A service processing method, performed by a cloud application management server, includes: upon receiving an allocation request from a target terminal, acquiring N pieces of selection reference information corresponding to a pending edge server and related to the target terminal and running reference information, the pending edge server being one of P edge servers connected to the cloud application management server; upon determining that the pending edge server meets a requirement of providing a running service of a target cloud application for the target terminal, determining a connection reference score corresponding to the pending edge server; storing the connection reference score and identification information about the pending edge server into a candidate set; and transmitting the candidate set to the target terminal.

The disclosure provides an approach for distribution of functions among data centers of a cloud system that provides function-as-a-service (FaaS). For example, the disclosure provides one or more function distributors configured to receive a request for loading or executing a function, automatically determine an appropriate data center to load or execute the function, and automatically load or execute the function on the determined data center. In certain embodiments, the function distributors are further configured to determine an appropriate data center to provide storage resources for the function and configure the function to utilize the storage resources of the determined data center.

The present disclosure describes a technique for honoring virtual machine placement constraints established on a first host implemented on a virtualized computing environment by receiving a request to migrate one or more virtual machines from the first host to a second host and without violating the virtual machine placement constraints, identifying an architecture of the first host, provisioning a second host with an architecture compatible with that of the first host, adding the second host to the cluster of hosts, and migrating the one or more virtual machines from the first host to the second host.

Computing node identifiers can be used to encode information regarding the distance between requesting and available computing nodes. Computing node identifiers can be computed based on proximity values for respective computing nodes. Requests can be directed from one computing node to an available computing node based on information encoded by both the computing node identifiers of the requesting node and the receiving node. Using these computing node identifiers to direct request traffic among VMs can more efficiently leverages network resources.

Network capacity is provisioned in a computing environment comprising a computing service provider and an edge computing network. A cost function is applied to usage data for a number of user endpoints at the edge computing network, a number and type of workloads at the edge computing network, offload capability of the edge computing network, and resource capacities at the edge computing network. An estimated network capacity is determined, where the workloads are dynamic, and the cost function is usable to optimize the network capacity with respect to one or more criteria.

Techniques for optimizing virtual machine (VM) scheduling on a non-uniform cache access (NUCA) system are provided. In one set of embodiments, a hypervisor of the NUCA system can partition the virtual CPUs of each VM running on the system into logical constructs referred to as last level cache (LLC) groups, where each LLC group is sized to match (or at least not exceed) the LLC domain size of the system. The hypervisor can then place/load balance the virtual CPUs of each VM on the system’s cores in a manner that attempts to keep virtual CPUs which are part of the same LLC group within the same LLC domain, subject to various factors such as compute load, cache contention, and so on.

A computer-implemented method, a computer program product, and a computer system for determining optimal data access for deep learning applications on a cluster. A server determines candidate cache locations for one or more compute nodes in the cluster. The server fetches a mini-batch of a dataset located at a remote storage service into the candidate cache locations. The server collects information about time periods of completing a job on the one or more nodes, where the job is executed against fetched mini-batch at the candidate cache locations and the mini-batch at the remote storage location. The server selects, from the candidate cache locations and the remote storage location, a cache location. The server fetches the data of the dataset from the remote storage service to the cache location, and the one or more nodes execute the job against fetched data of the dataset at the cache location.

A system for query processing of a frequency of utility indicators comprises a processor operable to receive a transmission from a first wearable device comprising entity file information associated with a first entity. The processor is operable to generate a file vector comprising one or more files of a digital folder based on an association with one or more utility indicators and determine that one of the files corresponds to a greater number of the one or more utility indicators than the remaining files based, at least in part, on the entity file information. The processor is operable to assign the determined one of the one or more files as a first file within the file vector and send a transmission to the first wearable device comprising the file vector and an indication to utilize the first file in an interaction between the first user and the first entity.

Systems and methods for enabling various devices to remotely interact with cloud-based client applications are provided. A method comprises receiving a first request from a first client device of a user to initiate an interactive session with a cloud-based client application, reserving an application engine for executing the cloud-based client application remotely from the first client device, receiving interaction data from the first client device as the user engages with a first media data associated with the cloud-based client application, modifying the cloud-based client application executing within the application engine that is reserved based on the interaction data received from the first client device, receiving a second request from the first client device to end the interactive session with the cloud-based client application that is modified, and deallocating the application engine that is reserved, wherein the application engine that is reserved is delinked from the first client device.

The disclosed method is applicable to a many-core system. The method includes: acquiring multiple pieces of .routing information, each of which includes two logical nodes and a data transmission amount between the two logical nodes; determining a piece of unprocessed routing information with a maximum data transmission amount as current routing information; mapping each unlocked logical node of the current routing information to one unlocked processing node, and locking the mapped logical node and processing node, wherein if there is an unlocked edge processing node, the unlocked logical node is mapped to the unlocked edge processing node; and returning, if there is at least one unlocked logical node, to the step of determining the piece of unprocessed routing information with the maximum data transmission amount as the current routing information.