Abstract: In some examples, a data management and storage (DMS) system comprises peer DMS nodes in a node cluster, a distributed data store comprising local and cloud storage, and an IO request scheduler comprising at least one processor configured to perform operations in a method of scheduling IO requests. Example operations comprise implementing a kernel scheduler to schedule a flow of IO requests in the DMS system, and providing an adjustment layer to adjust the kernel scheduler based on an IO request prioritization. A flow of IO requests is identified and some examples implement an IO request prioritization based on the adjustments made by the adjustment layer.

Abstract: A computer-implemented method at a data management system comprises: generating, with one or more processors, a containerized runtime in a memory in communication with the one or more processors; instantiating, with the one or more processors, an app in the runtime; receiving, with the one or more processors, a request from the app for data; retrieving, with the one or more processors, a copy of the requested data from a data source; and transmitting, with the one or more processors, the data to the containerized runtime for the app to operate on.

Abstract: A computer-implemented method at a data management system comprises: receiving, at a storage appliance from a server hosting a virtual machine, a write made to the virtual machine; computing, at the storage appliance, a fingerprint of the transmitted write; comparing, at the storage appliance, the computed fingerprint to malware fingerprints in a malware catalog; repeating the computing and comparing; and disabling the virtual machine if a number of matches from the comparing breaches a predetermined threshold over a predetermined amount of time.

Abstract: A computer-implemented method at a data management system comprises: generating, with one or more processors, a containerized runtime in a memory in communication with the one or more processors; instantiating, with the one or more processors, an app in the runtime; receiving, with the one or more processors, a request from the app for data; retrieving, with the one or more processors, a copy of the requested data from a data source; and transmitting, with the one or more processors, the data to the containerized runtime for the app to operate on.

Abstract: A computer-implemented method at a data management system comprises receiving, at the system, a write made to a virtual machine from a virtual machine host; computing, at the system, a fingerprint of the transmitted write; comparing, at the system, the computed fingerprint to malware fingerprints in a malware catalog; repeating the computing and comparing; and disabling the virtual machine if a number of matches from the comparing breaches a predetermined threshold over a predetermined amount of time.

Abstract: A computer-implemented method at a data management system comprises: receiving, at a storage appliance from a server hosting a virtual machine, a write made to the virtual machine; computing, at the storage appliance, a fingerprint of the transmitted write; comparing, at the storage appliance, the computed fingerprint to malware fingerprints in a malware catalog; repeating the computing and comparing; and disabling the virtual machine if a number of matches from the comparing breaches a predetermined threshold over a predetermined amount of time.

Abstract: Systems, methods, and machine-storage medium for classifying snapshot image processing are described. The system receives read requests to read snapshot information. Each read request includes an offset identifying a storage location and a length. The snapshot information includes snapshots including a full snapshot and at least one incremental snapshot. The read requests include a first read request to read data from the snapshot information. The system generates a first plurality of read events including a second plurality of read events that are generated by processing the first read request. The second plurality of read events includes first and a second read events. The system identifies whether utilizing a cache optimizes the job based on the first plurality of read events.

Abstract: Systems, methods, and machine-storage medium for classifying snapshot image processing are described. The system receives read requests to read snapshot information. Each read request includes an offset identifying a storage location and a length. The snapshot information includes snapshots including a full snapshot and at least one incremental snapshot. The read requests include a first read request to read data from the snapshot information. The system generates a first plurality of read events including a second plurality of read events that are generated by processing the first read request. The second plurality of read events includes first and a second read events. The system identifies whether utilizing a cache optimizes the job based on the first plurality of read events.

Abstract: Systems, methods, and machine-storage medium for classifying snapshot image processing are described. The system receives read requests to read snapshot information. Each read request includes an offset identifying a storage location and a length. The snapshot information includes snapshots including a full snapshot and at least one incremental snapshot. The read requests include a first read request to read data from the snapshot information. The system generates a first plurality of read events including a second plurality of read events that are generated by processing the first read request. The second plurality of read events includes first and a second read events. The system identifies whether utilizing a cache optimizes the job based on the first plurality of read events.

Abstract: In some examples, a data management system processes snapshots of a distributed file system, the distributed file system having files, each file comprising multiple data chunks. The data management system performs operations including storing file-to-chunk mapping in file system metadata; creating, for each chunk, a chunk generation ID by associating each chunk with a file system generation ID; in a next-generation snapshot of the distributed file system, incrementing, for all chunks in the next-generation snapshot, the respective chunk generation IDs; and taking a snapshot of the file system metadata and storing an updated file-to-chunk mapping associated with the next-generation snapshot.

Abstract: A computer-implemented method at a data management system comprises: generating, with one or more processors, a containerized runtime in a memory in communication with the one or more processors; instantiating, with the one or more processors, an app in the runtime; receiving, with the one or more processors, a request from the app for data; retrieving, with the one or more processors, a copy of the requested data from a data source; and transmitting, with the one or more processors, the data to the containerized runtime for the app to operate on.

Abstract: A computer-implemented method at a data management system comprises: generating, with one or more processors, a containerized runtime in a memory in communication with the one or more processors; instantiating, with the one or more processors, an app in the runtime; receiving, with the one or more processors, a request from the app for data; retrieving, with the one or more processors, a copy of the requested data from a data source; and transmitting, with the one or more processors, the data to the containerized runtime for the app to operate on.

Abstract: A computer-implemented method at a data management system comprises: generating, with one or more processors, a containerized runtime in a memory in communication with the one or more processors; instantiating, with the one or more processors, an app in the runtime; receiving, with the one or more processors, a request from the app for data; retrieving, with the one or more processors, a copy of the requested data from a data source; and transmitting, with the one or more processors, the data to the containerized runtime for the app to operate on.

Abstract: In some examples, a data management and storage (DMS) system comprises peer DMS nodes in a node cluster, a distributed data store comprising local and cloud storage, and an IO request scheduler comprising at least one processor configured to perform operations in a method of scheduling IO requests. Example operations comprise implementing a kernel scheduler to schedule a flow of IO requests in the DMS system, and providing an adjustment layer to adjust the kernel scheduler based on an IO request prioritization. A flow of IO requests is identified and some examples implement an IO request prioritization based on the adjustments made by the adjustment layer.

Abstract: A computer-implemented method at a data management system comprises receiving, at the system, a write made to a virtual machine from a virtual machine host; computing, at the system, a fingerprint of the transmitted write; comparing, at the system, the computed fingerprint to malware fingerprints in a malware catalog; repeating the computing and comparing; and disabling the virtual machine if a number of matches from the comparing breaches a predetermined threshold over a predetermined amount of time.

Abstract: A computer-implemented method at a data management system comprises: receiving, at a storage appliance from a server hosting a virtual machine, a write made to the virtual machine; computing, at the storage appliance, a fingerprint of the transmitted write; comparing, at the storage appliance, the computed fingerprint to malware fingerprints in a malware catalog; repeating the computing and comparing; and disabling the virtual machine if a number of matches from the comparing breaches a predetermined threshold over a predetermined amount of time.

Abstract: A computer-implemented method at a data management system comprises receiving a write made to a virtual machine; computing, outside of the virtual machine, a fingerprint of the write; comparing, outside of the virtual machine, the computed fingerprint to malware fingerprints in a malware catalog; repeating the computing and comparing; and disabling the virtual machine if malware is detected based on a number of matches from the comparing breaching a predetermined threshold over a predetermined amount of time.

Abstract: Systems, methods, and machine-storage medium for classifying snapshot image processing are described. The system receives read requests to read snapshot information. Each read request includes an offset identifying a storage location and a length. The snapshot information includes snapshots including a full snapshot and at least one incremental snapshot. The read requests include a first read request to read data from the snapshot information. The system generates a first plurality of read events including a second plurality of read events that are generated by processing the first read request. The second plurality of read events includes first and a second read events. The system identifies whether utilizing a cache optimizes the job based on the first plurality of read events.