An intelligent surveillance video analysis service in cloud environment

The global optimal Web service selection based on quality of service (QoS) in cloud environment has become a research focus when there are lots of the same or similar services. In this environment, it is possible that many service users request the same or similar services at the same time, which will result in users' unsatisfied requirement and services' load imbalance. The existing service selection approaches usually suppose that service's load capacity is infinite and user always select the service with the best expected QoS despite the amount of user requests. Therefore, it is very important problem how to get a tradeoff between the users' satisfied requirement and service load balance. To solve this problem, this paper presents a global optimal service selection approach based on QoS and load-aware in cloud environment. In this approach, we first build a user QoS utility model which describes the relationship between user's request and services' QoS, and design a service's load capacity model to achieve the load capacity of a service. Then, we use 0-1 integer programming to build a global optimal model based on QoS utility of users and services' load capacity, and provide the optimal service selection algorithm for users. Finally, by conducting large-scale experiments based on a Web service dataset, we show that our approach can effectively help users to select high qualified services while keeping load balance of services in cloud environment.

With the vigorous development of Internet technology, applications are increasingly migrating to the cloud. Cloud, a distributed network environment, has been widely extended to many fields such as digital finance, supply chain management, and biomedicine. In order to meet the needs of the rapid development of the modern biomedical industry, the biological cloud platform is an inevitable choice for the integration and analysis of medical information. It improves the work efficiency of the biological information system and also realizes reliable and credible intelligent processing of biological resources. Cloud services in bioinformatics are mainly for the processing of biological data, such as the analysis and processing of genes, the testing and detection of human tissues and organs, and the storage and transportation of vaccines. Biomedical companies form a data chain on the cloud, and they provide services and transfer data to each other to create composite services. Therefore, our motivation is to improve process efficiency of biological cloud services. Users’ business requirements have become complicated and diversified, which puts forward higher requirements for service scheduling strategies in cloud computing platforms. In addition, deep reinforcement learning shows strong perception and continuous decision-making capabilities in automatic control problems, which provides a new idea and method for solving the service scheduling and resource allocation problems in the cloud computing field. Therefore, this paper designs a composite service scheduling model under the containers instance mode which hybrids reservation and on-demand. The containers in the cluster are divided into two instance modes: reservation and on-demand. A composite service is described as a three-level structure: a composite service consists of multiple services, and a service consists of multiple service instances, where the service instance is the minimum scheduling unit. In addition, an improved Deep Q-Network (DQN) algorithm is proposed and applied to the scheduling algorithm of composite services. The experimental results show that applying our improved DQN algorithm to the composite services scheduling problem in the container cloud environment can effectively reduce the completion time of the composite services. Meanwhile, the method improves Quality of Service (QoS) and resource utilization in the container cloud environment.

Trust management is one of the most challenging issues in the emerging cloud computing area. Over the past few years, many studies have proposed different techniques to address trust management issues. However, despite these past efforts, several trust management issues such as identification, privacy, personalization, integration, security, and scalability have been mostly neglected and need to be addressed before cloud computing can be fully embraced. In this article, we present an overview of the cloud service models and we survey the main techniques and research prototypes that efficiently support trust management of services in cloud environments. We present a generic analytical framework that assesses existing trust management research prototypes in cloud computing and relevant areas using a set of assessment criteria. Open research issues for trust management in cloud environments are also discussed.

With the continuous development of video detection technology, the video analysis technology based on campus security has become an important part of the construction of safe campus. As the college students still are a group that has poor ability of security protection, campus security issue is closely related to the stability of society and family happiness, and has become a topic of concern to the whole society. The intelligent vision-based campus public safety monitoring system is an important means to achieve security monitoring, it can automatically analyze the video image sequence, and detect, track and identify objects in the monitoring scene without human intervention, and make high-level understanding and analysis of behaviors on this basis. Most of the existing visual monitoring systems can collect and store video data, and the real-time event detection task can automatically be generated through background analysis. Intelligent visual monitoring system should not only be used for accident investigation, but also be used to prevent potential disasters and accidents. The system is consisted of system management platform, event mining and analysis, monitoring and extraction of moving targets, forecasting and tracking targets. The paper makes an in-depth study on the application of intelligent visual detection technology on campus. Based on the intelligent visual video analysis, hidden Markov model is adopted in the paper for video event detection and analysis, motion features and shape features are taken as the observation data, and segmentation method is adopted to analyze the influence of video viewing height and angle on the detection result.

In this paper, we have developed a novel framework to enable more effective investigation of large-scale news video database via knowledge visualization. To relieve users from the burdensome exploration of well-known and uninteresting knowledge of news reports, a novel interestingness measurement for video news reports is presented to enable users to find news stories of interest at first glance and capture the relevant knowledge in large-scale video news databases efficiently. Our framework takes advantage of both automatic semantic video analysis and human intelligence by integrating with visualization techniques on semantic video retrieval systems. Our techniques on intelligent news video analysis and knowledge discovery have the capacity to enable more effective visualization and exploration of large-scale news video collections. In addition, news video visualization and exploration can provide valuable feedback to improve our techniques for intelligent news video analysis and knowledge discovery.

Purpose Data service (DS) is a special software service that enables data access in cloud environment and provides a unified data model for cross-origination data integration and data sharing. The purpose of the work is to automatically compose DSs and quickly generate data view to satisfy users' various data requirements (DRs).Design/methodology/approach The paper proposes an automatic DS composition and view generation approach. DSs are organized into DS dependence graph (DSDG) based on their inherent dependences, and DSs can be automatically composed using the DSDG according to user's DRs. Then, data view will be generated by interpreting the composed DS.Findings Experimental results with real cross-origination data sets show the proposed approaches have high efficiency and good quality for DS composition and view generation.Originality/value The authors propose a DS composition algorithm and a data view generation algorithm according to users' DRs.

In the field of video surveillance, the demands of intelligent video analysis services based on Deep Neural Networks (DNNs) have grown rapidly. Although most existing studies focus on the performance of DNNs pre-deployed at remote clouds, the network delay caused by computation offloading from network cameras to remote clouds is usually long and sometimes unbearable. Edge computing can enable rich services and applications in close proximity to the network cameras. However, owing to the limited computing resources of distributed edge clouds, it is challenging to satisfy low latency and high accuracy requirements for all users, especially when the number of users surges. To address this challenge, we first formulate the intelligent video surveillance task scheduling problem that minimizes the average response time while meeting the performance requirements of tasks and prove that it is NP-hard. Second, we present an adaptive DNN model selection method to identify the most effective DNN model for each task by comparing the feature similarity between the input video segment and pre-stored training videos. Third, we propose a two-stage delay-aware graph searching approach that presents a beneficial trade-off between network delay and computing delay. Experimental results demonstrate the efficiency of our approach.

With the trend of cloud computing, more and more web applications move their content to external storage services to reduce the cost of hardware and maintenance. In this paper, a novel architecture called CloudEdge for Content Delivery Network (CDN) with the storage service in cloud computing is introduced. This architecture could not only keep the loosely coupled integration between storage service and web applications but also provide better content manipulation features such as edge network content delivery, caching, secured access control, the variations of content objects generated on demand, and post-process on content objects.

With the increasing popularity of Software as a Service (SaaS) clouds, software architects face new challenges in software architecture design, e.g. efficiently integrating those chosen web services into clouds. In this respect, early quality evaluation of the designed SaaS cloud application is crucial to mitigate the risk of later architectural changes due to a violation of quality requirements (such as response time, network throughput and scalability). Architects need to navigate in a rich set of offered services, a variability of the SaaS cloud environments, which makes it difficult to predict the general approach without time-consuming implementation of application prototypes (using GraphQL, Representational State Transfer (REST), Simple Object Access Protocol (SOAP), and Application Programming Interfaces (APIs) approaches). In this paper, we present an abstract application model designed to select the details with a high level of scalability and efficient interactions between service components. This model is reusable and provides new way of thinking about cloud architecture.

Recent advances in the Internet of Things (IoT) domain for deploying IoT data systems within the cloud have generated Internet-connected silos of sensor technology which make the collection and processing of sensor-generated information more complex. A viable solution to this problem is the use of local sub-servers acting as collector's hubs between mobile sensing devices and the cloud. Publish/subscribe mechanisms have the capacity to provide full control over the data acquisition and filtering process in mobile IoT environments as well as the methods for implementing optimisations related to energy-efficient data harvesting. The paper formulates design principles for IoT data management methods and optimisation algorithms by means of publish/subscribe middleware and linked data which span over mobile networks and cloud infrastructures to produce a coherent IoT ecosystem. Mobile sensing thus becomes a prominent feature of the OpenIoT platform1, the flaghship Open Source IoT middleware for services based on the automated cloud-based formulation of societies of Internet-connected objects.

Virtualization provides a coarse-grained isolation mechanism that results in large systems, with full operating systems and a complete software stack as their foundation. Though much of this foundation is not strictly necessary, the programmatic burden of building systems at a finer-granularity, on a smaller foundation, has previously been shown to be prohibitive. The aim of this work is to revisit this tension, and present an alternative, lightweight and composable approach to virtualization that we call MacroComponents-software components that run in isolation from the rest of the system, but without the full foundations of their more traditionally virtualized counterparts. We argue that this approach will provide a more scalable and sustainable approach for composing robust services in cloud environments, both in terms of dynamic system properties and software engineering qualities.

In this paper, we present an approach for improving fault-tolerance and service availability in intelligent video surveillance (IVS) systems. A typical IVS system consists of various intelligent video sensors that combine image sensing with video analysis and network streaming. System monitoring and fault diagnosis followed by appropriate dynamic system reconfiguration mitigate effects of faults and therefore enhance the system's fault-tolerance. The applied monitoring and diagnosis unit (MDU) allows the detection of both node- and system-level faults. Lacking redundant hardware such reconfigurations are established by graceful degradation of the overall application. An optimizer module that performs multi-criterion optimization is used to compute a new degraded system configuration by trading off quality of service (QoS), energy consumption, and service availability. We demonstrate the functionality of our approach by an illustrative example.

This paper is mainly based on the video monitoring of unmanned substation, with the detection and tracking object of moving target, combined with the inter-frame difference method and background difference method, in order to achieve effective detection of moving targets through improving the traditional algorithm; combined with cam- shift algorithm and mean-shift algorithm to reduce the calculated amount and improve the accuracy of target tracking, in order to achieve intelligence level of substation video monitoring. This paper uses the computer vision and video analysis method to further analyze and understand the image sequences recorded by the camera, and links with related equipment to do simple processing while timely giving an alarm and feedback to monitoring personnel. In this way, the monitoring personnel can offer centralized processing for accident alarm, thus greatly reducing erroneous judgment and missing judgment of accidents, and improving the reliability, accuracy and timeliness of the alarm. Therefore, the intelligent video analysis is applied to substation attending, and combined with the traditional monitoring system, thus having a very important significance on security monitoring of the substation.

With the acceleration of digital construction process, robotic process automation technology is more and more applied in the company's digital business, in order to achieve the goal of reducing cost, improving quality and increasing efficiency. By analyzing the scenarios where robotic process automation is required in the daily work of regional power supply services and command centers, this paper provides solution ideas and designs for key software functions of robotic process automation to assist the labor force. Combined with the actual application effect analysis. Put forward relevant conclusions and suggestions. Against the "three a" national power grid operating space after transforming the defect of operations management, through the intelligent video monitoring equipment installed in open space, intelligent video analysis technology is used to collect subregional traffic statistics, studies the VIP customers recognition and customer behavior analysis, and other functions, under the condition of without changing the original structure, further enhance the level of intelligent management of power supply business hall, Standardize the service quality of the staff of the power supply business hall, conduct the operation efficiency analysis of the power supply business hall, save management and reduce costs, improve the on-site control function, and improve the company image.

Due to the high growth of SNS population, service scalability is one of the critical issues to be addressed. The cloud environment provides the flexible computing and storage resources for services deployment, which fits the characteristics of scalable SNS deployment. However, if the SNS related information is not properly placed, it will cause unbalance load and heavy transmission cost on the storage virtual machine (VM) and cloud data center (CDC) network. In this paper, we characterize the SNS into a graph model based on the users associations and interest correlations. The node weight represents the degree of associations, which can be indexed by the number of friends or data sources, and the link weight denotes the correlation between users/data sources. Then, based on the SNS graph, the two-step algorithm is proposed in this paper to determine the placement of SNS related data among VMs. Two k-means based clustering schemes are proposed to allocate social data in proper VM and physical servers for pre-configured VM and dynamic VM environment, respectively. The experimental example was conducted and to illustrate and compare the performance of the proposed schemes.