In one embodiment, a monitoring engine obtains mesh flow data for traffic flows between nodes in a service mesh. The monitoring engine associates the mesh flow data with network traffic between an endpoint device and an edge of the service mesh. The monitoring engine identifies, based on the mesh flow data, a particular container workload associated with the traffic flows. The monitoring engine provides an indication that the particular container workload is associated with the network traffic between the endpoint device and the edge of the service mesh.

In one embodiment, a monitoring engine obtains mesh flow data for traffic flows between nodes in a service mesh. The monitoring engine associates the mesh flow data with network traffic between an endpoint device and an edge of the service mesh. The monitoring engine identifies, based on the mesh flow data, a particular container workload associated with the traffic flows. The monitoring engine provides an indication that the particular container workload is associated with the network traffic between the endpoint device and the edge of the service mesh.

Various embodiments are directed to receiving, at a receiving device, a packet from a node in a first network. determining a version identifier for the packet, encoding the version identifier into the packet, and transmitting the packet containing the encoded version identifier to a load balancing device in a second network. The version identifier may be encoded into a destination port field of the packet. The receiving device may be a perimeter network address translation device. The packet is received at the load balancing device, where the version identifier is extracted and a hash of source address information is performed. The version and hash are used to select a back-end device in the second network. The packet is transmitted to the selected back-end device.

According to one embodiment, a method, computer system, and computer program product for cloud service brokerage. The embodiment may include receiving a data set and user defined contextual parameters relating to a machine learning (ML) problem of a user to be performed on the data set. The embodiment may include identifying a resource requirement of the ML problem and available resources. The embodiment may include enabling user configuration of the contextual parameters in an interactive augmented reality (AR) view. The embodiment may include identifying a set of clusters upon which to execute computing tasks of the ML problem. The set of clusters is identified out of the available resources. The embodiment may include implementing a ML evaluation process to determine an optimized load distribution model for execution of the computing tasks within the set of clusters. The embodiment may include implementing the optimized load distribution model.

A device, system and method for changing a topology of a geographically distributed system is provided. One or more computing devices determine resource usage of an initial topology of a geographically distributed system that includes data stored at one or more initial locations and applications, that consume the data, being implemented at the one or more of initial locations. The computing device(s) determine projected resource usage of additional topologies of the geographically distributed system, the additional topologies defining respective locations, different from the one or more initial locations, where respective portions of the data and/or the applications are located. In response to an additional topology having a respective projected resource usage that is less than the resource usage of the initial topology, the computing device(s) control a respective portion of the data and/or the applications to move to the respective locations defined by the additional topology.

A method for allocating a device-specific resource from one or more databases is provided. The method includes receiving, at an interface, a coupling identifier including a pool identifier and a resource identifier, as part of a processing request from a requesting entity, the processing request including a request for the device-specific resource, wherein the coupling identifier associates the requesting entity with the device-specific resource based on the resource identifier, extracting, at the interface, the pool identifier from the coupling identifier, identifying, by the interface, the processing service in which the device-specific resource associated with the resource identifier is cached, based on the pool identifier, and transmitting, from the interface to the identified processing service, at least a part of the processing request to process the cached requested device-specific resource.

Systems, methods, and other embodiments described herein relate to determining an optimal data offloading rate for one or more connected mobile devices. In one embodiment, a method includes receiving mobile device transmission information and receiving edge server resource information. The method includes determining an offloading rate for a mobile device to an edge server based on the mobile device transmission information, the edge server resource information, and a mean-field game algorithm. The method includes outputting the offloading rate to the mobile device.

Systems, methods, and other embodiments described herein relate to determining an optimal data offloading rate for one or more connected mobile devices. In one embodiment, a method includes receiving mobile device transmission information and receiving edge server resource information. The method includes determining an offloading rate for a mobile device to an edge server based on the mobile device transmission information, the edge server resource information, and a mean-field game algorithm. The method includes outputting the offloading rate to the mobile device.

A network edge computing method includes receiving, by an edge data node, a service request at least processed by network edge computation scheduling; and routing, according to a service port involved in the service request, the service request to a container of the edge data node, to be processed by the container.

A system designed for increasing network communication speed for users, while lowering network congestion for content owners and ISPs. The system employs network elements including an acceleration server, clients, agents, and peers, where communication requests generated by applications are intercepted by the client on the same machine. The IP address of the server in the communication request is transmitted to the acceleration server, which provides a list of agents to use for this IP address. The communication request is sent to the agents. One or more of the agents respond with a list of peers that have previously seen some or all of the content which is the response to this request (after checking whether this data is still valid). The client then downloads the data from these peers in parts and in parallel, thereby speeding up the Web transfer, releasing congestion from the Web by fetching the information from multiple sources, and relieving traffic from Web servers by offloading the data transfers from them to nearby peers.