Abstract: Techniques discussed herein relate to implementing a distributed computing cluster (the “cluster”) including a plurality of edge devices (e.g., devices individually configured to selectively execute within an isolated computing environment). One edge device may be configured to operate as a head node of the cluster at a given time. A request for virtual resources of the cluster may be received from a user device and directed to the first edge device of the cluster. The first edge device may determine it is not operating as a head node of the cluster. The first edge device may determine that a second edge device of the cluster is operating as the head node. In response, the first edge device may forward the request to the second edge device, wherein forwarding the request to the second edge device causes the second request to be processed by the cluster.

Abstract: Techniques discussed herein relate to providing in-memory workflow management at an edge device (e.g., a computing device distinct from and operating remotely with respect to a data center). The edge device can operate as a computing node in a computing cluster of edge devices and implement a hosting environment (e.g., a distributed data plane). A work request can be obtained by an in-memory workflow manager of the edge device. The work request may include an intended state of a data plane resource (e.g., a computing cluster, a virtual machine, etc.). The in-memory workflow manager can determine the work request has not commenced and initialize an in-memory execution thread to execute orchestration tasks to configure a data plane of the computing cluster according to the intended state. Current state data corresponding to the configured data plane may be provided to the user device and eventually displayed.

Abstract: Techniques discussed herein relate to implementing a distributed computing cluster (the “cluster”) including a plurality of edge devices (e.g., devices individually configured to selectively execute within an isolated computing environment). One edge device may be configured to operate as a head node of the cluster at a given time. A request for virtual resources of the cluster may be received from a user device and directed to the first edge device of the cluster. The first edge device may determine it is not operating as a head node of the cluster. The first edge device may determine that a second edge device of the cluster is operating as the head node. In response, the first edge device may forward the request to the second edge device, wherein forwarding the request to the second edge device causes the second request to be processed by the cluster.

Abstract: Techniques discussed herein relate to implementing a distributed computing cluster (the “cluster”) including a plurality of edge devices (e.g., devices individually configured to selectively execute within an isolated computing environment). One edge device may be configured to operate as a head node of the cluster at a given time. A request for virtual resources of the cluster may be received from a user device and directed to the first edge device of the cluster. The first edge device may determine it is not operating as a head node of the cluster. The first edge device may determine that a second edge device of the cluster is operating as the head node. In response, the first edge device may forward the request to the second edge device, wherein forwarding the request to the second edge device causes the second request to be processed by the cluster.

Abstract: Techniques discussed herein relate to providing in-memory workflow management at an edge device (e.g., a computing device distinct from and operating remotely with respect to a data center). The edge device can operate as a computing node in a computing cluster of edge devices and implement a hosting environment (e.g., a distributed data plane). A work request can be obtained by an in-memory workflow manager of the edge device. The work request may include an intended state of a data plane resource (e.g., a computing cluster, a virtual machine, etc.). The in-memory workflow manager can determine the work request has not commenced and initialize an in-memory execution thread to execute orchestration tasks to configure a data plane of the computing cluster according to the intended state. Current state data corresponding to the configured data plane may be provided to the user device and eventually displayed.

Abstract: Techniques discussed herein relate to providing in-memory workflow management at an edge device (e.g., a computing device distinct from and operating remotely with respect to a data center). The edge device can operate as a computing node in a computing cluster of edge devices and implement a hosting environment (e.g., a distributed data plane). A work request can be obtained by an in-memory workflow manager of the edge device. The work request may include an intended state of a data plane resource (e.g., a computing cluster, a virtual machine, etc.). The in-memory workflow manager can determine the work request has not commenced and initialize an in-memory execution thread to execute orchestration tasks to configure a data plane of the computing cluster according to the intended state. Current state data corresponding to the configured data plane may be provided to the user device and eventually displayed.

Abstract: Techniques discussed herein relate to providing in-memory workflow management at an edge device (e.g., a computing device distinct from and operating remotely with respect to a data center). The edge device can operate as a computing node in a computing cluster of edge devices and implement a hosting environment (e.g., a distributed data plane). A work request can be obtained by an in-memory workflow manager of the edge device. The work request may include an intended state of a data plane resource (e.g., a computing cluster, a virtual machine, etc.). The in-memory workflow manager can determine the work request has not commenced and initialize an in-memory execution thread to execute orchestration tasks to configure a data plane of the computing cluster according to the intended state. Current state data corresponding to the configured data plane may be provided to the user device and eventually displayed.

Abstract: Techniques are described for implementing a virtual smart network interface card to facilitate data transmission in an edge device providing cloud-computing operations. An edge device can implement a private virtual network that includes a private virtual network data plane. The edge device can execute a virtual machine to be connected to the private virtual network. To establish the connection, the edge device can generate a virtual network interface that includes a first endpoint and a second endpoint and is hosted within the private virtual network data plane. The edge device can associate the first endpoint with the virtual machine and associate the second endpoint with an orchestration module of the private virtual network data plane. The virtual machine can then send a data packet to the orchestration module via the virtual network interface.

Abstract: Techniques discussed herein relate to implementing a distributed computing cluster (the “cluster”) including a plurality of edge devices (e.g., devices individually configured to selectively execute within an isolated computing environment). One edge device may be configured to operate as a head node of the cluster at a given time. A request for virtual resources of the cluster may be received from a user device and directed to the first edge device of the cluster. The first edge device may determine it is not operating as a head node of the cluster. The first edge device may determine that a second edge device of the cluster is operating as the head node. In response, the first edge device may forward the request to the second edge device, wherein forwarding the request to the second edge device causes the second request to be processed by the cluster.

Abstract: Techniques are described for implementing a secure enclave within an edge device (e.g., an edge device of a computing cluster of edge devices). In some embodiments, a service enclave comprising a plurality of services can be implemented. The plurality of services can be implemented within respective containers and communicatively connected to one another via a virtual substrate network of the cloud-computing edge device. The virtual substrate network may be dedicated to network traffic between services of the plurality of services. A first service of the enclave may generate and transmit a message to a second service of the enclave for processing. One or more operations may be executed by the second service based on reception of the message.

Abstract: Techniques are disclosed for migrating one or more services from an edge device to a cloud computing environment. In one example, a migration service receives a request to migrate a first set of services from the edge device to the cloud computing environment. The migration service identifies a hardware profile of a computing device (or devices) of the cloud computing environment that matches the edge device, and then configures the computing device to execute a second set of services that corresponds to the first set of services. The migration service establishes a communication channel between the edge device and the computing device, and then executes a set of migration operations such that the second set of services is configured to execute as the first set of services. The computing device may operate in a virtual bootstrap environment or dedicated region of the cloud computing environment.