Abstract: Systems and methods for Deep Learning techniques based multi-purpose conversational agents for processing natural language queries. The traditional systems and methods provide for conversational systems for processing natural language queries but do not employ Deep Learning techniques, and thus are unable to process large number of intents. Embodiments of the present disclosure provide for Deep Learning techniques based multi-purpose conversational agents for processing the natural language queries by defining and logically integrating a plurality of components comprising of multi-purpose conversational agents, identifying an appropriate agent to process one or more natural language queries by a High Level Intent Identification technique, predicting a probable user intent, classifying the query, and generate a set of responses by querying or updating one or more knowledge graphs.

Abstract: When a measure of buffer space queued for garbage collection in a network device grows beyond a certain threshold, one or more actions are taken to decreasing an enqueue rate of certain classes of traffic, such as of multicast traffic, whose reception may have caused and/or be likely to exacerbate garbage-collection-related performance issues. When the amount of buffer space queued for garbage collection shrinks to an acceptable level, these one or more actions may be reversed. In an embodiment, to more optimally handle multi-destination traffic, queue admission control logic for high-priority multi-destination data units, such as mirrored traffic, may be performed for each destination of the data units prior to linking the data units to a replication queue. If a high-priority multi-destination data unit is admitted to any queue, the high-priority multi-destination data unit can no longer be dropped, and is linked to a replication queue for replication.

Abstract: To more efficiently utilize buffer resources, schedulers within a traffic manager may generate and queue read instructions for reading buffered portions of data units that are ready to be sent to the egress blocks. The traffic manager may be configured to select a read instruction for a given buffer bank from the read instruction queues based on a scoring mechanism or other selection logic. To avoid sending too much data to an egress block during a given time slot, once a data unit portion has been read from the buffer, it may be temporarily stored in a shallow read data cache. Alternatively, a single, non-bank specific controller may determine all of the read instructions and write operations that should be executed in a given time slot. The read instruction queue architecture may be duplicated for link memories and other memories in addition to the buffer memory.

Abstract: A logical bank may comprise multiple physical banks across which logical blocks, such as buffer entries, are striped. Data structures are stored in the logical blocks, with each logical block storing no more than one data structure. When writing a data structure, if the data structure is less than half the logical block size, one or more duplicate copies of the data structure may be stored in the otherwise unused physical blocks of the logical block. Before executing a first read instruction to read a first data structure from a first logical block, if the first data structure can be read without accessing one or more of the physical banks, a second read instruction for a second data structure may be analyzed to determine if there is a copy of a second data structure within the one or more unneeded physical banks. If so, the two read instructions are consolidated.

Abstract: Certain hash-based operations in network devices and other devices, such as mapping and/or lookup operations, are improved by manipulating a hash key prior to executing a hash function on the hash key and/or by manipulating outputs of a hash function. A device may be configured to manipulate hash keys and/or outputs using manipulation logic based on one or more predefined manipulation values. A similar hash-based operation may be performed by multiple devices within a network of computing devices. Different devices may utilize different predefined manipulation values for their respective implementations of the manipulation logic. For instance, each device may assign itself a random mask value for key transformation logic as part of an initialization process when the device powers up and/or each time the device reboots. In an embodiment, described techniques may increase the entropy of hashing function outputs in certain contexts, thereby increasing the effectiveness of certain hashing functions.

Abstract: This disclosure relates generally to data processing, and more particularly to a system and a method for mapping heterogeneous data sources. For a product being sold globally, there might be one global database listing characteristics of the product, and from various System and method for mapping attributes of entities are disclosed. In an embodiment, the system uses a combination of Supervised Bayesian Model (SBM) and an Unsupervised Textual Similarity (UTS) model for data analysis. A weighted ensemble of the SBM and the UTS is used, wherein the ensemble is weighted based on a confidence measure. The system, by performing data processing, identifies data match between different data sources (a local databases and a corresponding global database) being compared, and based on matching data found, performs mapping between the local databases and the global database.

Abstract: A distributed switch architecture supports very high bandwidth applications. For instance, the distributed switch architecture may be implemented for cloud networks. The architecture scales by organizing traffic management components into tiled structures with distributed buffering. The tile structures are replicated and interconnected to perform transfers from ingress to egress using an interconnect bandwidth scheduling algorithm. Bandwidth scaling may be achieved by adding more tiles to achieve higher bandwidth. The interconnect in the architecture may be swapped out depending on implementation parameters, e.g., physical efficiency.

Abstract: A traffic manager is shared amongst two or more egress blocks of a network device, thereby allowing traffic management resources to be shared between the egress blocks. Among other aspects, this may reduce power demands and allow a larger amount of buffer memory to be available to a given egress block that may be experiencing high traffic loads. Optionally, the shared traffic manager may be leveraged to reduce the resources required to handle data units on ingress. Rather than buffer the entire unit in the ingress buffers, an arbiter may be configured to buffer only the control portion of the data unit. The payload of the data unit, by contrast, is forwarded directly to the shared traffic manager, where it is placed in the egress buffers. Because the payload is not being buffered in the ingress buffers, the ingress buffer memory may be greatly reduced.

Abstract: A network traffic manager receives, from an ingress port, a cell of a packet destined for an egress port. Upon determining that a number of cells of the packet stored in a buffer queue meets a threshold value, the manager checks whether the ingress port has been assigned a token corresponding to the queue. Upon determining that the ingress port has been assigned the token, the manager determines whether other cells of the packet are stored in the buffer, in response to which the manager stores the received cell in the buffer, and stores linking information for the received cell in a receive context for the packet. When all cells of the packet have been received, the manager copies linking information for the packet cells from the receive context to the buffer queue or a copy generator queue, and releases the token from the ingress port.

Abstract: A network device organizes packets into various queues, in which the packets await processing. Queue management logic tracks how long certain packet(s), such as a designated marker packet, remain in a queue. Based thereon, the logic produces a measure of delay for the queue, referred to herein as the “queue delay.” Based on a comparison of the current queue delay to one or more thresholds, various associated delay-based actions may be performed, such as tagging and/or dropping packets departing from the queue, or preventing addition enqueues to the queue. In an embodiment, a queue may be expired based on the queue delay, and all packets dropped. In other embodiments, when a packet is dropped prior to enqueue into an assigned queue, copies of some or all of the packets already within the queue at the time the packet was dropped may be forwarded to a visibility component for analysis.

Abstract: A switch architecture includes an ingress module, ingress fabric interface module, and a switch fabric. The switch fabric communicates with egress fabric interface modules and egress modules. The architecture implements multiple layers of congestion management. The congestion management may include fast acting link level flow control and more slowly acting end-to-end flow control. The switch architecture simultaneously provides high scalability, with low latency and low frame loss.

Abstract: This disclosure relates generally to health monitoring of systems, and more particularly to monitor health of a system for fault signature identification. The system estimates Health Index (HI) of the system as time series data. By analyzing data corresponding to the estimated HI, the system identifies one or more time windows in which majority of the estimated HI values are low as a low HI window, and one or more time windows in which majority of the estimated HI values are high as a high HI window. Upon identifying a low HI window, which indicates an abnormal behavior of the system being monitored, based on a local Bayesian Network generated for the system being monitored, an Explainability Index (EI) for each sensor is generated, wherein the EI quantifies contribution of the sensor to the low HI. Further, associated component(s) is identified as contributing to abnormal/faulty behavior of the system.

Abstract: A network device organizes packets into various queues, in which the packets await processing. Queue management logic tracks how long certain packet(s), such as a designated marker packet, remain in a queue. Based thereon, the logic produces a measure of delay for the queue, referred to herein as the “queue delay.” Based on a comparison of the current queue delay to one or more thresholds, various associated delay-based actions may be performed, such as tagging and/or dropping packets departing from the queue, or preventing addition enqueues to the queue. In an embodiment, a queue may be expired based on the queue delay, and all packets dropped. In other embodiments, when a packet is dropped prior to enqueue into an assigned queue, copies of some or all of the packets already within the queue at the time the packet was dropped may be forwarded to a visibility component for analysis.

Abstract: Users have to assign labels to a ticket to route to right domain expert for resolving issue(s). In practice, labels are large and organized in form of a tree. Lack in clarity in problem description has resulted in inconsistent and incorrect labeling of data, making it hard for one to learn/interpret. Embodiments of the present disclosure provide systems and methods that identify relevant queries to obtain user response, for identification of right category and ticket logging there. This is achieved by implementing attention based sequence to sequence (seq2seq) hierarchical classification model to assign the hierarchical categories to tickets, followed by a slot filling model to enable identifying/deciding right set of queries, if the top-k model predictions are not consistent. Further, training data for slot filling model is automatically generated based on attention weight in the hierarchical classification model.

Abstract: Advanced analytics refers to theories, technologies, tools, and processes that enable an in-depth understanding and discovery of actionable insights in big data, wherein conventional systems and methods may be prone to errors leading to inaccuracies.

Abstract: A method of and system for correlating data from among a disparate group of data sources and providing the correlated data to data consumers via API's and direct transmission of the data are disclosed. Once the validity of the data is verified, the data is translated from a format specific to the data source into a format that is usable by various other data repositories. Thereafter, the data may be provided to data consumers.

Abstract: Certain hash-based operations in network devices and other devices, such as mapping and/or lookup operations, are improved by manipulating a hash key prior to executing a hash function on the hash key and/or by manipulating outputs of a hash function. A device may be configured to manipulate hash keys and/or outputs using manipulation logic based on one or more predefined manipulation values. A similar hash-based operation may be performed by multiple devices within a network of computing devices. Different devices may utilize different predefined manipulation values for their respective implementations of the manipulation logic. For instance, each device may assign itself a random mask value for key transformation logic as part of an initialization process when the device powers up and/or each time the device reboots. In an embodiment, described techniques may increase the entropy of hashing function outputs in certain contexts, thereby increasing the effectiveness of certain hashing functions.

Abstract: Techniques for improved handling of queues of data units are described, such as queues of buffered data units of differing types and/or sources within a switch or other network device. When the size of a queue surpasses the state entry threshold for a certain state, the queue is said to be in the certain state. While in the certain state, data units assigned to the queue may be handled differently in some respect, such as being marked or being dropped without further processing. The queue remains in this certain state until its size falls below the state release threshold for the state. The state release threshold is adjusted over time in, for example, a random or pseudo-random manner. Among other aspects, in some embodiments, this adjustment of the state release threshold addresses fairness issues that may arise with respect to the treatment of different types or sources of data units.

Abstract: The present subject matter discloses system and method for executing prescriptive analytics. Simulation is performed from an input data (xinput) and simulation parameters (?) to generate simulating data (D). Further, forecast data may be predicted by processing the simulating data (D) using predictive model (M). Further, prescriptive value (x?) may be determined based on the forecast data by using optimization model. The prescriptive value (x?) may be determined such that an objective function associated with the optimization model is optimized, whereby the optimization of the objective function indicates business objective being achieved. Further, the steps of simulating, predicting and determining may be iteratively performed until the objective function is not further optimized, satisfying predefined condition.

Abstract: The efficiency of the network device is improved by sharing a packet processor across two or more control paths. Data unit sources send the first portion of each data unit that passes through the sources to the shared packet processor via their respective control paths. The packet processor generates control information for each of the data units, and sends the control information to a merger component that corresponds to the source of the data unit. The merger component merges the control information with a payload portion of the data unit that the data unit source sent along a separate data path. To better facilitate the sharing of the packet processor, the control paths may converge at an adaptive distributor, which uses a policy-based mechanism to select which data unit portion to forward to the packet processor in a given time slot. The policy may change based on various demand measures.