Adaptive Resource Management for Analyzing Video Streams from Globally Distributed Network Cameras

Abstract

There has been tremendous growth in the amount of visual data available on the Internet in recent years. One type of visual data of particular interest is produced by network cameras providing real-time views. Millions of network cameras around the world continuously stream data to viewers connected to the Internet. This data may be used by a wide variety of applications such as enhancing public safety, urban planning, emergency response, and traffic management which are computationally intensive. Analyzing this data requires significant amounts of computational resources. Cloud computing can be a preferred solution for meeting the resource requirements for analyzing these data. There are many options when selecting cloud instances (amounts of memory, number of cores, locations, etc.). Inefficient provisioning of cloud resources may become costly in pay-per-use cloud computing. This paper presents a method to select cloud instances in order to meet the performance requirements for visual data analysis at a lower cost. We measure the frame rates when analyzing the data using different computer vision methods and model the relationships between frame rates and resource utilizations. We formulate the problem of managing cloud resources as a Variable Size Bin Packing Problem and use a heuristic solution. Experiments using Amazon EC2 validate the model and demonstrate that the proposed solution can reduce the cost up to 62% while meeting the performance requirements.

Probability distribution based resource management for multitenant cloud clusters

“…Dynamic adjusting resource allocation can effectively reduce idle resources. Some recent researches 19,20 have proposed dynamic resource management solutions for streaming applications. Xiao et al 21 present a system that uses virtualization technology to allocate data center resources dynamically based on application demands.…”

Cloud Resource Optimization for Processing Multiple Streams of Visual Data

“…A solution to handling cloud resource management, proposed by Mohan et al [6], is Adaptive Resource Management for Video Analysis in the Cloud (ARMVAC) . This method does the following: (1) reads inputs necessary for modeling the problem as a Vector Bin Packing Problem, (2) selects the locations of cloud instances to be considered for the given analysis, (3) determines the types and number of cloud instances needed for the analysis, and (4) employs an adaptive resource management solution to adjust resource requirements during runtime.…”

“…Consider the example in Considering instances' types and locations simultaneously makes cloud resource management a complex optimization problem. Mohan et al [8], [6] propose to first eliminate instance locations that are outside the acceptable RTT range. This method, named ARMVAC, then selects the lowest-cost instances from the remaining pool, and sends as many data streams to this instance while meeting the desired frame rates.…”

See the World Through Network Cameras

“…However, when the geographical distance (hence, network round-trip time) increases, the data refresh rate may decline [14], [15]. The network camera's image quality can suffer.…”