of a customer of a managed container service. An inventory of bare-metal machines available within the private cloud is received from a Bare-Metal-as-a-Service (BMaaS) provider associated with the private cloud. A particular bare metal machine is identified for the cluster by selecting among the available bare-metal machines based on cluster information associated with the request, the inventory, and a best fit algorithm configured in accordance with a policy established by the customer.

Abstract: Embodiments described herein are generally directed to a controller of a managed container service that facilitates autoscaling based on bare metal machines available within a private cloud. According to an example, a CaaS controller of a managed container service monitors a metric of a cluster deployed on behalf of a customer within a container orchestration system. Responsive to a scaling event being identified for the cluster based on the monitoring and an autoscaling policy associated with the cluster, a BMaaS provider associated with the private cloud may be caused to create an inventory of bare-metal machines available within the private cloud. Finally, a bare metal machine is identified to be added to the cluster by selecting among the bare-metal machines based on the autoscaling policy, the inventory and a best fit algorithm configured in accordance with a policy established by or on behalf of the customer.

Abstract: Embodiments described herein are generally directed to a controller of a managed container service that facilitates autoscaling based on bare metal machines available within a private cloud. According to an example, a CaaS controller of a managed container service monitors a metric of a cluster deployed on behalf of a customer within a container orchestration system. Responsive to a scaling event being identified for the cluster based on the monitoring and an autoscaling policy associated with the cluster, a BMaaS provider associated with the private cloud may be caused to create an inventory of bare-metal machines available within the private cloud. Finally, a bare metal machine is identified to be added to the cluster by selecting among the bare-metal machines based on the autoscaling policy, the inventory and a best fit algorithm configured in accordance with a policy established by or on behalf of the customer.

Abstract: Embodiments described herein are generally directed to a controller of a managed container service that facilitates autoscaling based on bare metal machines available within a private cloud. According to an example, a CaaS controller of a managed container service monitors a metric of a cluster deployed on behalf of a customer within a container orchestration system. Responsive to a scaling event being identified for the cluster based on the monitoring and an autoscaling policy associated with the cluster, a BMaaS provider associated with the private cloud may be caused to create an inventory of bare-metal machines available within the private cloud. Finally, a bare metal machine is identified to be added to the cluster by selecting among the bare-metal machines based on the autoscaling policy, the inventory and a best fit algorithm configured in accordance with a policy established by or on behalf of the customer.

Abstract: Embodiments described herein are generally directed to a controller of a managed container service that facilitates selection among bare metal machines available within a private cloud. According to an example, a request is received by a Container-as-a-Service controller from a CaaS portal to create a cluster based at least in part on resources of a private cloud of a customer of a managed container service. An inventory of bare-metal machines available within the private cloud is received from a Bare-Metal-as-a-Service (BMaaS) provider associated with the private cloud. A particular bare metal machine is identified for the cluster by selecting among the available bare-metal machines based on cluster information associated with the request, the inventory, and a best fit algorithm configured in accordance with a policy established by the customer.

Abstract: The present invention includes various novel systems and methods for communication in a network. A System Environment Monitor is employed in some embodiments to extract from the network both real-time and historical Network Metrics at the Infrastructure Layer, as well as Application Metadata at the Application Layer. Network analytics facilitate decisions based upon the differing characteristics of Application Components and lower-level hardware components across multiple DTTs. In response, an SDN Controller generates modified sets of SDN Flows, and implements them in real time across a mixed technology (multi-DTT) network in a manner that avoids disrupting existing SDN Flows and other real-time network traffic.

Abstract: The present invention includes various novel systems and methods for communication in a network. A System Environment Monitor is employed in some embodiments to extract from the network both real-time and historical Network Metrics at the Infrastructure Layer, as well as Application Metadata at the Application Layer. Network analytics facilitate decisions based upon the differing characteristics of Application Components and lower-level hardware components across multiple DTTs. In response, an SDN Controller generates modified sets of SDN Flows, and implements them in real time across a mixed technology (multi-DTT) network in a manner that avoids disrupting existing SDN Flows and other real-time network traffic.

Abstract: The present invention includes various novel systems and methods for communication in a network. A System Environment Monitor is employed in some embodiments to extract from the network both real-time and historical Network Metrics at the Infrastructure Layer, as well as Application Metadata at the Application Layer. Network analytics facilitate decisions based upon the differing characteristics of Application Components and lower-level hardware components across multiple DTTs. In response, an SDN Controller generates modified sets of SDN Flows, and implements them in real time across a mixed technology (multi-DTT) network in a manner that avoids disrupting existing SDN Flows and other real-time network traffic.

Abstract: Various technologies described herein pertain to combining high dynamic range techniques to enable rendering higher dynamic range scenes with an image sensor. The image sensor can implement a combination of spatial exposure multiplexing and temporal exposure multiplexing, for example. By way of another example, the image sensor can implement a combination of spatial exposure multiplexing and dual gain operation. Pursuant to another example, the image sensor can implement a combination of temporal exposure multiplexing and dual gain operation. In accordance with yet another example, the image sensor can implement a combination of spatial exposure multiplexing, temporal exposure multiplexing, and dual gain operation. The image sensor can be formed on a single wafer or the image sensor can be a 3D-IC image sensor that includes at least two vertically integrated layers.

Abstract: Various technologies described herein pertain to combining high dynamic range techniques to enable rendering higher dynamic range scenes with an image sensor. The image sensor can implement a combination of spatial exposure multiplexing and temporal exposure multiplexing, for example. By way of another example, the image sensor can implement a combination of spatial exposure multiplexing and dual gain operation. Pursuant to another example, the image sensor can implement a combination of temporal exposure multiplexing and dual gain operation. In accordance with yet another example, the image sensor can implement a combination of spatial exposure multiplexing, temporal exposure multiplexing, and dual gain operation.

Abstract: Various technologies described herein pertain to combining high dynamic range techniques to enable rendering higher dynamic range scenes with an image sensor. The image sensor can implement a combination of spatial exposure multiplexing and temporal exposure multiplexing, for example. By way of another example, the image sensor can implement a combination of spatial exposure multiplexing and dual gain operation. Pursuant to another example, the image sensor can implement a combination of temporal exposure multiplexing and dual gain operation. In accordance with yet another example, the image sensor can implement a combination of spatial exposure multiplexing, temporal exposure multiplexing, and dual gain operation. The image sensor can be formed on a single wafer or the image sensor can be a 3D-IC image sensor that includes at least two vertically integrated layers.

Abstract: Various technologies described herein pertain to combining high dynamic range techniques to enable rendering higher dynamic range scenes with an image sensor. The image sensor can implement a combination of spatial exposure multiplexing and temporal exposure multiplexing, for example. By way of another example, the image sensor can implement a combination of spatial exposure multiplexing and dual gain operation. Pursuant to another example, the image sensor can implement a combination of temporal exposure multiplexing and dual gain operation. In accordance with yet another example, the image sensor can implement a combination of spatial exposure multiplexing, temporal exposure multiplexing, and dual gain operation.

Abstract: The present invention includes various novel systems and methods for communication in a network. A System Environment Monitor is employed in some embodiments to extract from the network both real-time and historical Network Metrics at the Infrastructure Layer, as well as Application Metadata at the Application Layer. Network analytics facilitate decisions based upon the differing characteristics of Application Components and lower-level hardware components across multiple DTTs. In response, an SDN Controller generates modified sets of SDN Flows, and implements them in real time across a mixed technology (multi-DTT) network in a manner that avoids disrupting existing SDN Flows and other real-time network traffic.

Abstract: A process for disinfecting a treatment fluid is disclosed, including the step of admixing an aqueous solution comprising two or more oxidants generated via electrolysis of a salt solution with a treatment fluid. The mixed oxidants may be generated on site, using a containerized system.

Abstract: The present invention includes various novel systems and methods for dynamically modifying WDM transmission and receive steps (individually or in combination), including encoding client signals, mapping them to subchannels within or across ITU channels, modulating them onto subcarrier frequencies, and multiplexing them together for optical transmission. By dynamically modifying one or more of these processing steps over time (as well as encrypting underlying client signals), additional security is provided at the physical (optical) layer of an optical network, thereby greatly enhancing overall network security. Tunable lasers are employed to generate respective subcarrier frequencies, which represent subchannels of an ITU channel to which client signals are mapped.

Abstract: A baffle arrangement for a genset enclosure is disclosed. The genset enclosure may include an opening in a side of the enclosure and a plurality of baffles internally supported in the enclosure between the opening and a radiator portion. The baffles may be positioned in a sound path to reflect a sound transmitted along the sound path. A method for reducing sound in a genset enclosure is also disclosed. The method may include arranging a plurality of baffles within the enclosure between an opening and a radiator portion, the baffles being positioned in a sound path to reflect a sound transmitted along the sound path.

Abstract: A baffle arrangement for a genset enclosure is disclosed. The genset enclosure may include an opening in a side of the enclosure and a plurality of baffles internally supported in the enclosure between the opening and a radiator portion. The baffles may be positioned in a sound path to reflect a sound transmitted along the sound path. A method for reducing sound in a genset enclosure is also disclosed. The method may include arranging a plurality of baffles within the enclosure between an opening and a radiator portion, the baffles being positioned in a sound path to reflect a sound transmitted along the sound path.

Abstract: Disclosed are systems and methods for increasing the difficulty of data sniffing. In one embodiment, a system and a method pertain to presenting information to a user via an output device, the information corresponding to characters available for identification as part of sensitive information to be entered by the user, receiving from the user via an input device identification of information other than the explicit sensitive information, the received information being indicative of the sensitive information, such that the sensitive information cannot be captured directly from the user identification through data sniffing, and interpreting the identified information to determine the sensitive information.