Abstract: Systems, computer program products, and methods for automated node failure detection across a multi-node network. The method includes receiving one or more node metrics. Each of the one or more node metrics are associated with a node of a distributed network with a plurality of nodes. The method also includes determining a potential failure node of the plurality of nodes based on at least one of the one or more node metrics. The potential failure node is the node associated with the at least one of the one or more node metrics. The at least one of the one or more node metrics is different than an expected node metric for the node. The method further includes determining one or more replacement nodes for the potential failure node. The one or more replacement nodes are capable of performing one or more operations being performed by the potential failure node. The method still further includes causing the one or more replacement nodes to replace one or more operations of the potential failure node.

Abstract: Systems, computer program products, and methods are described herein for using advanced computation models for data analysis and automated decision-making for root cause analysis within software. The present disclosure is configured to receive a set of file extensions associated with an error generated from an at least one piece of software; analyzing the set of file extensions using an advanced computation model for data analysis and automated decision-making; determining a set of root cause analysis from the analyzed set of file extensions using a root cause database; summarizing the set of root cause analysis using the advanced computation model for data analysis and automated decision making and a predetermined database of identifiers; and transmitting the summarized set of root cause analysis associated with the error generated from the at least one piece of software to an end-point device.

Abstract: Systems, computer program products, and methods are described herein for dynamic allocation of computational resources for optimized performance of ML models. The present disclosure is configured to receive a request to execute a ML model; determine computational requirements associated with the ML model; determine a subset of computational resources from a pool of computational resources to execute the ML model based on the computational requirements associated with the ML model; allocate the subset of computational resources to the ML model; and execute the ML model using the subset of computational resources.

Abstract: Systems, computer program products, and methods are described herein for selective data routing in a distributed network via data throughput analysis in a distributed ledger network. The present invention is configured to receive a request for a data transmission from an endpoint device of a network, determine, via a routing module, a throughput of data associated with the data transmission, determine a classification of the data transmission, and assign the data transmission to an assigned routing channel. The assigned routing channel may include either a first routing channel or a second routing channel based on the classification, wherein the first routing channel is a light load network, and wherein the second routing channel is a heavy load network comprising a first multicore optical fiber.

Abstract: Systems, computer program products, and methods are described herein for validation of electronic data transfers in a distributed network via self-directed image data. The present invention is configured to receive a request for a transfer of electronic data from a first repository associated with a first user to a second repository associated with a second user, transfer the electronic data, generate and apply a first identifier to the electronic data, collect a geolocation of a second endpoint device, transmit the geolocation and a request for a verification of the geolocation to the first endpoint device, receive the verification of the geolocation, generate and apply a second identifier to the electronic data, transmit control signals to the second endpoint device to prompt collection of self-directed image data, and collect, using the second endpoint device, the self-directed image data.

Abstract: Systems, computer program products, and methods are described herein an automated system for restarting large scale cluster supercomputers. The present disclosure is configured to receive a request to reboot a supercomputer cluster, wherein the request comprises a sequence of reboot instructions; determine, using a data integrity engine, whether a current state of the supercomputer cluster meets reboot requirements, wherein the reboot requirements are associated with a core logic of the data integrity engine; and execute the sequence of reboot instructions in an instance where the current state of the supercomputer cluster meets the reboot requirements.

Abstract: Systems, computer program products, and methods are described herein for a machine learning based system for optimized CPU utilization in data transformation. The present disclosure is configured to receive a new data segment; retrieve characteristics of the new data segment; determine, using a trained machine learning model, an encryption algorithm, and a compression algorithm for implementation on the new data segment based on at least the characteristics of the new data segment; determine, using the trained machine learning model, an order of implementation associated with the implementation of the encryption algorithm and the compression algorithm; and implement the encryption algorithm and the compression algorithm on the new data segment in the determined order of implementation.

Abstract: Systems, computer program products, and methods are described herein for a secure tunnel proxy with software-defined perimeter for network data transfer. The present disclosure is configured to receive sensor configuration information associated with an Internet of Things (IoT) sensor; receive information associated with an edge computing device configured to receive sensor data from the IoT sensor; configure a secure tunnel proxy between the IoT sensor and the edge computing device based on at least the sensor configuration information and the information associated with the edge computing device; and deploy the secure tunnel proxy between the IoT sensor and the edge computing device.

Abstract: Systems, computer program products, and methods are described herein for selective data routing in a distributed network via data throughput analysis in a distributed ledger network. The present invention is configured to receive a request for a data transmission from an endpoint device of a network, determine, via a routing module, a throughput of data associated with the data transmission, determine a classification of the data transmission, and assign the data transmission to an assigned routing channel. The assigned routing channel may include either a first routing channel or a second routing channel based on the classification, wherein the first routing channel is a light load network comprising emitter devices operatively coupled to endpoint devices proximate the emitter devices, and wherein the second routing channel is a heavy load network comprising a first multicore optical fiber.