This disclosure describes techniques for providing virtual resources (e.g., containers, virtual machines, etc.) of a clustered application with information regarding a cluster of physical servers on which the distributed clustered application is running. A virtual resource that supports the clustered application is executed on a physical server of the cluster of physical servers. The virtual resource may receive an indication of a database instance (or other application) running on a particular physical server of the cluster of physical servers that is nearest the physical server. The database instance may be included in a group of database instances that are maintaining a common data set on respective physical servers of the group of physical servers. The virtual resource may then access the database instance on the particular physical server based at least in part on the database instance running on the particular server that is nearest the physical server.

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.

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.

In some examples, a system receives a first unit of work to be scheduled in the system that includes a plurality of collections of processing units to execute units of work, where each respective collection of processing units of the plurality of collections of processing units is associated with a corresponding scheduling queue. The system selects, for the first unit of work according to a first criterion, candidate collections from among the plurality of collections of processing units, and enqueues the first unit of work in a schedule queue associated with a selected collection of processing units that is selected, according to a selection criterion, from among the candidate collections.

Examples include techniques associated with mapping system memory physical addresses to proximity domains. Examples include mapping system memory physical addresses for a memory coupled with a multi-die system to proximity domains that include cores of a multi-core processor and the associated level 3 (L3) cache for use by each core included in a respective proximity domain. The mapping is to facilitate cache line ownership of a cache line in an L3 cache by an input/output device or agent located on a separate die from the multi-core processor.

A method for automatically and dynamically allocating edge computing resources to autonomous vehicles is provided. The method may include determining a data processing speed associated with at least one autonomous vehicle. The method may further include automatically determining a computing performance of hardware and software components associated with the at least one autonomous vehicle relative to the computing performance of the hardware and software components of the connected network of autonomous vehicles. The method may further include, automatically identifying at least one edge computing resource necessary for reaching the synchronized threshold level of data processing speed associated with the connected network of autonomous vehicles. The method may further include dynamically allocating the at least one edge computing resource from an edge computing device to the at least one autonomous vehicle based on a location of the at least one autonomous vehicle to the edge computing device.

Embodiments of the present disclosure disclose a method and apparatus for allocating a server resource, an electronic device and a computer readable storage medium, and relate to the technical fields of cloud platform, cloud environment, containerization and resource allocation. A specific implementation of the method comprises: acquiring a container group creation request initiated by a user for creating a target container group; determining a required amount of server resources required by the user and a remaining amount of the server resources according to the container group creation request, the remaining amount comprising at least one of an exclusive server resource or a shared server resource; rating qualities of the remaining amount of server resources in the remaining amount, and selecting a target server resource corresponding to the required amount according to an obtained actual rating; and allocating the target server resource to the user for creating the target container group.

In some examples, a system receives a first unit of work to be scheduled in the system that includes a plurality of collections of processing units to execute units of work, where each respective collection of processing units of the plurality of collections of processing units is associated with a corresponding scheduling queue. The system selects, for the first unit of work according to a first criterion, candidate collections from among the plurality of collections of processing units, and enqueues the first unit of work in a schedule queue associated with a selected collection of processing units that is selected, according to a selection criterion, from among the candidate collections.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, are described for scheduling tasks of ML workloads. A system receives requests to perform the workloads and determines, based on the requests, resource requirements to perform the workloads. The system includes multiple hosts and each host includes multiple accelerators. The system determines a quantity of hosts assigned to execute tasks of the workload based on the resource requirement and the accelerators for each host. For each host in the quantity of hosts, the system generates a task specification based on a memory access topology of the host. The specification specifies the task to be executed at the host using resources of the host that include the multiple accelerators. The system provides the task specifications to the hosts and performs the workloads when each host executes assigned tasks specified in the task specifications for the host.

In a computer system having multiple memory proximity domains including a first memory proximity domain with a first processor and a first memory and a second memory proximity domain with a second processor and a second memory, latencies of memory access from each memory proximity domain to its local memory as well as to memory at other memory proximity domains are probed. When there is no contention, the local latency will be lower than remote latency. If the contention at the local memory proximity domain increases and the local latency becomes large enough, memory pages associated with a process running on the first processor are placed in the second memory proximity domain, so that after the placement, the process is accessing the memory pages from the memory of the second memory proximity domain during execution.