Abstract: Described are methods, systems, and media for detecting malicious activity in a network by performing operations comprising: feeding network packets from the network into a header crypto engine; sending the network packets from the header crypto engine to a work scheduler; divaricating the network packets using the work scheduler based on flow data and header data of the network packets to at least one of a firewall and a neural network processor; generating output data comprising: a first output data from the firewall according to rules of the firewall; a second output data from the neural network processor based on behavioral analysis performed by the neural network processor, wherein the second output data is used to update the rules in the firewall; and aggregating the output data from the firewall and the neural network processor to detect malicious activity in the network.

Abstract: Technologies for collecting metrics associated with a processing resource (e.g., central processing unit (CPU) resources, accelerator device resources, and the like) over a time window are disclosed. According to an embodiment presented herein, a network device receives, in an edge network, a request to provide one or more metrics associated with a processing resource, the request specifying a window indicative of a time period to capture the one or more metrics. The network device obtains the one or more metrics from the processing resource for the specified window and provides the obtained one or more metrics in response to the request.

Abstract: Technologies for providing a multi-tenant local breakout switching and dynamic load balancing include a network device to receive network traffic that includes a packet associated with a tenant. Upon a determination that the packet is encrypted, a secret key associated with the tenant is retrieved. The network device decrypts a payload from the packet using the secret key. The payload is indicative of one or more characteristics associated with network traffic. The network device evaluates the characteristics and determines whether the network traffic is associated with a workload requesting compute from a service hosted by a network platform. If so, the network device forwards the network traffic to the service.

Abstract: Examples described herein relate to requesting execution of a workload by a next function with data transport overhead tailored based on memory sharing capability with the next function. In some examples, data transport overhead is one or more of: sending a memory address pointer, virtual memory address pointer or sending data to the next function. In some examples, the memory sharing capability with the next function is based on one or more of: whether the next function shares an enclave with a sender function, the next function shares physical memory domain with a sender function, or the next function shares virtual memory domain with a sender function. In some examples, selection of the next function from among multiple instances of the next function based on one or more of: sharing of memory domain, throughput performance, latency, cost, load balancing, or service legal agreement (SLA) requirements.

Abstract: Various systems and methods for implementing a service-level agreement (SLA) apparatus receive a request from a requester via a network interface of the gateway, the request comprising an inference model identifier that identifies a handler of the request, and a response time indicator. The response time indicator relates to a time within which the request is to be handled indicates an undefined time within which the request is to be handled. The apparatus determines a network location of a handler that is a platform or an inference model to handle the request consistent with the response time indicator, and routes the request to the handler at the network location.

Abstract: A central processing unit can offload table lookup or tree traversal to an offload engine. The offload engine can provide hardware accelerated operations such as instruction queueing, bit masking, hashing functions, data comparisons, a results queue, and a progress tracking. The offload engine can be associated with a last level cache. In the case of a hash table lookup, the offload engine can apply a hashing function to a key to generate a signature, apply a comparator to compare signatures against the generated signature, retrieve a key associated with the signature, and apply the comparator to compare the key against the retrieved key. Accordingly, a data pointer associated with the key can be provided in the result queue. Acceleration of operations in tree traversal and tuple search can also occur.

Abstract: Examples are described that relate to waking up or invoking a function such as a processor-executed application or a hardware device. The application or a hardware device can specify which sources can cause wake-ups and which sources are not to cause wake-ups. A device or processor-executed software can monitor reads from or writes to a region of memory and cause the application or a hardware device to wake-up unless the wake-up is specified as inhibited. The updated region of memory can be precisely specified to allow a pinpoint retrieval of updated content instead of scanning a memory range for changes. In some cases, a write to a region of memory can include various parameters that are to be used by the woken-up application or a hardware device. Parameters can include a source of a wake-up, a timer to cap execution time, or any other information.

Abstract: Technologies for providing hardware resources as a service with direct resource addressability are disclosed. According to one embodiment of the present disclosure, a device receives a request to access a destination accelerator device in an edge network, the request specifying a destination address assigned to the destination accelerator device. The device determines, as a function of the destination address, a location of the destination accelerator device and sends the request to the destination accelerator device.

Abstract: Various systems and methods for providing adaptable sensor data collection are described herein. A system, includes: a sensor interface to receive sensor data; processing circuitry to: process the sensor data according to a configuration to generate processed sensor data; and transmit the processed sensor data to a plurality of data consumer devices; and feedback circuitry to: receive feedback from the plurality of data consumer systems; and revise the configuration based on the feedback from the plurality of data consumer systems, to modify subsequent processing of sensor data before transmitting to the plurality of data consumer systems.

Abstract: Various systems and methods for implementing a service-level agreement (SLA) apparatus receive a request from a requester via a network interface of the gateway, the request comprising an inference model identifier that identifies a handler of the request, and a response time indicator. The response time indicator relates to a time within which the request is to be handled indicates an undefined time within which the request is to be handled. The apparatus determines a network location of a handler that is a platform or an inference model to handle the request consistent with the response time indicator, and routes the request to the handler at the network location.

Abstract: Technologies for providing a multi-tenant local breakout switching and dynamic load balancing include a network device to receive network traffic that includes a packet associated with a tenant. Upon a determination that the packet is encrypted, a secret key associated with the tenant is retrieved. The network device decrypts a payload from the packet using the secret key. The payload is indicative of one or more characteristics associated with network traffic. The network device evaluates the characteristics and determines whether the network traffic is associated with a workload requesting compute from a service hosted by a network platform. If so, the network device forwards the network traffic to the service.

Abstract: An apparatus for training artificial intelligence (AI) models is presented. In embodiments, the apparatus may include an input interface to receive in real time model training data from one or more sources to train one or more artificial neural networks (ANNs) associated with the one or more sources, each of the one or more sources associated with at least one of the ANNs; a load distributor coupled to the input interface to distribute in real time the model training data for the one or more ANNs to one or more AI appliances; and a resource manager coupled to the load distributor to dynamically assign one or more computing resources on ones of the AI appliances to each of the ANNs in view of amounts of the training data received in real time from the one or more sources for their associated ANNs.

Abstract: Technologies for collecting metrics associated with a processing resource (e.g., central processing unit (CPU) resources, accelerator device resources, and the like) over a time window are disclosed. According to an embodiment presented herein, a network device receives, in an edge network, a request to provide one or more metrics associated with a processing resource, the request specifying a window indicative of a time period to capture the one or more metrics. The network device obtains the one or more metrics from the processing resource for the specified window and provides the obtained one or more metrics in response to the request.

Abstract: Various systems and methods for implementing a service-level agreement (SLA) apparatus receive a request from a requester via a network interface of the gateway, the request comprising an inference model identifier that identifies a handler of the request, and a response time indicator. The response time indicator relates to a time within which the request is to be handled indicates an undefined time within which the request is to be handled. The apparatus determines a network location of a handler that is a platform or an inference model to handle the request consistent with the response time indicator, and routes the request to the handler at the network location.

Abstract: Technologies for collecting metrics associated with a processing resource (e.g., central processing unit (CPU) resources, accelerator device resources, and the like) over a time window are disclosed. According to an embodiment presented herein, a network device receives, in an edge network, a request to provide one or more metrics associated with a processing resource, the request specifying a window indicative of a time period to capture the one or more metrics. The network device obtains the one or more metrics from the processing resource for the specified window and provides the obtained one or more metrics in response to the request.

Abstract: Techniques to facilitate a hardware based table look of a table maintained in or more types of memories or memory domains include examples of receiving a search request forwarded from a queue management device. Examples also include implementing table lookups to obtain a result and sending the result to an output queue of the queue management device for the queue management device to forward the result to a requestor of the search request.

Abstract: Technologies for providing a multi-tenant local breakout switching and dynamic load balancing include a network device to receive network traffic that includes a packet associated with a tenant. Upon a determination that the packet is encrypted, a secret key associated with the tenant is retrieved. The network device decrypts a payload from the packet using the secret key. The payload is indicative of one or more characteristics associated with network traffic. The network device evaluates the characteristics and determines whether the network traffic is associated with a workload requesting compute from a service hosted by a network platform. If so, the network device forwards the network traffic to the service.

Abstract: Technologies for providing hardware resources as a service with direct resource addressability are disclosed. According to one embodiment of the present disclosure, a device receives a request to access a destination accelerator device in an edge network, the request specifying a destination address assigned to the destination accelerator device. The device determines, as a function of the destination address, a location of the destination accelerator device and sends the request to the destination accelerator device.

Abstract: Various systems and methods for implementing a service-level agreement (SLA) apparatus receive a request from a requester via a network interface of the gateway, the request comprising an inference model identifier that identifies a handler of the request, and a response time indicator. The response time indicator relates to a time within which the request is to be handled indicates an undefined time within which the request is to be handled. The apparatus determines a network location of a handler that is a platform or an inference model to handle the request consistent with the response time indicator, and routes the request to the handler at the network location.

Abstract: Technologies for providing hardware resources as a service with direct resource addressability are disclosed. According to one embodiment of the present disclosure, a device receives a request to access a destination accelerator device in an edge network, the request specifying a destination address assigned to the destination accelerator device. The device determines, as a function of the destination address, a location of the destination accelerator device and sends the request to the destination accelerator device.