A method, a system, and a computer program product for generating and/or configuring a computing instance. A request to configure a computing instance for integration with at least one first computing system is received. At least one first computing component associated with the first computing system required for integration of the configured computing instance with the first computing system is identified. At least one second computing component for inclusion into the computing instance is identified. The second computing component is not required for integration of the configured computing instance with the first computing system. A configuration of the computing instance is generated using the identified first and second computing components. The configuration of the computing instance is applied in the first computing system. The configuration of the computing instance is executed by the first computing system and stored.

Aspects of the present disclosure involve systems and methods for a service activation system in a telecommunications network that utilizes one or more generic container files for building the configuration file to instantiate the service on the network. A request for service from a network may be received from an order entry system that includes specific information about the requested service. A collection of generic configuration files may be selected based on the information included in the service order and arranged to build a configuration file to be executed on the network. The service activation system may also include a component or group of components to verify a received service order and alter the service order with default information or data where applicable. The configuration file may also be executed on the network through one or more drivers communicating with the affected devices to configure the one or more network devices.

Software performance can be automatically managed in a distributed computing environment. In one example, a system that can receive metrics information describing resource usage by a first instance of a service in a distributed computing environment. The system can also determine a quality-of-service (QoS) constraint for the service. The system can then modify a definition file based on the metrics information and the QoS constraint, the definition file being configured for deploying instances of the service in the distributed computing environment. The system can deploy a second instance of the service in the distributed computing environment using the modified definition file. As a result, the second instance can more closely satisfy the QoS constraint than the first instance.

Techniques are disclosed for utilizing directed acyclic graphs for deployment instructions. A computer-implemented method can include various operations. Instructions may be executed by a computing device to perform parses of configuration data associated with a deployment. The computing device may cause a first directed acyclic graph (DAG) to be generated, the first DAG being utilized for deploying a first resource based on the parses. A second DAG may be generated for deploying execution targets based on the parses, the second DAG specifying dependencies between execution targets of the deployment. The computing device may generate a linked list data structure based on the parses and may deploy the computing system by traversal of the linked list data structure.

Some embodiments of the invention provide a method for deploying network elements for a set of machines in a set of one or more datacenters. The datacenter set is part of one availability zone in some embodiments. The method receives intent-based API (Application Programming Interface) requests, and parses these API requests to identify a set of network elements to connect and/or perform services for the set of machines. In some embodiments, the API is a hierarchical document that can specify multiple different compute and/or network elements at different levels of compute and/or network element hierarchy. The method performs automated processes to define a virtual private cloud (VPC) to connect the set of machines to a logical network that segregates the set of machines from other machines in the datacenter set. In some embodiments, the set of machines include virtual machines and containers, the VPC is defined with a supervisor cluster namespace, and the API requests are provided as YAML files.

The present invention provides a hardware acceleration method and a communications system. The hardware acceleration method includes: sending, by a network functions virtualization orchestrator (NFVO), first request information to a virtualized infrastructure manager (VIM), wherein the first request information is configured to request the VIM to deploy the to-be-accelerated VNF onto a host in a management domain of the VIM, wherein a hardware resource of the host meets a requirement of the to-be-accelerated VNF, and the requirement of the to-be-accelerated VNF includes information indicating a type of a required hardware acceleration resource and a size of the required hardware acceleration resource in the to-be-accelerated VNF; receiving, by the VIM, the first request information from the NFVO; and deploying, by the VIM, the to-be-accelerated VNF onto the host in the management domain of the VIM.

A cost estimator system receives a plan, a configuration or proposed changes for a cloud-based infrastructure, and which include data representing one or more computing resources needed for a cloud-based application. A new configuration is generated for the cloud-based infrastructure incorporating the plan, configuration or proposed changes. A price resolver resolves a price of the resources that are part of the new configuration, and requests, from one or more cloud providers associated with the new configuration, price data for the resolved resources. The cost estimator system then generates, based on the price data received by the price resolver, a cost estimate for the new configuration of the cloud-based infrastructure.

Techniques are disclosed for implementing, by a computer infrastructure orchestration service (CIOS) changes to a first service. The CIOS may manage a declarative infrastructure provisioner to deploy infrastructure resources and artifacts based at least in part on declarative instructions. CIOS may detect a first modification to a first service (e.g., a sidecar application). CIOS may identify that configuration data of a second service references (e.g., via a git submodule) the first service. In response to identifying a second modification to the second service, CIOS may present, via a graphical interface, changes comprising the first modification to the first service. User input may be received indicating acceptance or rejection of the changes of the first modification. CIOS may then implement the changes to the first service according to the user input. For example, CIOS may ensure that the first service, as modified, is deployed alongside the second service in subsequent deployments.

Techniques for implementing rollback of infrastructure changes in an infrastructure orchestration service are described. In certain examples, an infrastructure orchestration service is disclosed that manages both provisioning and deploying of infrastructure assets within a cloud environment. The service receives a plan comprising a set of instructions associated with a set of infrastructure assets of an execution target and identifies a first state of the set of infrastructure assets. The service executes the set of instructions in the plan to achieve a second state for the set of infrastructure assets. Based in part on the executing, the service receives a trigger for rolling back the plan to restore the set of infrastructure assets in the plan to the first state and executes a rollback plan for the plan. The service then transmits a result associated with the execution of the rollback plan.

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.