Quality of Service Evaluation in On-Demand Cloud-Based Video Surveillance

Video surveillance has demonstrated its value and benefits countless times by providing real-time monitoring of a facility's environment, people, and assets, recording events for subsequent investigation, proof of compliance and audit purposes. This paper evaluates Quality of Service (QoS) in effective on-demand video surveillance system activated from a remote location with cloud-based storage, and provision for predictive analytics, which relies on Tier-4 network integration. The discrete event tool of Riverbed modelling software version 1.75 has been used to evaluate the on-demand real-time video surveillance system based on on-demand latency, throughput and resource utilization using both primary and secondary backups at the storage end. It was observed that the primary backup took about 0.002secs while the secondary backup took about 0.01secs delay, and a throughput of 100packets/sec obtained without virtualization which significantly increased to 900packets/sec (primary) and 1100packets/sec (secondary) over time with virtualization. These values characterize a state-of-the-art high speed network.