Abstract: Embodiments of the present disclosure provide a resource processing method of a cloud platform and a related computing device. The cloud platform includes a plurality of nodes, and each node includes a first resource and a second resource. The method is performed by the computing device and/or a host manager in the cloud platform, and includes: obtaining a current first resource allocation ratio and a current second resource allocation ratio of each of the plurality of nodes; obtaining a first resource limit and a second resource limit that are required by a newly added host; determining, among the plurality of modes, a target node according to the current first resource allocation ratio and the current second resource allocation ratio of each node, the first resource limit, and the second resource limit; and deploying the newly added host in the target node.

Abstract: In one embodiment, a processor includes: a plurality of cores each comprising a multi-threaded core to concurrently execute a plurality of threads; and a control circuit to concurrently enable at least one of the plurality of cores to operate in a single-threaded mode and at least one other of the plurality of cores to operate in a multi-threaded mode. Other embodiments are described and claimed.

Abstract: A data processing method includes receiving, by a source physical machine, a first data packet including first data, and a destination address of the first data is a first virtual machine, generating a second data packet including the first data and an identifier of a second virtual machine, where the second virtual machine is the first virtual machine after being live migrated from the source physical machine to a destination physical machine, and the identifier of the second virtual machine identifies the second virtual machine on the destination physical machine, and sending, by the source physical machine, the second data packet to the destination physical machine.

Abstract: An apparatus to facilitate disaggregated computing for a distributed confidential computing environment is disclosed. The apparatus includes a source remote direct memory access (RDMA) network interface controller (RNIC); a queue to store a data entry corresponding to an RDMA request between the source RNIC and a sink RNIC; a data buffer to store data for an RDMA transfer corresponding to the RDMA request, the RDMA transfer between the source RNIC and the sink RNIC; and a trusted execution environment (TEE) comprising an authentication tag controller to: initialize a first authentication tag calculated using a first key known between a source consumer generating the RDMA request and the source RNIC; associate the first authentication tag with the data entry as integrity verification; initialize a second authentication tag calculated using a second key; and associate the second authentication tag with the data buffer as integrity verification for the data buffer.

Abstract: After obtaining a virtual machine migration message, a control node performs first network configuration on a virtual switch of a new virtual machine. After performing the first network configuration, the control node creates the new virtual machine, and performs second network configuration on a second virtual switch in a virtual network in which a to-be-migrated virtual machine is located. In the present application, before the new virtual machine is created, a network connection of the to-be-migrated virtual machine is maintained, and a part of network configuration for the new virtual machine is completed.

Abstract: Adaptive control of deadline-constrained workload migrations can include monitoring migrations of workloads forming a wave migrating from a source computing node to a target computing node. The monitoring can be performed in real time. The migrations can be performed by transferring image replications of each workload over a data communication network. Based on an expected bandwidth availability, a likelihood that a cutover deadline associated with the wave is exceeded prior to completing a migration of each of the wave's workloads can be predicted. Migration of one or more selected workloads can be suspended in response to determining that exceeding the cutover deadline prior to completing migration of each of the wave's workloads is likely.

Abstract: Technologies for providing efficient migration of services include a server device. The server device includes compute engine circuitry to execute a set of services on behalf of a terminal device and migration accelerator circuitry. The migration accelerator circuitry is to determine whether execution of the services is to be migrated from an edge station in which the present server device is located to a second edge station in which a second server device is located, determine a prioritization of the services executed by the server device, and send, in response to a determination that the services are to be migrated and as a function of the determined prioritization, data utilized by each service to the second server device of the second edge station to migrate the services. Other embodiments are also described and claimed.

Abstract: In a method of scheduling jobs in a storage device, a first job is performed. While the first job is being performed, a first timestamp for the first job is pre-updated based on a pre-defined operation time table that represents a relationship between operations of the storage device and operation times. While the first job is being performed, scheduling requests are received that are associated with a second job to be performed after the first job. While the first job is being performed, a scheduling operation for selecting the second job is pre-performed based on the scheduling requests and the timestamps.

Abstract: Systems, methods, and machine-readable media for predicting interruptions to the use of spare cloud resources and rebalancing based on those predictions are disclosed. A computing platform collects data for customers over time. The computing platform runs a machine learning algorithm on the historical data to generate a prediction classifier. The prediction classifier relates to a time window for prediction into the future, on the order of minutes or hours. The prediction classifier is run on monitored data from ongoing activity with a cloud provider to generate a risk score. Each risk score may identify an amount of risk that a spare cloud resource related to new resource metrics data will be interrupted within the future time frame corresponding to that prediction classifier. If predicted to be interrupted, the customer may be assisted in rebalancing to other resources. As a result, interruptions can be predicted hours into the future.

Abstract: Embodiments relate to a virtualization layer capturing replayable execution traces of VMs managed by the virtualization layer. Execution tracing can be performed on any unit of execution managed by the virtualization layer, e.g., threads, processes, virtual processors, individual VMs, multiple VMs, etc. Traced execution units may be executing in parallel. Execution tracing involves capturing to a buffer: executed instructions, memory inputted to instructions, memory outputted by instructions, registers touched by instructions, and ordering markers. Trace data can be captured in chunks, where causality is preserved and ordering is preserved between chunks but not necessarily within chunks. The chunks may be delineated by inserting monotonically increasing markers between context switches, thus relatively ordering the chunks. Determinism may be partially provided by identifying non-deterministic events. VM tracing may be transparent to guest software, which need not be instrumented.

Abstract: Methods, computing systems, and technology for scheduling computing tasks to be performed by an unreliable device are presented. For example, a task management system may receive, from a first electric vehicle, vehicle data comprising computing capabilities of the first electric vehicle and an availability calendar of the first electric vehicle. Additionally, the system can generate, based on the computing capabilities of the first electric vehicle, a work package for the first electric vehicle. The work package can include a computing task for the first electric vehicle to complete. Moreover, the system can determine, based on the availability calendar, one or more transmission parameters for communicating the work package to the first electric vehicle. Subsequently, based on the transmission parameters, the system can transmit a request for the first electric vehicle to perform the computing task of the work package.

Abstract: An apparatus to facilitate disaggregated computing for a distributed confidential computing environment is disclosed. The apparatus includes a processor executing a trusted execution environment (TEE) comprising a field-programmable gate array (FPGA) driver to interface with an FPGA device that is remote to the apparatus; and a remote memory-mapped input/output (MMIO) driver to expose the FPGA device as a legacy device to the FPGA driver, wherein the processor to utilize the remote MMIO driver to: enumerate the FPGA device using FPGA enumeration data provided by a remote management controller of the FPGA device, the FPGA enumeration data comprising a configuration space and device details; load function drivers for the FPGA device in the TEE; create corresponding device files in the TEE based on the FPGA enumeration data; and handle remote MMIO reads and writes to the FPGA device via a network transport protocol.

Abstract: An apparatus comprises a processing device configured to obtain information associated with services hosted in an information technology infrastructure comprising a core data center hosting a first subset of the services and edge computing sites hosting a second subset of the services. The processing device is also configured to determine, based on the obtained information, values associated with parameters characterizing suitability of hosting respective ones of the services at the computing sites, and to generate, based on the determined values, scores for each of the services. The processing device is further configured to identify, based on the generated scores, at least a given one of the services to be migrated from the core data center to the edge computing devices or from the edge computing sites to the core data center. The processing device is further configured to migrate the given one of the services.

Abstract: A method to optimize the usage of these crypto resources for effectively sharing the resource with identical configuration crypto material and thereby reducing the operation overhead associated with cryptographic operations is disclosed.

Abstract: A method for execution by a record processing and storage system includes assigning each of a plurality of key space sub-intervals of a cluster key domain to a corresponding one of a plurality of processing core resources, and generating a plurality of segments from the set of records via the plurality of processing core resources. Each processing core resource in the plurality of processing core resources generates a subset of the plurality of segments by identifying a proper subset of the set of records based on having cluster key values included in a corresponding one of the plurality of key space sub-intervals, and by generating the subset of the plurality of segments to include the proper subset of the set of records.

Abstract: A method includes starting a second virtual machine using a first virtualization emulator of a later version when a first virtual machine is running based on a second virtualization emulator of an earlier version, where the second virtual machine has a same configuration as the first virtual machine, suspending the first virtual machine and the second virtual machine, sending device status information of the first virtual machine to the second virtual machine, and controlling the second virtual machine to resume running based on the device status information.

Abstract: A computer-implemented method according to one embodiment includes receiving data associated with a driver performing actions. At least some of the actions trigger events emitted by an event emitter. Information, from the received data, about the performed actions is logged in an action log. An event observer is instructed to log the events emitted by the event emitter that the event observer observes. The observed events are logged in an event log. The information of the action log and information of the event log is compared based on a rule, and a validity of the event emitter is determined based on results of the comparing. A computer program product according to another embodiment includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a processor to cause the processor to perform the foregoing method.

Abstract: A simulated annealing-based metaheuristic method for scheduling tasks in the infrastructures that use cloud computing service with multitasking and multi-node structure that can perform the big data analysis.

Abstract: Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: receiving, by a manager node, from a plurality of compute nodes metrics data, the manager node and the plurality of compute nodes defining a first local cluster of a first computing environment, wherein nodes of the compute nodes defining the first local cluster have running thereon container based applications, wherein a first container based application runs on a first compute node of the plurality of compute nodes defining the first local cluster, and wherein a second compute node of the plurality of compute nodes defining the first local cluster runs a second container based application; wherein the manager node has received from an orchestrator availability data specifying a set of compute nodes available for hosting the first application.

Abstract: A method for managing memory for applications in a computing system includes receiving a selection of a preferred application. During user-controlled operation over the application, the transitions of selected application between foreground and background are monitored. A retention of the application in memory is triggered upon a transition of the application to background during the user operation. Retention of the application includes compressing memory portions of the application. Accordingly, the application is retained within the memory based on said compressed memory portions. A requirement to restore the retained application is sensed based on either a user selection or an automatically generated prediction and the application is restored from the retained state back to the foreground.