Abstract: In various examples, systems and methods are disclosed relating to data center scale prediction-based power reservation steering. One or more circuits can receive, from a plurality of components of a data center, power consumption data for a first time period. The one or more circuits can generate, using at least one prediction model and based at least on the power consumption data, a predicted power consumption of the plurality of components for a second time period following the first time period. The one or more circuits can determine a power policy for the plurality of components based at least on the predicted power consumption and a state of the data center and cause the plurality of components to limit power consumption for the second time period according to the power policy.

Abstract: In one embodiment, a method includes receiving data of a set of configurations of preprocessor engines, receiving measurements of performance of a device executing benchmark applications while changing a configuration of preprocessor engines selected from the set of configurations of preprocessor engines, defining an order of at least some of the configurations based on the measurements, and providing a pruned set of configurations based on the defined order of the at least some configurations.

Abstract: Network devices, systems, and methods are provided. In one example, a network device includes one or more packet classification circuits and one or more hash selection circuits that cooperate with the one or more packet classification circuits to provide the one or more packet classification circuits with a hash table selection order. The hash table selection order may be determined for a packet to be classified with the support of machine learning by the one or more packet classification circuits based on information contained in the packet.

Abstract: A method and system for evaluating cyber risk of an entity comprising a risk evaluation module configured to collect risk data on risks of cyber-attacks connected to SaaS, infrastructure, and legal regulations classified by geolocation, industry type, and size of the victim organization, an entity evaluation module for collecting vulnerability data on assets of the entity classified by industry type, geolocation, size and cyber threat vector vulnerabilities and a monetization engine configured to make an assessment of expected financial loss from a specified cyber-attack to an entity classified by geolocation, industry type, and size, based on the risk data.

Abstract: A network device, system-on-a-chip, and method of performing packet handling are described. A packet is received, and a lookup based on one or more packet header fields of the packet is performed using a lookup generator action handler. An artificial intelligence engine processes packet header data associated with the packet, and a smart indication is generated. A forwarding decision is made for the packet based on the lookup performed by the lookup generator action handler as well as based on the smart indication generated with the artificial intelligence engine.

Abstract: In one embodiment, a system includes prefetcher engines to predict next memory access addresses of a memory from which to load data to a cache during execution of a software application, and load the data from the predicted next memory access addresses to the cache during execution of the software application, and a processor to control the prefetcher engines according to configurations of the prefetcher engines selected by a machine learning agent in exploration phases and in exploitation phases during execution of the software application, and execute the machine learning agent to select from a pruned set of configurations to control the prefetcher engines in the exploration phases, perform measurements on the system during execution of the machine learning agent, and execute the machine learning agent to select from the configurations to maximize potential rewards from controlling the prefetcher engines in the exploitation phases based on the performed measurements.

Abstract: A network device includes a memory and a memory management circuit. The memory is to store a shared buffer. The memory management circuit is to estimate respective bandwidth measures for one or more queues used in processing packets in the network device, and to allocate and deallocate segments of the shared buffer to at least one of the queues based on the bandwidth measures.

Abstract: In one embodiment, a system includes a processor to reduce a number of cache replacement precedence value policies available for selection by a machine learning agent, each cache replacement precedence value policy including an array of predefined cache replacement precedence values for corresponding different combinations of (a) prefetcher engines that loaded cache lines, and (b) event-types of events that have been performed on the cache lines, and a memory to store data used by the processor.

Abstract: In one embodiment, a system includes prefetcher engines to predict next memory access addresses of a memory from which to load data to a cache during execution of a software application, and load the data from the predicted next memory access addresses to the cache during execution of the software application, and a processor to assign cache replacement precedence values to cache lines based on the prefetcher engines that loaded the cache lines, and evict the cache lines from the cache based on the cache replacement precedence values of the cache lines.

Abstract: In one embodiment, a system includes a processor to reduce a number of cache replacement precedence value policies available for selection by a machine learning agent, each cache replacement precedence value policy including an array of predefined cache replacement precedence values for corresponding different combinations of (a) prefetcher engines that loaded cache lines, and (b) event-types of events that have been performed on the cache lines, and a memory to store data used by the processor.

Abstract: In one embodiment, a system includes prefetcher engines to predict next memory access addresses of a memory from which to load data to a cache during execution of a software application, and load the data from the predicted next memory access addresses to the cache during execution of the software application, and a processor to assign cache replacement precedence values to cache lines based on the prefetcher engines that loaded the cache lines, and evict the cache lines from the cache based on the cache replacement precedence values of the cache lines.

Abstract: A networking device and system are described, among other things. An illustrative system is disclosed to include a shared buffer and at least a flow controller. In some embodiments, the system and/or flow controller may be configured to measure a packet flow's bandwidth consumption of the shared buffer, assign a flow-type attribute to the packet flow based on the packet flow's bandwidth consumption of the shared buffer, select a shared buffer schema for the packet flow based on the flow-type attribute assigned to the packet flow, and apply the selected shared buffer schema to the packet flow. For example, the flow-type attribute assigned to the packet flow may comprise a mice flow state or an elephant flow state, and a reserve attribute may be assigned to the flow based on the packet flow being assigned the mice flow state or the elephant flow state.

Abstract: A method and system for mapping vulnerabilities to a private network identifier are provided. The method includes generating a request addressed to a public network identifier of a server, wherein the request includes a first beacon; transmitting the generated request to the server, wherein upon receiving the request by the server, the request causes a registry of a log file of the server, wherein the log includes at least the first beacon; in response to a query, receiving a response from the server, wherein the response includes a second beacon, and a private network identifier of the server; associating the public network identifier with the private network identifier, when the first beacon matches the second beacon; and mapping vulnerabilities identified with the public network identifier to associate the identified vulnerabilities with the associated public network identifier and the private network identifier.

Abstract: A network device includes multiple ports, a Shared Buffer (SB) and a SB controller. The ports to connect to a communication network. The SB to temporarily store packets received from the communication network via the ports, the packets belonging to multiple flows. The SB controller to allocate one or more flow-specific storage regions in the SB, a given flow-specific storage region being allocated to store the packets that (i) belong to respective one or more of the flows and (ii) are to be transmitted via a respective egress queue. In response to detecting that an occupancy level in the given flow-specific storage region exceeds a specified occupancy threshold, the SB controller to report the flows in the given flow-specific storage region as congested.

Abstract: Apparatus for global policing of a bandwidth of a flow, the apparatus including a network device including a local policer configured to perform bandwidth policing on the flow within the network device, and a communications module configured to: send local policer state information from the local policer to a remote global policer, and receive policer state information from the remote global policer and update the local policer state information based on the remote global policer state information. Related apparatus and methods are also provided.

Abstract: In one embodiment, a method includes receiving data of a set of configurations of preprocessor engines, receiving measurements of performance of a device executing benchmark applications while changing a configuration of preprocessor engines selected from the set of configurations of preprocessor engines, defining an order of at least some of the configurations based on the measurements, and providing a pruned set of configurations based on the defined order of the at least some configurations.

Abstract: In one embodiment, a system includes a processor to control a resource according to policies selected by a multi-armed bandit machine learning agent in exploration phases and in exploitation phases, and execute the multi-armed bandit machine learning agent to select from the policies to control the resource in the exploration phases according to probabilities to explore corresponding one of the policies, wherein the probabilities include different probabilities, perform measurements on the system during execution of the multi-armed bandit machine learning agent, and execute the multi-armed bandit machine learning agent to select from the policies to maximize potential rewards from controlling the resource in exploitation phases based on the performed measurements, and a memory to store data used by the processor.

Abstract: In one embodiment, a method includes finding an impact on performance of a device from changing settings of preprocessor engines applied to benchmark applications being executed by the device, defining groups of the preprocessor engines responsively to the impact on the performance of the device from changing the settings of the preprocessor engines, and providing different preprocessor engine configurations based on the settings to be applied to the preprocessor engines such that for each one of the defined groups a respective setting is to be applied equally to the preprocessor engines of the one group, thereby reducing a number of the preprocessor engine configurations available for selection by a machine learning agent.

Abstract: In one embodiment, a system includes a processor to receive machine learning training data including label scores based on measurements of device performance during execution of benchmark applications for different prefetcher engine configurations, and corresponding device hardware states, and train configuration specific machine learning regression models based on the received machine learning training data to provide corresponding configuration specific device performance predictions based on given device hardware states, and a memory to store data used by the processor.

Abstract: In one embodiment, a system includes prefetcher engines to predict next memory access addresses of a memory from which to load data to a cache during execution of a software application, and load the data from the predicted next memory access addresses to the cache during execution of the software application, and a processor to control the prefetcher engines according to configurations of the prefetcher engines selected by a machine learning agent in exploration phases and in exploitation phases during execution of the software application, and execute the machine learning agent to select from a pruned set of configurations to control the prefetcher engines in the exploration phases, perform measurements on the system during execution of the machine learning agent, and execute the machine learning agent to select from the configurations to maximize potential rewards from controlling the prefetcher engines in the exploitation phases based on the performed measurements.