Method for offloading calculation tasks between a user terminal and an edge host device in a communication network according to a multi-access edge computing technique, including steps of: Offloading data necessary for the execution of the calculation from the user terminal to the edge host device, and Transmitting data resulting from the calculation carried out by the edge host device, from the edge host device to the user terminal, wherein the offloading of data is controlled on the basis of joint criteria of energy efficiency and of minimization of exposure of a user of the user terminal to electromagnetic fields.

Embodiments herein describe a CDN where anycast routing is used to identify a load balancer for selecting a cache in the CDN to use to deliver a requested object to a user. In one embodiment, the user performs a DNS lookup to identify an anycast IP address for a plurality of load balancers in the CDN. The user can then initiate anycast routing using the anycast IP address to automatically identify the closest load balancer. Once the identified balancer selects the cache, the load balancer can close the anycast connection with the user device and use an HTTP redirect to provide the user device with a unicast path to the selected cache. The user device can then establish a unicast connection with the cache to retrieve (e.g., stream) the object.

Embodiments herein describe a CDN where anycast routing is used to identify a load balancer for selecting a cache in the CDN to use to deliver a requested object to a user. In one embodiment, the user performs a DNS lookup to identify an anycast IP address for a plurality of load balancers in the CDN. The user can then initiate anycast routing using the anycast IP address to automatically identify the closest load balancer. Once the identified balancer selects the cache, the load balancer can close the anycast connection with the user device and use an HTTP redirect to provide the user device with a unicast path to the selected cache. The user device can then establish a unicast connection with the cache to retrieve (e.g., stream) the object.

A data processing system implements obtaining a set of input parameters associated with an update to be deployed to a plurality of server farms of a cloud-based service, wherein each server farm includes a primary replica configured to handle user traffic and a disaster recovery replica configured to handle user traffic responsive to a failure of the primary replica; determining temperature information for each of the server farms, ranking the server farms based on the temperature information to determine an order in which an update is to be deployed to the server farms; iteratively deploying the updates to the primary replicas of the server farms according to the ranking until an deployment threshold has been satisfied; and iteratively deploying the updates to the primary replicas of server farms for which the primary replicas have not yet been updated and to the disaster recovery replicas of the server farms.

Example methods and systems for service-aware global server load balancing are described. One example may involve a first load balancer receiving, from a client device, a request to access a service associated with an application deployed in at least a first cluster and a second cluster. In response to determination that a first pool in the first cluster is associated with an unhealthy status, the first load balancer may identify a second pool implementing the service in the second cluster, the second pool being associated with a healthy status and includes one or more second backend servers selectable by a second load balancer to process the request. Failure handling may be performed by interacting with the client device, or the second load balancer, to allow the client device to access the service implemented by the second pool in the second cluster.

Example methods and systems for service-aware global server load balancing are described. One example may involve a first load balancer receiving, from a client device, a request to access a service associated with an application deployed in at least a first cluster and a second cluster. In response to determination that a first pool in the first cluster is associated with an unhealthy status, the first load balancer may identify a second pool implementing the service in the second cluster, the second pool being associated with a healthy status and includes one or more second backend servers selectable by a second load balancer to process the request. Failure handling may be performed by interacting with the client device, or the second load balancer, to allow the client device to access the service implemented by the second pool in the second cluster.

A computer implemented method of executing a plurality of discrete software modules each including a machine learning algorithm as an executable software component configurable to approximate a function relating a domain data set to a range data set; a data store; and a message handler as an executable software component arranged to receive input data and communicate output data for the module, wherein the message handler is adapted to determine domain parameters for the algorithm based on the input data and to generate the output data based on a result generated by the algorithm, each module having associated a metric of resource utilization by the module, the method including receiving a request for a machine learning task; and selecting a module from the plurality of modules for the task based on the metric associated with the module.

A computer implemented method of executing a plurality of discrete software modules each including a machine learning algorithm as an executable software component configurable to approximate a function relating a domain data set to a range data set; a data store; and a message handler as an executable software component arranged to receive input data and communicate output data for the module, wherein the message handler is adapted to determine domain parameters for the algorithm based on the input data and to generate the output data based on a result generated by the algorithm, each module having associated a metric of resource utilization by the module, the method including receiving a request for a machine learning task; and selecting a module from the plurality of modules for the task based on the metric associated with the module.

Methods and systems for monitoring activity on a network. The systems may include a host computer executing a non-honeypot service. The host computer may also include a control module configured to enable or disable a honeypot service on the host computer in response to at least one of computational resource availability and configured tolerance for degraded service.

In some aspects, a server device may identify one or more services of a cloud infrastructure via a management layer. The server device may determine service information and configuration information for the one or more services. The server device may generate an environment model based at least in part on the service information and the configuration information, the environment model providing information on relationship between one or more components of the cloud infrastructure. The server device may determine one or more threats to the one or more services based at least in part on analyzing the environment model and accessing a threat information database. The server device may generate a threat model that lists the one or more threats to the one or more services. The server device may generate one or more recommendations for the cloud infrastructure based at least on the threat model.