Abstract: Some embodiments provide a method for a load balancer executing in a public cloud to load balance requests to datacenters from a network management system implemented in the public cloud. The method receives a message from a network management service for a local network manager at a particular datacenter of multiple datacenters managed by the network management system. The method maps a datacenter identifier for the particular datacenter included in the request to a particular connection server that handles a persistent connection between the local network manager at the particular datacenter and the network management system. Persistent connections between the datacenters and the network management system are handled by multiple different connection servers. The method sends the message to the particular connection server for the particular connection server to transmit the message to the local network manager via the persistent connection.

Abstract: Some embodiments provide a method for initiating a connection between a network management system in a public cloud and a datacenter managed by the network management system. The method is performed at a controller executing in the public cloud. The method receives a registration message from a particular datacenter to be managed by the network management system. The method identifies one of multiple connection servers in the public cloud for handling a persistent connection initiated by the datacenter. The method configures a load balancer in the public cloud to statically select the identified connection server for data messages that include a datacenter identifier associated with the particular datacenter.

Abstract: Some embodiments provide a method for a connection server that handles connections between a network management system in a public cloud and datacenters managed by the network management system. The method receives (i) a first application programming interface (API) request for a first local network manager located at a first datacenter belonging to a first datacenter group of a first tenant and (ii) a second API request for a second local network manager located at a second datacenter belonging to a second datacenter group of a second tenant. Based on site identifiers respectively associated with the first and second datacenters and included in the first and second API requests, the method respectively sends the first and second API requests to the first and second local network managers via first and second connections previously initiated by the first and second local network managers.

Abstract: Some embodiments provide a method for a connection server that handles a connection between a network management system in a public cloud and a datacenter belonging to a datacenter group managed by the network management system. The method receives an API request, having a first format, for a local network manager located at the datacenter. The method translates the API request into a second format used by the connection between the network management system and the datacenter. The connection is a type that (i) allows for the connection server to push data onto the connection when the connection is initiated by a connection agent at the datacenter and (ii) uses the second format. The method sends the translated API request in the second format to the connection agent via the connection. The connection agent translates the API request into the first format for execution at the local network manager.

Abstract: Some embodiments provide a method that identifies a first number of requests received at a first application. Based on the first number of requests received at the first application, the method determines that a second application that processes requests after processing by the first application requires additional resources to handle a second number of requests that will be received at the second application. The method increases the amount of resources available to the second application prior to the second application receiving the second number of requests.

Abstract: Dynamically scaling control plane for ingress services for large numbers of applications with minimal traffic disruption includes receiving an estimate of a number of applications to be executed by multiple clusters implemented by an orchestrator platform. Each cluster includes multiple containers. The multiple clusters implement a centralized controller that control execution of the applications by the multiple clusters. The centralized controller is sharded into a variable number of controllers that collectively control the estimated number of applications based on the estimate of the number of applications and a pre-determined number of applications that each controller can control. Each controller of the variable number of controllers controls an execution of a respective subset of the applications. In response to a change in the number of applications over time, the number of controllers is modified based on a number of applications to be executed by the multiple clusters at any given time.

Abstract: Some embodiments provide a method that identifies a first number of requests received at a first application. Based on the first number of requests received at the first application, the method determines that a second application that processes requests after processing by the first application requires additional resources to handle a second number of requests that will be received at the second application. The method increases the amount of resources available to the second application prior to the second application receiving the second number of requests.

Abstract: Some embodiments provide a method for scaling a service chain that includes multiple services, each of which is provided by one or more instances of the service. The method identifies that a first service in the service chain has received a number of requests. For each service in the service chain, the method (i) identifies a scaling factor that estimates a portion of requests received at the first service that will be subsequently received at the service and (ii) deploys a number of additional instances of the service based on the identified scaling factor for the service and the number of requests received at the first service.

Abstract: Dynamically scaling control plane for ingress services for large numbers of applications with minimal traffic disruption includes receiving an estimate of a number of applications to be executed by multiple clusters implemented by an orchestrator platform. Each cluster includes multiple containers. The multiple clusters implement a centralized controller that control execution of the applications by the multiple clusters. The centralized controller is sharded into a variable number of controllers that collectively control the estimated number of applications based on the estimate of the number of applications and a pre-determined number of applications that each controller can control. Each controller of the variable number of controllers controls an execution of a respective subset of the applications. In response to a change in the number of applications over time, the number of controllers is modified based on a number of applications to be executed by the multiple clusters at any given time.

Abstract: Technical solutions are described for assigning containers for executing functions in a software application using a server cluster that uses functions as a service architecture. An example method includes creating, by a global scheduler of the server cluster, a first container at a first host node in the server cluster for executing a first function from the software application. The method further includes intercepting, by an in-node scheduler of the first host node, a command from the first function for creating a second container for executing a second function. The method further includes assigning, by the in-node scheduler, an in-node container as the second container, the in-node container being a container executing on the first host node. The method further includes updating, by the in-node scheduler, a resource database that indicates that the second function is being executed in the in-node container on the first host node.

Abstract: An apparatus for implementing a user policy includes a policy module that issues a user policy for a plurality of computing devices. Two or more of the computing devices have different hardware configurations. The apparatus includes a soft metrics module that, in response to the user policy, sends soft metrics for a computing device of the plurality of computing devices, to determine if the computing device complies with the user policy, where the soft metrics are specific to a hardware configuration of the computing device. The apparatus includes an operational data module that receives, from the computing device, operational data responsive to the soft metrics, and a compliance module that analyzes the operational data to determine if the computing device complies with the user policy.

Abstract: Technical solutions are described for assigning containers for executing functions in a software application using a server cluster that uses functions as a service architecture. An example method includes creating, by a global scheduler of the server cluster, a first container at a first host node in the server cluster for executing a first function from the software application. The method further includes intercepting, by an in-node scheduler of the first host node, a command from the first function for creating a second container for executing a second function. The method further includes assigning, by the in-node scheduler, an in-node container as the second container, the in-node container being a container executing on the first host node. The method further includes updating, by the in-node scheduler, a resource database that indicates that the second function is being executed in the in-node container on the first host node.

Abstract: Embodiments of the present disclosure provide bisbiotin ligands and related conjugates and methods. The bisbiotin ligands, combined with streptavidin, can be used in the separation, labelling, targeting, and immobilization of biomolecules.

Abstract: A computer manages methods for determining workload placement in a computing environment. The computer receives a plurality of workloads with associated information, wherein the associated information for each workload contains at least: contract information, billing information, and resource availability information. The computer determines a profitability factor for each workload of the plurality of workloads, wherein the profitability factor is at least based on the billing information. The computer determines a penalty factor for each workload of the plurality of workloads, wherein the penalty factor is at least based on the contract information. The computer determines a preference factor for each workload of the plurality of workloads, wherein the preference factor is at least based on the resource availability information. The computer assigns a priority ordering for each of the workloads from the plurality of workloads.

Abstract: An apparatus for implementing a user policy includes a policy module that issues a user policy for a plurality of computing devices. Two or more of the computing devices have different hardware configurations. The apparatus includes a soft metrics module that, in response to the user policy, sends soft metrics for a computing device of the plurality of computing devices, to determine if the computing device complies with the user policy, where the soft metrics are specific to a hardware configuration of the computing device. The apparatus includes an operational data module that receives, from the computing device, operational data responsive to the soft metrics, and a compliance module that analyzes the operational data to determine if the computing device complies with the user policy.

Abstract: Embodiments disclosed herein are directed to recognition tunneling systems, methods and devices, and more particularly, to chemical reactions for selectively labeling proteins and peptides and placing protein and/or peptides into, or onto a nanopore formed in a solid support and threading such in and/or through the nanopore, with such nanopores, in some embodiments, including a molecular motor to pull or otherwise force the protein/peptide through the nanopore.

Abstract: Embodiments disclosed herein are directed to recognition tunneling systems, methods and devices, and more particularly, to chemical reactions for selectively labeling proteins and peptides and placing protein and/or peptides into, or onto a nanopore formed in a solid support and threading such in and/or through the nanopore, with such nanopores, in some embodiments, including a molecular motor to pull or otherwise force the protein/peptide through the nanopore.

Abstract: Embodiments of the present disclosure provide bisbiotin ligands and related conjugates and methods. The bisbiotin ligands, combined with streptavidin, can be used in the separation, labelling, targeting, and immobilization of biomolecules.

Abstract: Multi-site disaster recovery mechanism performed by the following steps: (i) providing a disaster recovery (DR) system that includes a plurality of sites where each site of the plurality of sites actively serves infrastructure-as-a-service to a set of tenant(s); (ii) for each site of the plurality of sites, determining the following characteristics of the site: workloads that require DR, workloads characteristics, tenants and capabilities; (iii) for each site of the plurality of sites, determining a plurality of associated sites; and (iv) on condition that a disaster occurs which impacts a first site of the plurality of sites, distributing a primary site workload of the first site across the associated sites of the first site. The determination of the plurality of associated sites associated with each site is based upon at least one of the following characteristics: capacity, workloads that require DR, workloads characteristics, tenants and/or capabilities.

Abstract: A computer manages methods for determining workload placement in a computing environment. The computer receives a plurality of workloads with associated information, wherein the associated information for each workload contains at least: contract information, billing information, and resource availability information. The computer determines a profitability factor for each workload of the plurality of workloads, wherein the profitability factor is at least based on the billing information. The computer determines a penalty factor for each workload of the plurality of workloads, wherein the penalty factor is at least based on the contract information. The computer determines a preference factor for each workload of the plurality of workloads, wherein the preference factor is at least based on the resource availability information. The computer assigns a priority ordering for each of the workloads from the plurality of workloads.