A method comprising discovering workload attributes and identify dependencies, receiving utilization performance measurements including memory utilization measurements of at least a subset of workloads, grouping workloads based on the workload attributes, the dependencies, and the utilization performance measurements into affinity groups, determining at least one representative synthetic workload for each affinity group, each representative synthetic workload including a time slice of a predetermined period of time when there are maximum performance values for any number of utilization performance measurements among virtual machines of that particular affinity group, determining at least one cloud service provider (CSP)'s cloud services based on performance of the representative synthetic workloads, and generating a report for at least one of the representative synthetic workloads, the report identifying the at least one of the representative synthetic workloads and the at least one CSP's cloud services including cloud workload cost.

A method comprising discovering workload attributes and identify dependencies, receiving utilization performance measurements including memory utilization measurements of at least a subset of workloads, grouping workloads based on the workload attributes, the dependencies, and the utilization performance measurements into affinity groups, determining at least one representative synthetic workload for each affinity group, each representative synthetic workload including a time slice of a predetermined period of time when there are maximum performance values for any number of utilization performance measurements among virtual machines of that particular affinity group, determining at least one cloud service provider (CSP)'s cloud services based on performance of the representative synthetic workloads, and generating a report for at least one of the representative synthetic workloads, the report identifying the at least one of the representative synthetic workloads and the at least one CSP's cloud services including cloud workload cost.

A method for fetching a content from a web server to a client device is disclosed, using tunnel devices serving as intermediate devices. The tunnel device is selected based on an attribute, such as IP Geolocation. A tunnel bank server stores a list of available tunnels that may be used, associated with values of various attribute types. The tunnel devices initiate communication with the tunnel bank server, and stays connected to it, for allowing a communication session initiated by the tunnel bank server. Upon receiving a request from a client to a content and for specific attribute types and values, a tunnel is selected by the tunnel bank server, and is used as a tunnel for retrieving the required content from the web server, using standard protocol such as SOCKS, WebSocket or HTTP Proxy. The client only communicates with a super proxy server that manages the content fetching scheme.

Systems and methods are described for providing a virtual machine (“VM”) as a service. A user device can install a VM to enable itself as an edge node. The user device can then and use a portion of its computing resources to provide the service to the endpoint device by running the VM. In an example, an edge node can directly receive a request for a service from an endpoint device. The edge node can determine that it needs assistance from another device to jointly provide the service. Then another user device which is available to operate as an edge node can join the edge team.

Systems and methods are described for providing a virtual machine (“VM”) as a service. A user device can install a VM to enable itself as an edge node. The user device can then and use a portion of its computing resources to provide the service to the endpoint device by running the VM. In an example, an edge node can directly receive a request for a service from an endpoint device. The edge node can determine that it needs assistance from another device to jointly provide the service. Then another user device which is available to operate as an edge node can join the edge team.

Technology described herein can verify readiness of a customer tenant/customer for cloud provisioning based on the customer tenant. An example method comprises collecting, by a system comprising a processor, data based on user input from a customer tenant via a user interface, the data comprising information of a configuration setting that is to be used for cloud provisioning of a device based on the configuration setting. The method comprises, in response to collecting the configuration setting, analyzing, by the system, the configuration setting by comparing the configuration setting with respect to a selected rules check. The method comprises, in response to the analyzing of the configuration setting, determining, by the system, whether the configuration setting is acceptable for use in the cloud provisioning based on the configuration setting.

Technology described herein can verify readiness of a customer tenant/customer for cloud provisioning based on the customer tenant. An example method comprises collecting, by a system comprising a processor, data based on user input from a customer tenant via a user interface, the data comprising information of a configuration setting that is to be used for cloud provisioning of a device based on the configuration setting. The method comprises, in response to collecting the configuration setting, analyzing, by the system, the configuration setting by comparing the configuration setting with respect to a selected rules check. The method comprises, in response to the analyzing of the configuration setting, determining, by the system, whether the configuration setting is acceptable for use in the cloud provisioning based on the configuration setting.

The technology provides for live migration from a first cluster to a second cluster. For instance, when requests to one or more cluster control planes are received, a predetermined fraction of the received requests may be allocated to a control plane of the second cluster, while a remaining fraction of the received requests may be allocated to a control plane of the first cluster. The predetermined fraction of requests are handled using the control plane of the second cluster. While handling the predetermined fraction of requests, it is detected whether there are failures in the second cluster. Based on not detecting failures in the second cluster, the predetermined fraction of requests allocated to the control plane of the second cluster may be increased in predetermined stages until all requests are allocated to the control plane of the second cluster.

The technology provides for live migration from a first cluster to a second cluster. For instance, when requests to one or more cluster control planes are received, a predetermined fraction of the received requests may be allocated to a control plane of the second cluster, while a remaining fraction of the received requests may be allocated to a control plane of the first cluster. The predetermined fraction of requests are handled using the control plane of the second cluster. While handling the predetermined fraction of requests, it is detected whether there are failures in the second cluster. Based on not detecting failures in the second cluster, the predetermined fraction of requests allocated to the control plane of the second cluster may be increased in predetermined stages until all requests are allocated to the control plane of the second cluster.

Systems and methods for establishing a connection with an edge application server are provided. A user equipment (UE) in a wireless communication network establishes a connection with an edge application server to offload the data processing of an application executing on the UE to the edge application server. The UE communicates key performance indicators (KPIs) associated with the application to the edge data network. The KPIs indicate the resources that application uses to process the data. In response, the UE receives edge application server parameters from multiple servers in the edge data network that meet or exceed the KPIs. The parameters include compute, graphical compute, memory and storage parameters with various levels of specificity. The UE selects one of the edge application servers to process the data on behalf of the application based on the parameters.