Resource Allocation for Service Composition in Cloud-based Video Surveillance Platform

Hossain, M. Shamim; Hassan, Mohammad Mehedi; Qurishi, Mohammed Al; Alghamdi, Abdullah

doi:10.1109/icmew.2012.77

Cited by 59 publications

(30 citation statements)

References 10 publications

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“…A dynamic resource allocation mechanism for service composition in cloud is proposed in [9]. The authors suggested that for multiple surveillance services, a number of virtual machines need to be optimally utilized.…”

Section: Related Workmentioning

confidence: 99%

“…Such challenges stem from the fact that diverse design decisions need to be made given the abundance of cloud resources and the specific requirements of a surveillance system. Existing work in this direction studies several aspects of cloud-based surveillance system design, for example, dependability characteristics [8], resource allocation [9], video recording [10], cloud storage mechanism [6], and cloud computing suitability for video surveillance [4,11]. However, a holistic approach to develop a multimedia surveillance framework on cloud infrastructure addressing the aforementioned challenges is still missing, which we aim to propose in this paper.…”

Section: Introductionmentioning

confidence: 99%

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Framework for a Cloud-Based Multimedia Surveillance System

Hossain

2014

International Journal of Distributed Sensor Networks

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The new generation of multimedia surveillance systems integrates a large number of heterogeneous sensors to collect, process, and analyze multimedia data for identifying events of potential security threats. Some of the major concerns facing these systems are scalability, ubiquitous access to sensory data, event processing overhead, and massive storage requirements-all of which demand novel scalable approach. Cloud computing can provide a powerful and scalable infrastructure for large-scale storage, processing, and dissemination of sensor data. Furthermore, the integration of sensor technology and cloud computing offers new possibilities for efficient development and deployment of sensor-based systems. This paper proposes a framework for a cloud-based multimedia surveillance system and highlights several research and technical issues. A prototype surveillance system is also designed and analyzed in the context of the proposed surveillance framework. The paper finally reports that cloud-based multimedia surveillance system can effectively support the processing overload, storage requirements, ubiquitous access, security, and privacy in large-scale surveillance settings.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Framework for a Cloud-Based Multimedia Surveillance System

Hossain

2014

International Journal of Distributed Sensor Networks

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“…MapReduce [11][12] is a simplified distributed programming model and efficient a task scheduling model emerging in recent years.MapReduce parallel process mass data through the two steps include the Map and Reduce.The advantage of MapReduce framework mainly include two aspects [13]which one is the distributed framework not only processing mass data but also hiding complicated computing details; the other one is framework having a good scalability, so with 5th International Conference on Computer Sciences and Automation Engineering (ICCSAE 2015) increasing a server every time ,the computing source of the server will be accessing to the cluster.…”

Section: Mapreduce Calculation Modelmentioning

confidence: 99%

Research on Parallel Particle Filtering Target Tracking Algorithm Based on Hadoop

Sun¹,

Song²

2016

Proceedings of the 2015 5th International Conference on Computer Sciences and Automation Engineering

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Abstract. In order to achieve high efficiency and low cost moving target tracking in video sequences, we proposed parallel particle filtering target tracking algorithm based on Hadoop cloud platform, the algorithm using the open source calculation model to realize the parallel calculation of all particles in the particle filter.The experiments show that the parallel particle filter target tracking algorithm based on Hadoop improve the calculation efficiency when compared with the existing algorithms improve the calculation efficiency.

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“…In [13], the Scalable Video Coding proxy based on cloud computing was proposed that can deliver highquality video streams. An integer programming model was formulated in [4] to allocate virtual machines (VMs) provided by a cloud provider for operating a video surveillance platform. The model minimizes the number of VMs, while QoS requirements of the platform can be fulfilled.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, cloud computing has been applied to a video monitoring system for providing value-added services [4]. In a cloud-based video monitoring (CVM) system, value-added services associated with on-demand storage capacity and scalable processing power are available for video distribution, video backup, video database management system, video encoding, and image/video recognition, for example.…”

Section: Introductionmentioning

confidence: 99%

Cloud-Based Video Monitoring Framework: An Approach Based on Software-Defined Networking for Addressing Scalability Problems

Sandar

Chaisiri

Yongchareon

et al. 2015

Lecture Notes in Computer Science

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Abstract. Closed-circuit television (CCTV) and Internet protocol (IP) cameras have been applied to a surveillance or monitoring system, from which users can remotely monitor video streams. The system has been employed for many applications such as home surveillance, traffic monitoring, and crime prevention. Currently, cloud computing has been integrated with the video monitoring system for achieving value-added services such as video adjustment, encoding, image/video recognition, and backup services. One of the challenges in this integration is due to the size and geographical scalability problems when video streams are transferred to and retrieved from the cloud services by numerous cameras and users, respectively. Unreliable network connectivity is a major factor that causes the problems. To deal with the scalability problems, this paper proposes a framework designed for a cloud-based video monitoring (CVM) system. In particular, this framework applies two major approaches, namely stream aggregation (SA) and software-defined networking (SDN). The SA approach can reduce the network latency between cameras and cloud services. The SDN approach can achieve the adaptive routing control which improves the network performance. With the SA and SDN approaches applied by the framework, the total latency for transferring video streams can be minimized and the scalability of the CVM system can be significantly enhanced.

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Resource Allocation for Service Composition in Cloud-based Video Surveillance Platform

Cited by 59 publications

References 10 publications

Framework for a Cloud-Based Multimedia Surveillance System

Framework for a Cloud-Based Multimedia Surveillance System

Research on Parallel Particle Filtering Target Tracking Algorithm Based on Hadoop

Cloud-Based Video Monitoring Framework: An Approach Based on Software-Defined Networking for Addressing Scalability Problems

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