The present disclosure discloses a distributed system and a message processing method. The distributed system includes a client and a plurality of nodes. The client includes processing circuitry that is configured to send a message including a digital signature of the client. The distributed system is in a first consensus mode for reaching a consensus on the message. The processing circuitry obtains results from a subset of the nodes that receive the message. The results have respective digital signatures of the subset of the nodes. After verifying the digital signatures of the subset of the nodes, the processing circuitry of the client determines, based on the results, whether one or more of the nodes in the distributed system is malfunctioned.

Certain embodiments disclosed herein reduce or eliminate a communication bottleneck at the storage manager by reducing communication with the storage manager while maintaining functionality of an information management system. In some implementations, a client obtains information for enabling a secondary storage job (e.g., a backup or restore) from a storage manager and stores the information (which may be referred to as job metadata) in a local cache. The client may then reuse the job metadata for multiple storage jobs reducing the frequency of communication with the storage manager. When a configuration of the information management system changes, or the availability of resources changes, the storage manager can push updates to the job metadata to the clients. Further, a client can periodically request updated job metadata from the storage manager ensuring that the client does not rely on out-of-date job metadata.

An information management system is provided herein that uses machine learning (ML) to predict what data to store in a secondary storage device and/or when to perform the storage. For example, a client computing device can be initially configured to store data in a secondary storage device according to one or more storage policies. A media agent in the information management system can monitor data usage on the client computing device, using the data usage data to train a data storage ML model. The data storage ML model may be trained such that the model predicts what data to store in a secondary storage device and/or when to perform the storage. The client computing device can then be configured to use the trained data storage ML model in place of the storage polic(ies) to determine which data to store in a secondary storage device and/or when to perform the storage.

Systems and methods provide techniques for more effective and efficient predictive monitoring of a software application framework. In response, embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to enable effective and efficient predictive monitoring of a software application framework using incident signatures for the software application that are generated by using a natural language processing machine learning framework, a structured data processing machine learning model, a feature combination machine learning model, and a clustering machine learning model.

The techniques disclosed herein enable predictive maintenance features in Hybrid Cloud systems. The system can analyze system data defining one or more operating conditions of an on-prem server and determine if one or more predetermined conditions are met. If a predetermined condition is met, the on-prem server can generate and transmit log data to a primary server. The primary server can generate one or more monitor rules having one or more updated predetermined conditions for detecting an anomaly at the on-prem server. Using the monitor rules, the on-prem server can detect and proactively resolve potential issues. The on-prem server can also transmit diagnostic data to the primary server for generating an updated monitor rule that is further tailored to the conditions of the on-prem server.

An example method includes receiving, at a server, a request for a list of a first group of index records that correspond to stored data, creating a data streaming session with a client, reading an index file and obtaining a list of the first group of index records from the index file, populating a content cache with a signature that corresponds to the first group of index records, creating a sliding window and populating it with the signature and a pointer to a next group of index records, populating an attribute cache with data streaming session information and the sliding window, creating a continuation token which, when received by the server from the client, indicates to the server that the list of the first group of index records has been received by the client.

According to a computer-implemented method, an update package that includes update operational files is received at a computing device. At least one update operational file is to replace a corresponding original operational file for the computing device. It is determined which of the original operational files are to be replaced with corresponding update operational files. A delta file is stored at the computing device, which delta file indicates the original operational files that are replaced with corresponding update operational files and the update package is installed at the computing device.

A processing system encompasses several processing devices and a comparison device. A method for controlling the processing system encompasses: processing of identical information items by the processing devices using associated processing processes; furnishing a characteristic value of each processing process, respectively as a function of the processing that has occurred; and comparing the characteristic values by way of the comparison device and determining a defectively operating processing process on the basis of the comparison. The defectively operating processing process is replaced by a processing process restarted on the same processing device.

An information management system is provided herein that uses machine learning (ML) to predict what data to store in a secondary storage device and/or when to perform the storage. For example, a client computing device can be initially configured to store data in a secondary storage device according to one or more storage policies. A media agent in the information management system can monitor data usage on the client computing device, using the data usage data to train a data storage ML model. The data storage ML model may be trained such that the model predicts what data to store in a secondary storage device and/or when to perform the storage. The client computing device can then be configured to use the trained data storage ML model in place of the storage polic(ies) to determine which data to store in a secondary storage device and/or when to perform the storage.

Files can be managed to mitigate undesirable reading of files from secondary storage component (SSC) associated with a storage system comprising primary storage component (PSC). File management component (FMC) can determine file identifiers for files stored in SSC and store them in reference files associated with such files. FMC can determine file identifiers for files stored in PSC and store them in a file entry data store. In response to a client request, FMC can determine whether a local file stored in PSC is a copy of an archived file stored in SSC based on whether the respective file identifiers of the archived file and local file or snapshot of the local file match. If there is a suitable match, FMC can read the snapshot of the local file and provide it to client device; if not, FMC can read the archived file and provide it to client device.