Efficient Resource Management for Cloud-enabled Video Surveillance over Next Generation Network

Hossain, M. Shamim; Song, Biao

doi:10.1007/s11036-016-0699-3

Cited by 11 publications

(3 citation statements)

References 37 publications

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“…Hossain et al [16] propose a heuristic resource management for cloud based video surveillance systems. Song et al [30] propose a queuing based resource management for multimedia applications.…”

Section: Dynamic Resource Provisioning Methodsmentioning

confidence: 99%

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

Mohan

Kaseb

et al. 2021

IEEE Trans. Cloud Comput.

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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.

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Section: Dynamic Resource Provisioning Methodsmentioning

confidence: 99%

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

Mohan

Kaseb

et al. 2021

IEEE Trans. Cloud Comput.

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“…Complete solutions need to provide for integration of network infrastructure design and installation to keep up with the demands of multimedia data produced by surveillance peripheral. Some of advanced system designs are based on several areas such as wireless solution [210]- [216] cloud infrastructure solutionn [183], [186], [187], [217]- [220], sensor improvement [4], [108], [124], [212], protocol enhancement [111], [159], [181], [188], [221], [222]. Figure 5 shows numbers of research focus on network infrastructure improvement.…”

Section: G the Most Influential Network Rchitecturementioning

confidence: 99%

A Systematic Review of Intelligence Video Surveillance: Trends, Techniques, Frameworks, and Datasets

Shidik¹,

Noersasongko²,

Nugraha³

et al. 2019

IEEE Access

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Video surveillance systems obtain a great interest as application-oriented studies that have been growing rapidly in the past decade. The most recent studies attempt to integrate computer vision, image processing, and artificial intelligence capabilities into video surveillance applications. Although there are so many achievements in the acquisition of datasets, methods, and frameworks published, there are not many papers that can provide a comprehensive picture of the current state of video surveillance system research. This paper provides a comprehensive and systematic review on the literature from various video surveillance system studies published from 2010 through 2019. Within a selected study extraction process, 220 journal-based publications were identified and analyzed to illustrate the research trends, datasets, methods, and frameworks used in the field of video surveillance, to provide an in-depth explanation about research trends that many topics raised by researchers as a focus in their researches, to provide references on public datasets that are often used by researchers as a comparison and a means of developing a test method, and to give accounts on the improvement and integration of network infrastructure design to meet the demand for multimedia data. In the end of this paper, several opportunities and challenges related to researches in the video surveillance system are mentioned.INDEX TERMS Artificial intelligence, cloud video surveillance, intelligent video surveillance, video surveillance framework.This study is conducted as follows: the methodology of the study is presented in Section 2. The outcomes and answers to the research questions are then discussed in Section 3. Finally, the study is summarized in the last section.

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“…It is of great theoretical and practical significance to study how to realize the storage and retrieval of video data effectively. Storage technology [2][3][4] is a vital issue in video surveillance systems. The cloud storage of video surveillance is a research hotspot.…”

Section: Introductionmentioning

confidence: 99%

Replicas Strategy and Cache Optimization of Video Surveillance Systems Based on Cloud Storage

Zhang

Shen

2018

Future Internet

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With the rapid development of video surveillance technology, especially the popularity of cloud-based video surveillance applications, video data begins to grow explosively. However, in the cloud-based video surveillance system, replicas occupy an amount of storage space. Also, the slow response to video playback constrains the performance of the system. In this paper, considering the characteristics of video data comprehensively, we propose a dynamic redundant replicas mechanism based on security levels that can dynamically adjust the number of replicas. Based on the location correlation between cameras, this paper also proposes a data cache strategy to improve the response speed of data reading. Experiments illustrate that: (1) our dynamic redundant replicas mechanism can save storage space while ensuring data security; (2) the cache mechanism can predict the playback behaviors of the users in advance and improve the response speed of data reading according to the location and time correlation of the front-end cameras; and (3) in terms of cloud-based video surveillance, our proposed approaches significantly outperform existing methods.

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Efficient Resource Management for Cloud-enabled Video Surveillance over Next Generation Network

Cited by 11 publications

References 37 publications

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

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

A Systematic Review of Intelligence Video Surveillance: Trends, Techniques, Frameworks, and Datasets

Replicas Strategy and Cache Optimization of Video Surveillance Systems Based on Cloud Storage

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