Abstract: Various embodiments of the present disclosure include a scalable distributed computing and network system that is configured to update multitude of passive devices in many isolated networks. The system may include a centralized update service (CUS) computing device that receives a firmware file, generates a firmware profile, identifies passive devices that include outdated firmware, select one of the identified passive devices as a test device, and send the firmware file to the test device through intermediates and proxy agents. The CUS computing device receives feedback from the test device, generates a trust score for the firmware file based on the received feedback, and determine whether to send the firmware file to the other identified passive devices based on the generated trust score.

Abstract: Various embodiments of the present disclosure include a scalable distributed computing and network system that is configured to install, update or revoke certificates in a multitude of passive devices in many isolated networks. Various embodiments may include a processor in a computing device associating a certificate profile with one or more passive devices in a plurality of passive devices in one or more isolated networks, generating a certificate signing request (CSR) message for each of the associated passive devices, sending the generated CSR messages to a certificate authority, receiving digital certificates from the certificate authority, and sending the received digital certificates to their respective associated passive devices.

Abstract: One or more key performance indicators are necessary to properly measure the health of video surveillance applications and the supporting infrastructure. Some of the key performance indicators include: Video Path Uptime (VPU), Video Stream Delivery Index (VSDI), and, Video Retention Compliance (VRC). From these metrics, it is possible to calibrate whether a surveillance infrastructure is operating properly. These metrics can be used to properly alert video network administrators of problems that are actually affecting the video surveillance application. It is also possible to use these metrics to build better analytics to determine root cause of problems as well as build prediction models for potential problems before they occur.

Abstract: A distributed storage of sequential video frames is designed without compromising data reliability. Capitalizing on the loss-tolerant nature of video data, user experience is maintained by leveraging higher capacity drives with lower latency, higher throughput and more efficient utilization of provisioned storage. No RAID, replication or erasure computations are necessary. There are a few optimization to this method which improves reliability, such as reliably storing video data in partitioned drives, making data storage configuration map known to each partition, implementing a strategy for avoiding loss of a common moment, preventing potential loss of reference frames, leveraging different sized drives, avoiding pitfalls associated with data getting stored disproportionately to large drives etc. Though video streaming data has been used as an illustrative example of loss-tolerant data, the methods and systems can be applied to any type of loss-tolerant data.

Abstract: This disclosure articulates methods for calculating a metric based on actual time period of retention (??) in a given video recording system. The measurement is performed on a per-camera-stream basis for each camera stream being recorded on a system. The metric is related to Video Retention Compliance (VRC), which is another key performance indicators of video surveillance applications, as it is tied to a business and, sometimes, regulatory requirement of an organization. This disclosure describes methods of calculating retention for each camera stream recording on a server, and doing so in an efficient enough way so as to be practical for ongoing operations.