A performance evaluation of public cloud using TPC-C
- Authors: Yao, Jinhui , Ng, Alex , Chen, Shiping , Liu, Dongxi , Friedrich, Carsten , Nepal, Surya
- Date: 2012
- Type: Text , Conference paper
- Relation: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2012 International Conference on Service-Oriented Computing, ICSOC 2012; Shanghai; China; 12 November 2012 through 15 November 2012 Vol. 7759 LNCS, p. 3-13
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- Description: Cloud is becoming the next-generation computing paradigm for enterprises to deploy services and run business. While most Cloud service providers promise some Quality of Service (QoS) through a Service Level Agreement (SLA), it is very hard for Cloud clients to know what impacts these QoS have on their businesses. In this paper, we study this issue by conducting a simple performance evaluation of two public Clouds. We selected TPC-C to benchmark three types of instances (Small, Medium and Large) provided by the Cloud providers in order to find out how the typical online transaction process systems perform on the cloud nodes. Our testing results show that the different Cloud environments deliver very different performance landscapes with different Cloud instances. Our work demonstrates the importance and opportunity to choose the appropriate Cloud instance in achieving an optimal cost-performance ratio for a class of cloud applications. © Springer-Verlag 2013.
Network-aware virtual machine placement and migration in cloud data centres
- Authors: Ferdaus, Md Hasanul , Murshed, Manzur , Clalheiros, Rodrigo , Buyya, Rajkumar
- Date: 2015
- Type: Text , Book chapter
- Relation: Emerging research in cloud distributed computing systems (Advances in systems analysis, software engineering, and high performance computing (ASASEHPC) book series) Chapter 2 p. 42-91
- Full Text: false
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- Description: With the pragmatic realization of computing as a utility, Cloud Computing has recently emerged as a highly successful alternative IT paradigm. Cloud providers are deploying large-scale data centers across the globe to meet the Cloud customers’ compute, storage, and network resource demands. Efficiency and scalability of these data centers, as well as the performance of the hosted applications’ highly depend on the allocations of the data center resources. Very recently, network-aware Virtual Machine (VM) placement and migration is developing as a very promising technique for the optimization of compute-network resource utilization, energy consumption, and network traffic minimization. This chapter presents the relevant background information and a detailed taxonomy that characterizes and classifies the various components of VM placement and migration techniques, as well as an elaborate survey and comparative analysis of the state of the art techniques. Besides highlighting the various aspects and insights of the network-aware VM placement and migration strategies and algorithms proposed by the research community, the survey further identifies the benefits and limitations of the existing techniques and discusses on the future research directions.
Generating a performance test-bed for cloud computing systems
- Authors: Chen, Feifei
- Date: 2013
- Type: Text , Conference paper
- Relation: 22nd Australian Software Engineering Conference
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- Description: Cloud computing delivers IT solutions as a utility to users. A common economic objective for both cloud consumers and providers is to minimise their total deployment and operational costs while achieving satisfactory system performance to meet Service Level Agreements (SLAs). Therefore, the trade-off between cost and system performance is needed to be managed to achieve the best cost effectiveness. The PhD project presented in this paper provides an efficient and accurate method to evaluate the trade-off between cost and system performance by proposing a performance testing tool for Cloud systems. This tool can accommodate different Cloud system architectures and adopt different workload and cost models of Cloud systems during the trade-off evaluation process.
Energy-aware virtual machine consolidation in IaaS cloud computing
- Authors: Ferdaus, Md Hasanul , Murshed, Manzur
- Date: 2014
- Type: Text , Book chapter
- Relation: Cloud Computing : Challenges, Limitations and R&D Solutions (Computer Communications and Networks series) Chapter 8 p. 179-208
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- Description: With immense success and rapid growth within the past few years, cloud computing has been established as the dominant paradigm of IT industry. To meet the increasing demand of computing and storage resources, infrastructure cloud providers are deploying planet-scale data centers across the world, consisting of hundreds of thousands, even millions of servers. These data centers incur very high investment and operating costs for the compute and network devices as well as for the energy consumption. Moreover, because of the huge energy usage, such data centers leave large carbon footprints and thus have adverse effects on the environment. As a result, efficient computing resource utilization and energy consumption reduction are becoming crucial issues to make cloud computing successful. Intelligent workload placement and relocation is one of the primary means to address these issues. This chapter presents an overview of the infrastructure resource management systems and technologies and detailed description of the proposed solution approaches for efficient cloud resource utilization and minimization of power consumption and resource wastages. Different types of server consolidation mechanisms are presented along with the solution approaches proposed by the researchers of both academia and industry. Various aspects of workload reconfiguration mechanisms and existing works on workload relocation techniques are described.
QoS-aware service selection for customisable multi-tenant service-based systems : Maturity and approaches
- Authors: He, Qiang , Han, Jun , Chen, Feifei , Wang, Yanchun , Vasa, Rajesh , Yang, Yun , Jin, Hai
- Date: 2015
- Type: Text , Conference paper
- Relation: 2015 IEEE 8th International Conference on Cloud Computing (CLOUD) p. 237-244
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- Description: Multi-tenant service-based systems (SBSs) have become a major paradigm in software engineering in the cloud environment. Instead of serving a single end-user, a multitenant SBS provides multiple tenants with similar and yet customised functionalities with potentially different quality-of service (QoS) values. Thus, existing approaches to service selection for single-tenant SBSs are no longer suitable. Furthermore, the target multi-tenancy maturity level also needs to be considered in the service selection approach for an SBS. In this paper, we propose three novel QoS-aware service selection approaches for composing multi-tenant SBSs that achieve three different multi-tenancy maturity levels. Extensive and comprehensive experiments are conducted and the experimental results show that our approaches outperform the existing approach in both effectiveness and efficiency.
An algorithm for network and data-aware placement of multi-tier applications in cloud data centers
- Authors: Ferdaus, Md Hasanul , Murshed, Manzur , Calheiros, Rodrigo , Buyya, Rajkumar
- Date: 2017
- Type: Text , Journal article
- Relation: Journal of Network and Computer Applications Vol. 98, no. (2017), p. 65-83
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- Description: Today's Cloud applications are dominated by composite applications comprising multiple computing and data components with strong communication correlations among them. Although Cloud providers are deploying large number of computing and storage devices to address the ever increasing demand for computing and storage resources, network resource demands are emerging as one of the key areas of performance bottleneck. This paper addresses network-aware placement of virtual components (computing and data) of multi-tier applications in data centers and formally defines the placement as an optimization problem. The simultaneous placement of Virtual Machines and data blocks aims at reducing the network overhead of the data center network infrastructure. A greedy heuristic is proposed for the on-demand application components placement that localizes network traffic in the data center interconnect. Such optimization helps reducing communication overhead in upper layer network switches that will eventually reduce the overall traffic volume across the data center. This, in turn, will help reducing packet transmission delay, increasing network performance, and minimizing the energy consumption of network components. Experimental results demonstrate performance superiority of the proposed algorithm over other approaches where it outperforms the state-of-the-art network-aware application placement algorithm across all performance metrics by reducing the average network cost up to 67% and network usage at core switches up to 84%, as well as increasing the average number of application deployments up to 18%. © 2017 Elsevier Ltd
Automating Performance and Energy Consumption Analysis for Cloud Applications
- Authors: Chen, Feifei , Grundy, John , Schneider, Jean-Guy , Yang, Yun , He, Qiang
- Date: 2015
- Type: Text , Conference paper
- Relation: 2015 IEEE World Congress on Services
- Full Text: false
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- Description: In cloud environments, IT solutions are delivered to users via shared infrastructure, enabling cloud service providers to deploy applications as services according to user QoS (Quality of Service) requirements. One consequence of this cloud model is the huge amount of energy consumption and significant carbon footprints caused by large cloud infrastructures. A key and common objective of cloud service providers is thus to develop cloud application deployment and management solutions with minimum energy consumption while guaranteeing performance and other QoS specified in Service Level Agreements (SLAs). However, finding the best deployment configuration that maximises energy efficiency while guaranteeing system performance is an extremely challenging task, which requires the evaluation of system performance and energy consumption under various workloads and deployment configurations. In order to simplify this process we have developed Stress Cloud, an automatic performance and energy consumption analysis tool for cloud applications in real-world cloud environments. Stress Cloud supports the modelling of realistic cloud application workloads, the automatic generation of load tests, and the profiling of system performance and energy consumption. We demonstrate the utility of Stress Cloud by analysing the performance and energy consumption of a cloud application under a broad range of different deployment configurations.
Distributed database management systems : Architectural design choices for the cloud
- Authors: Kamal, Joarder , Murshed, Manzur
- Date: 2014
- Type: Text , Book chapter
- Relation: Cloud Computing : Challenges, Limitations and R&D Solutions (Computer Communications and Networks series) Chapter 2 p. 23-50
- Full Text: false
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- Description: Cloud computing has changed the way we used to exploit software and systems. The two decades’ practice of architecting solutions and services over the Internet has just revolved within the past few years. End users are now relying more on paying for what they use instead of purchasing a full-phase license. System owners are also in rapid hunt for business profits by deploying their services in the Cloud and thus maximising global outreach and minimising overall management costs. However, deploying and scaling Cloud applications regionally and globally are highly challenging. In this context, distributed data management systems in the Cloud promise rapid elasticity and horizontal scalability so that Cloud applications can sustain enormous growth in data volume, velocity, and value. Besides, distributed data replication and rapid partitioning are the two fundamental hammers to nail down these challenges. While replication ensures database read scalability and georeachability, data partitioning favours database write scalability and system-level load balance. System architects and administrators often face difficulties in managing a multi-tenant distributed database system in Cloud scale as the underlying workload characteristics change frequently. In this chapter, the inherent challenges of such phenomena are discussed in detail alongside their historical backgrounds. Finally, potential way outs to overcome such architectural barriers are presented under the light of recent research and development in this area.
Dynamic virtual machine consolidation algorithms for energy-efficient cloud resource management : A review
- Authors: Khan, Md Anit , Paplinski, Andrew , Khan, Abdul , Murshed, Manzur , Buyya, Rajkumar
- Date: 2017
- Type: Text , Book chapter
- Relation: Sustainable Cloud and Energy Services : Principles and Practice Chapter 6 p. 135-165
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- Description: As envisioned by Leonard Kleinrock [1], Cloud computing has transformed the dream of “computing as a utility” into reality, so much so it has turned out as the latest computing paradigm [2]. Cloud computing is called as Service-on-demand, as Cloud Service Providers (CSPs) assure users about potentially unlimited amount of resources that can be chartered on demand. It is also known as elastic computing, since Cloud Service Users (CSUs) can dynamically scale, expand, or shrink their rented resources anytime and expect to pay for the exact tenure of resource usage under Service Level Agreements (SLA). Through such flexibilities and financial benefits, CSPs have been attracting millions of clients who are simultaneously sharing the underlying computing and storage resources that are collectively known as Cloud data centers.
Automated analysis of performance and energy consumption for cloud applications
- Authors: Chen, Feifei , Grundy, John , Schneider, Jean-Guy , Yang, Yun , He, Qiang
- Date: 2014
- Type: Text , Conference paper
- Relation: Proceedings of the 5th ACM/SPEC international conference on Performance engineering p. 39-50
- Full Text:
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- Description: In cloud environments, IT solutions are delivered to users via shared infrastructure. One consequence of this model is that large cloud data centres consume large amounts of energy and produce significant carbon footprints. A key objective of cloud providers is thus to develop resource provisioning and management solutions at minimum energy consumption while still guaranteeing Service Level Agreements (SLAs). However, a thorough understanding of both system performance and energy consumption patterns in complex cloud systems is imperative to achieve a balance of energy efficiency and acceptable performance. In this paper, we present StressCloud, a performance and energy consumption analysis tool for cloud systems. StressCloud can automatically generate load tests and profile system performance and energy consumption data. Using StressCloud, we have conducted extensive experiments to profile and analyse system performance and energy consumption with different types and mixes of runtime tasks. We collected fine-grained energy consumption and performance data with different resource allocation strategies, system configurations and workloads. The experimental results show the correlation coefficients of energy consumption, system resource allocation strategies and workload, as well as the performance of the cloud applications. Our results can be used to guide the design and deployment of cloud applications to balance energy and performance requirements.
An energy consumption model and analysis tool for Cloud computing environments
- Authors: Chen, Feifei , Schneider, Jean-Guy , Yang, Yun , Grundy, John , He, Qiang
- Date: 2012
- Type: Text , Conference paper
- Relation: 2012 First International Workshop on Green and Sustainable Software (GREENS) : Part of the 34th International Conference on Software Engineering (ICSE) p. 45-50
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- Description: Cloud computing delivers computing as a utility to users worldwide. A consequence of this model is that cloud data centres have high deployment and operational costs, as well as significant carbon footprints for the environment. We need to develop Green Cloud Computing (GCC) solutions that reduce these deployment and operational costs and thus save energy and reduce adverse environmental impacts. In order to achieve this objective, a thorough understanding of the energy consumption patterns in complex Cloud environments is needed. We present a new energy consumption model and associated analysis tool for Cloud computing environments. We measure energy consumption in Cloud environments based on different runtime tasks. Empirical analysis of the correlation of energy consumption and Cloud data and computational tasks, as well as system performance, will be investigated based on our energy consumption model and analysis tool. Our research results can be integrated into Cloud systems to monitor energy consumption and support static or dynamic system-level optimisation.
Keyword search for building service-based systems
- Authors: He, Qiang , Zhou, Rui , Zhang, Xuyun , Wang, Yanchun , Ye, Dayong , Chen, Feifei , Grundy, John , Yang, Yun
- Date: 2017
- Type: Text , Journal article
- Relation: IEEE Transactions on Software Engineering Vol. 43, no. 7 (2017), p. 658-674
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- Description: With the fast growth of applications of service-oriented architecture (SOA) in software engineering, there has been a rapid increase in demand for building service-based systems (SBSs) by composing existing Web services. Finding appropriate component services to compose is a key step in the SBS engineering process. Existing approaches require that system engineers have detailed knowledge of SOA techniques which is often too demanding. To address this issue, we propose Keyword Search for Service-based Systems (KS3), a novel approach that integrates and automates the system planning, service discovery and service selection operations for building SBSs based on keyword search. KS3 assists system engineers without detailed knowledge of SOA techniques in searching for component services to build SBSs by typing a few keywords that represent the tasks of the SBSs with quality constraints and optimisation goals for system quality, e.g., reliability, throughput and cost. KS3 offers a new paradigm for SBS engineering that can significantly save the time and effort during the system engineering process. We conducted large-scale experiments using two real-world Web service datasets to demonstrate the practicality, effectiveness and efficiency of KS3. © 1976-2012 IEEE.
A Model for human activity recognition in ambient assisted living
- Authors: do Amaral, Wagner , Dantas, Mario , Campos, Fernanda
- Date: 2020
- Type: Text , Book chapter
- Relation: Advances on P2P, Parallel, Grid, Cloud and Internet Computing Chapter 29 p.
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- Description: This work presents a model for human activity recognition, through an IoT paradigm, using location and movement data, generated from an accelerometer. The activities of five individuals from different age groups were monitored, utilizing IoT devices, using the activities of four of these individuals to train the model and the activities of the remaining individual for test data. For the prediction of the activities, the Extra Trees algorithm was used, where the results of 81.16% accuracy were obtained when only movement data were used, 92.59% when using both movement and location data, and 97.56% when using movement data and synthetic location data.
The role of big data analytics in industrial internet of things
- Authors: Rehman, Muhammad , Yaqoob, Ibrar , Salah, Khaled , Imran, Muhammad , Jayaraman, Prem , Perera, Charith
- Date: 2019
- Type: Text , Journal article
- Relation: Future Generation Computer Systems Vol. 99, no. (2019), p. 247-259
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- Description: Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational- and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT. We identify and discuss the indispensable challenges that remain to be addressed, serving as future research directions. © 2019 Elsevier B.V.
Secure and efficient data delivery for fog-assisted wireless body area networks
- Authors: Hayajneh, Thaier , Griggs, Kristen , Imran, Muhammad , Mohd, Bassam
- Date: 2019
- Type: Text , Journal article
- Relation: Peer-to-Peer Networking and Applications Vol. 12, no. 5 (2019), p. 1289-1307
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- Description: The growth of remote patient monitoring technology introduces new opportunities for improving patient outcomes, and Wireless Body Area Networks (WBANs) are a key piece in building a successful system. However, due to the limited power and computational resources of WBAN sensor nodes, combined with user mobility and large network coverage areas, integrating WBANs with cloud and fog computing presents one of the most viable options for successful remote monitoring. In order to help maintain the real-time operations of a fog-assisted WBAN, we propose a secure and efficient data delivery protocol that will reduce delay and protect against malicious attacks on the wireless signal. The protocol is composed of three custom algorithms that address channel assignment, gateway association, and introduce a new delay- and energy-aware routing metric. The channel assignment algorithm is designed to minimize and avoid interference, including jamming nodes. The fog gateway association algorithm helps to improve the efficiency and security of the connection between the WBAN and the remote resources. Similarly, the proposed routing metric is used to construct routes that both minimize delay and conserve power at the nodes along the path for improved efficiency and lifespan of the network. The system was simulated and tested under a variety of conditions to evaluate its performance in regards to mutual interference, human mobility, fog density, and attacks by jamming nodes. The results showed clear improvements in the efficiency and resiliency of the fog-assisted WBAN system when utilizing our protocol. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.
Security and privacy aspects of cloud computing : a smart campus case study
- Authors: Gill, Sajid , Razzaq, Mirza , Ahmad, Muneer , Almansour, Fahad , Haq, Ikram
- Date: 2022
- Type: Text , Journal article
- Relation: Intelligent Automation and Soft Computing Vol. 31, no. 1 (2022), p. 117-128
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- Description: The trend of cloud computing is accelerating along with emerging technologies such as utility computing, grid computing, and distributed computing. Cloud computing is showing remarkable potential to provide flexible, cost- effective, and powerful resources across the internet, and is a driving force in today’s most prominent computing technologies. The cloud offers the means to remotely access and store data while virtual machines access data over a network resource. Furthermore, cloud computing plays a leading role in the fourth industrial revolution. Everyone uses the cloud daily life when accessing Dropbox, various Google services, and Microsoft Office 365. While there are many advantages in such an environment, security issues such as data privacy, data security, access control, cyber-attacks, and data availability, along with performance and reliability issues, exist. Efficient security and privacy measures should be implemented by cloud service providers to ensure the privacy, confidentiality, integrity, and availability of data services. However, cloud service providers have not been providing enough secure and reliable services to end users. Blockchain is a technology that is improving cloud computing. This revolutionary technology offers persuasive data integrity properties and is used to tackle security problems. This research presents a detailed analysis of privacy and security challenges in the cloud. We demonstrate the importance of security challenges in a case study in the context of smart campus security, which will encourage researchers to examine security issues in cloud computing in the future. © 2022, Tech Science Press. All rights reserved. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Ikram Haq” is provided in this record**
Countering stasistical attacks in cloud-based searchable encryption
- Authors: Ahsan, M. , Ali, Ihsan , Bin Idris, Mohd , Imran, Muhammad , Shoaib, Muhammad
- Date: 2020
- Type: Text , Journal article
- Relation: International Journal of Parallel Programming Vol. 48, no. 3 (2020), p. 470-495
- Full Text: false
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- Description: Searchable encryption (SE) is appearing as a prominent solution in the intersection of privacy protection and efficient retrieval of data outsourced to cloud computing storage. While it preserves privacy by encrypting data, yet supports search operation without data leakage. Due to its applicability, many research communities have proposed different SE schemes under various security definitions with numerous customary features (i.e. multi keyword search, ranked search). However, by reason of multi-keyword ranked search, SE discloses encrypted document list corresponding to multiple (secure) query keywords (or trapdoor). Such disclosure of statistical information helps an attacker to analyze and deduce the content of the data. To counter statistical information leakage in SE, we propose a scheme referred to as Countering Statistical Attack in Cloud based Searchable Encryption (CSA-CSE) that resorts to randomness in all components of an SE. CSA-CSE adopts inverted index that is built with a hash digest of a pair of keywords. Unlike existing schemes, ranking factors (i.e. relevance scores) rank the documents and then they no longer exist in the secure index (neither in order preserving encrypted form). Query keywords are also garbled with randomness in order to hide actual query/result statistics. Our security analysis and experiment on request for comments database ensure the security and efficiency of CSA-CSE. © 2018, Springer Science+Business Media, LLC, part of Springer Nature. Correction to: Countering Statistical Attacks in Cloud-Based Searchable Encryption (International Journal of Parallel Programming, (2020), 48, 3, (470-495), 10.1007/s10766-018-0584-8)The original article has been published with an incorrect grant number in the acknowledgements which should be RG # 1439-036. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.
Cloud robotics in smart manufacturing environments : challenges and countermeasures
- Authors: Yan, Hehua , Hua, Qingsong , Wang, Yingying , Wei, Wenguo , Imran, Muhammad
- Date: 2017
- Type: Text , Journal article
- Relation: Computers and Electrical Engineering Vol. 63, no. (2017), p. 56-65
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- Description: In Smart Manufacturing Environments (SME), the use of cloud robotics is based on the integration of cloud computing and industrial robots, which provides a new technological approach to task execution and resource sharing compared to traditional industrial robots. However, research on cloud robotics in SME still faces some challenges. First, highly flexible load scheduling mechanisms are immature. Second, traditional optimization mechanisms for the network service quality do not meet the requirements of smart manufacturing due to time variability and service quality dynamics. And, finally, existing learning algorithms used without cloud-assisted resources cause great resource wasting. Accordingly, this paper explores main technologies related to cloud robotics in SME. The research contents include self-adaptive adjustment mechanisms for the service quality of a cloud robot network, computing load allocation mechanisms for cloud robotics, and group learning based on a cloud platform. The results presented in this paper are helpful to understand the internal mechanisms of perception and interaction, intelligent scheduling and control of cloud robot systems oriented to smart manufacturing, and the design of a cloud architecture oriented to group learning. © 2017
P2DCA: A Privacy-preserving-based data collection and analysis framework for IoMT applications
- Authors: Usman, Muhammad , Jan, Mian Ahmad , He, Xiangjian , Chen, Jinjun
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE journal on selected areas in communications Vol. 37, no. 6 (2019), p. 1222-1230
- Full Text: false
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- Description: The concept of Internet of Multimedia Things (IoMT) is becoming popular nowadays and can be used in various smart city applications, e.g., traffic management, healthcare, and surveillance. In the IoMT, the devices, e.g., Multimedia Sensor Nodes (MSNs), are capable of generating both multimedia and non-multimedia data. The generated data are forwarded to a cloud server via a Base Station (BS). However, it is possible that the Internet connection between the BS and the cloud server may be temporarily down. The limited computational resources restrict the MSNs from holding the captured data for a longer time. In this situation, mobile sinks can be utilized to collect data from MSNs and upload to the cloud server. However, this data collection may create privacy issues, such as revealing identities and location information of MSNs. Therefore, there is a need to preserve the privacy of MSNs during mobile data collection. In this paper, we propose an efficient privacy-preserving-based data collection and analysis (P2DCA) framework for IoMT applications. The proposed framework partitions an underlying wireless multimedia sensor network into multiple clusters. Each cluster is represented by a Cluster Head (CH). The CHs are responsible to protect the privacy of member MSNs through data and location coordinates aggregation. Later, the aggregated multimedia data are analyzed on the cloud server using a counter-propagation artificial neural network to extract meaningful information through segmentation. Experimental results show that the proposed framework outperforms the existing privacy-preserving schemes, and can be used to collect multimedia data in various IoMT applications.
Frame interpolation for cloud-based mobile video streaming
- Authors: Usman, Muhammad , Xiangjian, He , Kin-Man, Lam , Min, Xu , Bokhari, Syed , Jinjun, Chen
- Date: 2016
- Type: Text , Journal article
- Relation: IEEE transactions on multimedia Vol. 18, no. 5 (2016), p. 831-839
- Full Text: false
- Reviewed:
- Description: Cloud-based High Definition (HD) video streaming is becoming popular day by day. On one hand, it is important for both end users and large storage servers to store their huge amount of data at different locations and servers. On the other hand, it is becoming a big challenge for network service providers to provide reliable connectivity to the network users. There have been many studies over cloud-based video streaming for Quality of Experience (QoE) for services like YouTube. Packet losses and bit errors are very common in transmission networks, which affect the user feedback over cloud-based media services. To cover up packet losses and bit errors, Error Concealment (EC) techniques are usually applied at the decoder/receiver side to estimate the lost information. This paper proposes a time-efficient and quality-oriented EC method. The proposed method considers H.265/HEVC based intra-encoded videos for the estimation of whole intra-frame loss. The main emphasis in the proposed approach is the recovery of Motion Vectors (MVs) of a lost frame in real-time. To boost-up the search process for the lost MVs, a bigger block size and searching in parallel are both considered. The simulation results clearly show that our proposed method outperforms the traditional Block Matching Algorithm (BMA) by approximately 2.5 dB and Frame Copy (FC) by up to 12 dB at a packet loss rate of 1%, 3%, and 5% with different Quantization Parameters (QPs). The computational time of the proposed approach outperforms the BMA by approximately 1788 seconds.