A method for alleviating data poisoning in an edge computing resource includes receiving a numeric value from an Internet of Things (IoT) unit and associating the numeric value with a cluster selected from a plurality of clusters in accordance with a suitable clustering algorithm such as a k-means clustering algorithm. In at least some embodiments, the numeric value comprises a poisoned numeric value including an adversarial component injected by an adversary to negatively impact a trained model of a cloud-based artificial intelligence engine. Rather than permitting the injected adversarial component to corrupt the AI engine, a cluster with which the numeric value is associated is sampled in accordance with a probability distribution of the cluster to obtain a surrogate for the poisoned numeric value. The surrogate may then be provided as an input to an inference module of the AI engine to generate a prediction.

A distributed cluster training method and an apparatus thereof are provided. The method includes reading a sample set, the sample set including at least one piece of sample data; using the sample data and current weights to substitute into a target model training function for iterative training to obtain a first gradient before receiving a collection instruction, the collection instruction being issued by a scheduling server when a cluster system environment meets a threshold condition; sending the first gradient to an aggregation server if a collection instruction is received, wherein the aggregation server collects each first gradient and calculates second weights; and receiving the second weights sent by the aggregation server to update current weights. The present disclosure reduces an amount of network communications and an impact on switches, and avoids the use of an entire cluster from being affected.

A method and system for allocating tasks among processing devices in a data center. The method may include receiving a request to allocate a task to one or more processing devices, the request indicating a required bandwidth for performing the task, a list of predefined processing device groups connected to a host server and indicating availability of the processing device groups included therein for allocation of tasks and available bandwidth for each available processing device group, assigning the task to a processing device group having an available bandwidth greater than or equal to the required bandwidth for performing the task, and updating the list to indicate that each of the processing device group to which the task is assigned and other processing device group sharing at least one processing device is unavailable. The task may be assigned to an available processing device group having a lowest amount of power needed.

Systems and methods are provided for improving compute job distribution using federated computing nodes. This includes identifying a plurality of independently controlled computing nodes which then receive a token such that they can each be identified as being authorized to participate in a federated computing node cluster. Metrics associated with the particular nodes are then received and based on the received metrics compute jobs are assigned to the particular node by assembling a compute job data packet comprising the one or more compute jobs and transmitting the assembled compute job data packet to the particular node. Other features are also described in which assigned compute jobs and/or unrelated compute tasks can be dynamically modified in order to optimize compute job completion based on the received metrics.

In order to use zero trust network resources distributed across multiple gateways, an agent is deployed on an endpoint of an enterprise network. The agent maps requests for specific applications to corresponding gateways. The agent may also multiplex or otherwise aggregate communications among different network applications and gateways in order to provide seamless, transparent access to the distributed resources at a single endpoint, and/or within a single interface.

The present subject matter relates to a method and apparatus for managing a YAML file for Kubernetis. According to the present subject matter, the apparatus comprises a front-end component for providing a visual interface to a client, creating a YAML file in accordance with user input through the visual interface, and transforming the generated YAML file into a JavaScript Object Notation (JSON) form; and a back-end component for storing the YAML file transformed into the JSON form in a database, checking, applying and removing the YAML file, and applying the YAML file to a plurality of Kubernetes clusters such that the YAML file is executed.

The subject technology selects a particular zone among multiple zones based on a target skew to meet a global balancing of cluster instances. The subject technology deploys a particular type of cluster instance to the particular zone. The subject technology, for each zone from the multiple zones, determines a respective number of cluster instances. The subject technology identifies a second particular type of cluster instance to remove based on a total number of the second particular type of cluster instance in the multiple zones and a second total number of the particular type of cluster instance in the multiple zones. The subject technology removes the second particular type of cluster instance from a second particular zone to meet the global balancing of cluster instances in the multiple zones.

A computer system that includes a plurality of compute clusters that are located at different geographical locations. Each compute cluster is powered by a local energy source at a geographical location of that compute cluster. Each local energy source has a pattern of energy supply that is variable over time based on an environmental factor. The computer system further includes a server system that executes a global scheduler that distributes virtual machines that perform compute tasks for server-executed software programs to the plurality of compute clusters of the distributed compute platform. To distribute virtual machines for a target server-executed software program, the global scheduler is configured to select a subset of compute clusters that have different complementary patterns of energy supply such that the subset of compute clusters aggregately provide a target compute resource availability for virtual machines for the target server-executed software program.

Embodiments include an asymmetric multiprocessing (AMP) system having two or more central processing unit (CPU) clusters of a first core type and a CPU cluster of a second core type. Some embodiments include determining a control effort for an active thread group, and assigning the thread group to a first performance island according to the control effort range of the first performance island. The first performance island can include a first CPU cluster of the first core type, where a second performance island includes a second CPU cluster of the first core type, where the second performance island corresponds to a different control effort range than the first performance island. Some embodiments include assigning the first CPU cluster as a preferred CPU cluster of the first thread group, and transmitting a first signal identifying the first CPU cluster as the preferred CPU cluster assigned to the first thread group.

A gateway performs silent authentication refreshes with an identity management platform in order to extend the expiration of a cookie provided to an endpoint that accesses network applications through the gateway.