Abstract
The visual data generated by network cameras can be valuable for a wide range of scientific studies such as weather, wildlife, and traffic. The resource demands for analysis of the data may fluctuate significantly for some of these studies (for example, seasonal or during only rush hours). Cloud computing's pay-per-use can be a preferred solution for analysing large amounts of data from these network cameras. There are few studies focusing on how to allocate cloud resources to analyse many video streams from network cameras. Existing autoscaling and load balancing methods are inapplicable due to the nature of the workloads. This paper presents a solution to manage cloud resources analysing multiple video streams. When an analysis program is launched, the resource manager adjusts the resource allocation by one of two methods: gradually scaling up the number of instances or predicting the needed number of instances. Then, the resource manager monitors the utilization and scales the resources in response to the analysis program's loads. The proposed resource manager has been implemented using Microsoft Azure and demonstrated that the system can adaptively adjust resource to cater to the workload. We explore the trade-offs inherent to both methods. Gradually scaling up can reach more efficient resource allocation, but may take multiple trials. The prediction method is faster in making allocation decisions, but may need additional adjustment. We also investigate the impact of the types of virtual machines and find that smaller sizes are usually better in terms of performance-cost ratios.