Some embodiments provide policy-driven methods for deploying edge forwarding elements in a public or private SDDC for tenants or applications. For instance, the method of some embodiments allows administrators to create different traffic groups for different applications and/or tenants, deploys edge forwarding elements for the different traffic groups, and configures forwarding elements in the SDDC to direct data message flows of the applications and/or tenants through the edge forwarding elements deployed for them. The policy-driven method of some embodiments also dynamically deploys edge forwarding elements in the SDDC for applications and/or tenants after detecting the need for the edge forwarding elements based on monitored traffic flow conditions.

Systems and methods for performing self-contained posture assessment from within a protected portable-code workspace are described. In some embodiments, an Information Handling System (IHS) may include a processor and a memory having program instructions that, upon execution, cause the IHS to: transmit, from an orchestration service to a local agent, a workspace definition that references an application, where the application comprises a first portion of code provided by a developer and a second portion of code provided by the orchestration service; and receive, from a local agent at the orchestration service, a message in response to the execution of the second portion of code within a workspace instantiated based upon the workspace definition. The second portion of code may inspect the contents of the runtime memory of the workspace upon execution, for example, by performing a stack canary check, a hash analysis, a boundary check, and/or a memory scan.

A system and method for robotically arbitrating cloud computing services utilizes resource parameters, tolerance values, and client system requirements to configure a meta-orchestrator to select a validated compatible service from a service resource pool and employ an orchestrator to migrate a client system to the selected service and utilize block chain technology for logging transactions, storing metadata and data.

Systems and methods are described for enabling quantum computing at an edge node of a network. For example, a machine learning component residing on each of a plurality of edge nodes of the network may be implemented to distribute application processing by network location and processing type, including distribution among classical processing at a central cloud, classical processing at an edge node, and quantum processing at a quantum edge node including a quantum computing device. By distributing certain applications, such as latency-sensitive applications of a higher order of complexity, to an edge node, and particularly a quantum edge node, latency may be reduced and complex application code may be processed more quicky using quantum computations. For applications to be processed using quantum processing, the machine learning component may further identify qubits for the quantum processing and define containers based on the qubits for deployment by the quantum computing device.

Techniques are described herein that are capable of dynamically re-allocating computing resources while maintaining network connection(s). Applications of users are run in a computing unit. Computing resources are allocated among the applications based at least in part on dynamic demands of the applications for the computing resources and resource limits associated with the respective customers. In a first example, the computing resources are dynamically re-allocated among the applications, as a result of changing the resource limit of at least one customer, while maintaining at least one network connection between a client device of each customer and at least one respective application. In a second example, the computing resources are dynamically re-allocated among the applications, as a result of changing the resource limit of at least one customer, while maintaining at least one network connection between an interface and a client device of each customer.

A method, a device, and a non-transitory storage medium are described in which multi-tiered networks and resource utilization-based provisioning service is provided. A multi-tiered mobile edge computing network that includes multiple mobile edge computing networks that are multi-tiered based on distance from a network edge includes a network device that selects a location to provision an application service for an end device based on a total resource utilization value and a performance metric associated with one or multiple candidate mobile edge computing networks.

Various approaches for allocating resources to an application having multiple application components, with at least one executing one or more functions, in a serverless service architecture include identifying multiple routing paths, each routing path being associated with a same function service provided by one or more containers or serverless execution entities; determining traffic information on each routing path and/or a cost, a response time and/or a capacity associated with the container or serverless execution entity on each routing path; selecting one of the routing paths and its associated container or serverless execution entity; and causing a computational user of the application to access the container or serverless execution entity on the selected routing path and executing the function(s) thereon.

“Resource guarantee” refers to a unit of a resource that is guaranteed and therefore designated to a consumer. A multi-phased constraint programming (CP) approach is used to determine assignments of resource guarantees of a set of consumers to a set of hosts in a resource system. Phase I uses CP to segregate non-split consumers from split consumers. Phase II uses CP to assign each cotenant group of non-split consumers to a respective host. Phase III uses CP to assign resource guarantees of the split consumers across the hosts, wherein resource guarantees of a single split consumer may be splits across different hosts. Each phase involves execution of a CP solver based on a different CP data model. A CP data model declaratively expresses combinatorial properties of a problem in terms of constraints. CP is a form of declarative programming.

Device Assisted Services (DAS) for protecting network capacity is provided. In some embodiments, DAS for protecting network capacity includes monitoring a network service usage activity of the communications device in network communication; classifying the network service usage activity for differential network access control for protecting network capacity; and associating the network service usage activity with a network service usage control policy based on a classification of the network service usage activity to facilitate differential network access control for protecting network capacity.

Some embodiments provide a method for a software-defined wide area network (SD-WAN) connecting first and second sites, with the first site including an edge node and the second site including multiple forwarding hub nodes. At the edge node of the first site, the method receives a packet of a particular flow including a flow attribute. The method uses the flow attribute to identify a hub-selection rule from multiple hub-selection rules, each hub-selection rule identifying at least one forwarding hub node at the second site for receiving one or more flows from the first site, and at least one hub-selection rule identifying at least one forwarding hub node that is not identified by another hub-selection rule. The method uses the identified hub-selection rule to identify a forwarding hub node for the particular flow. The method then sends the packet from the edge node at the first site to the identified forwarding hub node at the second site.