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  • 1.
    Alhamazani, Khalid
    et al.
    University of New South Wales, Sydney.
    Ranjan, Rajiv
    CSIRO, Canberra.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Rabhi, Fethi
    University of New South Wales, Sydney.
    Jayaraman, Prem Prakash
    CSIRO, Canberra.
    Khan, Samee Ullah
    North Dakota State University, Fargo.
    Guabtni, Adnene
    NICTA, Sydney.
    Bhatnagar, Vasudha
    University of Delhi.
    An overview of the commercial cloud monitoring tools: research dimensions, design issues, and state-of-the-art2015In: Computing, ISSN 0010-485X, E-ISSN 1436-5057, Vol. 97, no 4, p. 357-377Article in journal (Refereed)
    Abstract [en]

    Cloud monitoring activity involves dynamically tracking the Quality of Service (QoS) parameters related to virtualized resources (e.g., VM, storage, network, appliances, etc.), the physical resources they share, the applications running on them and data hosted on them. Applications and resources configuration in cloud computing environment is quite challenging considering a large number of heterogeneous cloud resources. Further, considering the fact that at given point of time, there may be need to change cloud resource configuration (number of VMs, types of VMs, number of appliance instances, etc.) for meet application QoS requirements under uncertainties (resource failure, resource overload, workload spike, etc.). Hence, cloud monitoring tools can assist a cloud providers or application developers in: (i) keeping their resources and applications operating at peak efficiency, (ii) detecting variations in resource and application performance, (iii) accounting the service level agreement violations of certain QoS parameters, and (iv) tracking the leave and join operations of cloud resources due to failures and other dynamic configuration changes. In this paper, we identify and discuss the major research dimensions and design issues related to engineering cloud monitoring tools. We further discuss how the aforementioned research dimensions and design issues are handled by current academic research as well as by commercial monitoring tools.

  • 2.
    Alhamazani, Khalid
    et al.
    University of New South Wales.
    Ranjan,, Rajiv
    CSIRO, Australia.
    Rabhi, Fethi
    University of New South Wales.
    Wang, Lizhe
    Mitra, Karan
    Cloud Monitoring for Optimizing the QoS of Hosted Applications2013In: IEEE CloudCom, IEEE Communications Society, 2013Conference paper (Refereed)
  • 3.
    Alhamazani, Khalid
    et al.
    School of Computer Science and Engineering, University of New South Wales.
    Ranjan, Rajiv
    CSIRO Digital Productivity, Acton.
    Jayaraman, Prem
    CSIRO Digital Productivity, Acton.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Liu, Chang
    Sydney University of Technology.
    Rabhi, Fethi
    School of Computer Science and Engineering, University of New South Wales.
    Georgakopoulos, Dimitrios
    Royal Melbourne Institute of Technology, Melbourne.
    Wang, Lizhe
    Chinese Academy of Sciences, Beijing.
    Cross-Layer Multi-Cloud Real-Time Application QoS Monitoring and Benchmarking As-a-Service Framework2019In: I E E E Transactions on Cloud Computing, ISSN 2168-7161, Vol. 7, no 1, p. 48-61Article in journal (Refereed)
    Abstract [en]

    Cloud computing provides on-demand access to affordable hardware (e.g., multi-core CPUs, GPUs, disks, and networking equipment) and software (e.g., databases, application servers and data processing frameworks) platforms with features such as elasticity, pay-per-use, low upfront investment and low time to market. This has led to the proliferation of business critical applications that leverage various cloud platforms. Such applications hosted on single/multiple cloud provider platforms have diverse characteristics requiring extensive monitoring and benchmarking mechanisms to ensure run-time Quality of Service (QoS) (e.g., latency and throughput). This paper proposes, develops and validates CLAMBS—Cross-Layer Multi Cloud Application Monitoring and Benchmarking as-a-Service for efficient QoS monitoring and benchmarking of cloud applications hosted on multi-clouds environments. The major highlight of CLAMBS is its capability of monitoring and benchmarking individual application components such as databases and web servers, distributed across cloud layers (*-aaS), spread among multiple cloud providers. We validate CLAMBS using prototype implementation and extensive experimentation and show that CLAMBS efficiently monitors and benchmarks application components on multi-cloud platforms including Amazon EC2 and Microsoft Azure.  

  • 4.
    Alhamazani, Khalid
    et al.
    School of Computer Science and Engineering, University of New South Wales.
    Ranjan, Rajiv
    CSIRO Computational Informatics, Canberra.
    Jayaraman, Prem Prakash
    CSIRO Computational Informatics, Canberra.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Wang, Meisong
    CSIRO Computational Informatics, Canberra.
    Huang, Zhiquiang (George)
    CSIRO Computational Informatics, Canberra.
    Wang, Lizhe
    Chinese Academy of Sciences, Beijing.
    Rabhi, Fethi
    School of Computer Science and Engineering, University of New South Wales.
    Real-time QoS Monitoring for Cloud-based Big Data Analytics Application in Mobile Environments2014In: 2014 15th IEEE International Conference on Mobile Data Management (MDM 2014): Brisbane, Australia 15 -18 July 2014, Piscataway, NY: IEEE Communications Society, 2014, Vol. 1, p. 337-340, article id 6916940Conference paper (Refereed)
    Abstract [en]

    The service delivery model of cloud computing acts as a key enabler for big data analytics applications enhancing productivity, efficiency and reducing costs. The ever increasing flood of data generated from smart phones and sensors such as RFID readers, traffic cams etc require innovative provisioning and QoS monitoring approaches to continuously support big data analytics. To provide essential information for effective and efficient bid data analytics application QoS monitoring, in this paper we propose and develop CLAMS-Cross-Layer Multi-Cloud Application Monitoring-as-a-Service Framework. The proposed framework: (a) performs multi-cloud monitoring, and (b) addresses the issue of cross-layer monitoring of applications. We implement and demonstrate CLAMS functions on real-world multi-cloud platforms such as Amazon and Azure.

  • 5.
    Alhamazani, Khalid
    et al.
    University of New South Wales, Sydney.
    Ranjan, Rajiv
    CSIRO Computational Informatics, Canberra.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Jayaraman, Prem Prakash
    CSIRO, Canberra.
    Huang, Zhiqiang
    CSIRO Computational Informatics, Canberra.
    Wang, Lizhe
    Center for Earth Observation & Digital Earth, Chinese Academy of Sciences.
    Rabhi, Fethi
    University of New South Wales, Sydney.
    CLAMS: Cross-Layer Multi-Cloud Application Monitoring-as-a-Service Framework2014In: Proceedings of the 11th IEEE International Conference on Services Computing (IEEE SCC 2014), Piscataway, NJ: IEEE Communications Society, 2014, p. 283-290Conference paper (Refereed)
    Abstract [en]

    Cloud computing provides on-demand access to affordable hardware (e.g., multi-core CPUs, GPUs, disks, and networking equipment) and software (e.g., databases, application servers, data processing frameworks, etc.) platforms. Application services hosted on single/multiple cloud provider platforms have diverse characteristics that require extensive monitoring mechanisms to aid in controlling run-time quality of service (e.g., access latency and number of requests being served per second, etc.). To provide essential real-time information for effective and efficient cloud application quality of service (QoS) monitoring, in this paper we propose, develop and validate CLAMS—Cross-Layer Multi-Cloud Application Monitoring-as-a-Service Framework. The proposed framework is capable of: (a) performing QoS monitoring of application components (e.g., database, web server, application server, etc.) that may be deployed across multiple cloud platforms (e.g., Amazon and Azure); and (b) giving visibility into the QoS of individual application component, which is something not supported by current monitoring services and techniques. We conduct experiments on real-world multi-cloud platforms such as Amazon and Azure to empirically evaluate our framework and the results validate that CLAMS efficiently monitors applications running across multiple clouds.

  • 6.
    Belyakhina, Tamara
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zaslavsky, Arkady
    CSIRO, Melbourne.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Saguna, Saguna
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Jayaraman, Prem Prakash
    Swinburne University of Technology, Melbourne.
    DisCPAQ: Distributed Context Acquisition and Reasoning for Personalized Indoor Air Quality Monitoring in IoT-Based Systems2017In: Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 17th International Conference, NEW2AN 2017, 10th Conference, ruSMART 2017, Third Workshop NsCC 2017, St. Petersburg, Russia, August 28–30, 2017, Proceedings / [ed] Galinina O., Andreev S., Balandin S., Koucheryavy Y., Cham: Springer, 2017, p. 75-86Conference paper (Refereed)
    Abstract [en]

    The rapidly emerging Internet of Things supports many diverse applications including environmental monitoring. Air quality, both indoors and outdoors, proved to be a significant comfort and health factor for people. This paper proposes a smart context-aware system for indoor air quality monitoring and prediction called DisCPAQ. The system uses data streams from air quality measurement sensors to provide real-time personalised air quality service to users through a mobile app. The proposed system is agnostic to sensor infrastructure. The paper proposes a context model based on Context Spaces Theory, presents the architecture of the system and identifies challenges in developing large scale IoT applications. DisCPAQ implementation, evaluation and lessons learned are all discussed in the paper.

  • 7.
    Georgakopoulos, Dimitrios
    et al.
    CSIRO, Australia.
    Ranjan, Rajiv
    CSIRO, Australia.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zhou, Xiangmin
    CSIRO, Australia.
    MediaWise: designing a smart media cloud2012Conference paper (Refereed)
    Abstract [en]

    The MediaWise project aims to expand the scope ofexisting media delivery systems with novel cloud, personalizationand collaboration capabilities that can serve the needs of moreusers, communities, and businesses. The project develops aMediaWise Cloud platform that supports do-it-yourself creation,search, management, and consumption of multimedia content.The MediaWise Cloud supports pay-as-you-go models andelasticity that are similar to those offered by commerciallyavailable cloud services. However, unlike existing commercialCDN services providers such as Limelight Networks and Akamaithe MediaWise Cloud require no ownerships of computinginfrastructure and instead rely on the public Internet and publiccloud services (e.g., commercial cloud storage to store its content).In addition to integrating such public cloud services into a publiccloud-based Content Delivery Network, the MediaWise Cloudalso provides advanced Quality of Service (QoS) management asrequired for the delivery of streamed and interactive highresolution multimedia content. In this paper, we give a briefoverview of MediaWise Cloud architecture and present acomprehensive discussion on research objectives related to itsservice components. Finally, we also compare the featuressupported by the existing CDN services against the envisionedobjectives of MediaWise Cloud.

  • 8.
    Jayaraman, Prem
    et al.
    RMIT University, Melbourne.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Saguna, Saguna
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Shah, Tejal
    School of Computer Science and Engineering, University of New South Wales.
    Georgeakopoulos, Dimitros
    Royal Melbourne Institute of Technology, Melbourne.
    Ranjan, Rajiv
    School of Computing Science, Newcastle University, Newcastle upon Tyne.
    Orchestrating Quality of Service in the Cloud of Things Ecosystem2015In: 2015 IEEE International Symposium on Nanoelectronic and Information Systems: Indore, 21-23 Dec. 2015, Piscataway, NJ: IEEE Communications Society, 2015, p. 185-190, article id 7434422Conference paper (Refereed)
    Abstract [en]

    Cloud of Things (CoT) is a vision inspired by Internet of Things (IoT) and cloud computing where the IoT devices are connected to the clouds via the Internet for datastorage, processing, analytics and visualization. CoT ecosystem will encompass heterogeneous clouds, networks and devices to provide seamless service delivery, for example, in smart cites. To enable efficient service delivery, there is a need to guarantee a certain level of quality of service from both cloud and networksperspective. This paper discusses the Cloud of Things, cloud computing, networks and new quality of service management research issues arising due to realisation of CoT ecosystem vision.

  • 9. Kodikara, R.E.
    et al.
    Mitra, Karan
    Monash University, Melbourne, VIC.
    Zaslavsky, Arkady
    Context aware vertical handovers in next generation networks2006In: Proceedings of HiPC Workshops 2006, 2006Conference paper (Refereed)
  • 10.
    Li, Jianjiang
    et al.
    Department of Computer Science and Technology, University of Science and Technology, Beijing.
    Zhang, Kai
    Department of Computer Science and Technology, University of Science and Technology, Beijing.
    Yang, Xiaolei
    Department of Computer Science and Technology, University of Science and Technology, Beijing.
    Wei, Peng
    Department of Computer Science and Technology, University of Science and Technology, Beijing.
    Wang, Jie
    Department of Computer Science and Technology, University of Science and Technology, Beijing.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Ranjan, Rajiv
    School of Computer Science, China University of Geosciences.
    Category Preferred Canopy-K-means based Collaborative Filtering algorithm2019In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 93, p. 1046-1054Article in journal (Refereed)
    Abstract [en]

    It is the era of information explosion and overload. The recommender systems can help people quickly get the expected information when facing the enormous data flood. Therefore, researchers in both industry and academia are also paying more attention to this area. The Collaborative Filtering Algorithm (CF) is one of the most widely used algorithms in recommender systems. However, it has difficulty in dealing with the problems of sparsity and scalability of data. This paper presents Category Preferred Canopy-K-means based Collaborative Filtering Algorithm (CPCKCF) to solve the challenges of sparsity and scalability of data. In particular, CPCKCF proposes the definition of the User-Item Category Preferred Ratio (UICPR), and use it to compute the UICPR matrix. The results can be applied to cluster the user data and find the nearest users to obtain prediction ratings. Our experimentation results performed using the MovieLens dataset demonstrates that compared with traditional user-based Collaborative Filtering algorithm, the proposed CPCKCF algorithm proposed in this paper improved computational efficiency and recommendation accuracy by 2.81%.

  • 11.
    Li, Zheng
    et al.
    School of Computer Science, Australian National University and NICTA.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zhang, Miranda
    CSIRO Computational Informatics, Canberra.
    Ranjan, Rajiv
    CSIRO Computational Informatics, Canberra.
    Georgakopoulos, Dimitrios
    CSIRO Computational Informatics, Canberra.
    Zomaya, Albert
    University of Sydney.
    O*Brien, Liam
    Geoscience Australia.
    Sun, Shengtao
    Yanshan University.
    Towards Understanding the Runtime Configuration Management of Do-It-Yourself Content Delivery Network Applications over Public Clouds2014In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 37, p. 297-308Article in journal (Refereed)
    Abstract [en]

    Cloud computing is a new paradigm shift which enables applications and related content (audio, video, text, images, etc.) to be provisioned in an on-demand manner and being accessible to anyone anywhere in the world without the need for owning expensive computing and storage infrastructures. Interactive multimedia content-driven applications in the domains of healthcare, aged-care, and education have emerged as one of the new classes of big data applications. This new generation of applications need to support complex content operations including production, deployment, consumption, personalisation, and distribution. However, to efficiently provision these applications on the Cloud data centres, there is a need to understand their run-time resource configurations. For example: (i) where to store and distribute the content to and from driven by end-user Service Level Agreements (SLAs)? (ii) how many content distribution servers to provision? and (iii) what Cloud VM configuration (number of instances, types, speed, etc.) to provision? In this paper, we present concepts and factors related to engineering such content-driven applications over public Clouds. Based on these concepts and factors, we propose a performance evaluation methodology for quantifying and understanding the runtime configuration these classes of applications. Finally, we conduct several benchmark driven experiments for validating the feasibility of the proposed methodology.

  • 12. Louis, Baptiste
    et al.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Saguna, Saguna
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Åhlund, Christer
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    CloudSimDisk: Energy-Aware Storage Simulation in CloudSim2015In: 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC): Limassol, 7-10 Dec. 2015, Piscataway, NJ: IEEE Communications Society, 2015, p. 11-15, article id 7431390Conference paper (Refereed)
    Abstract [en]

    The cloud computing paradigm is continually evolving, and with it, the size and the complexity of its infrastructure. Assessing the performance of a cloud environment is an essential but an arduous task. Further, the energy consumed by data centers is steadily increasing and major components such as the storage systems need to be more energy efficient. Cloud simulation tools have proved quite useful to study these issues. However, these simulation tools lack mechanisms to study energy efficient storage in cloud systems. This paper contributes in the area of cloud computing by extending the widely used cloud simulator CloudSim. In this paper, we propose CloudSimDisk, a scalable module for modeling and simulation of energy-aware storage in cloud systems. We show how CloudSimDisk can be used to simulate energy-aware storage, and can be extended to study new algorithms for energy-awareness in cloud systems. Our simulation results proved to be in accordance with the analytical models that were developed to model energy consumption of hard disk drives in cloud systems. The source code of CloudSimDisk is also made available for the research community for further testing and development.

  • 13.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Quality of experience measurement, prediction and provisioning in heterogeneous access networks2013Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    In mobile computing systems, users can access network services anywhere and anytime using mobile devices such as tablets and smart phones. Users usually have some expectations about the services provided to them by different service providers, for example, telecommunication operators and network providers. Users’ expectations along with additional factors such as cognitive and behavioural states, cost, and network quality of service (QoS) may determine their quality of experience (QoE). If users are not satisfied with their QoE, they may switch to different providers or may stop using a particular application or service. QoE measurement and prediction can benefit users to avail personalized services from service providers. On the other hand, it can help service providers to achieve lower user-operator switch-over. Users with mobile devices can roam in heterogeneous access networks (HANs). A mobile device may go through handoffs while roaming in HANs i.e., it may switch from one access point (AP) to another. These APs within a network can belong to different network technologies, for example, WLAN or 4G. Handoffs may cause severe QoE degradation due to increased delay and packet losses. Thus, there is a need to facilitate QoE-aware handoffs for users roaming in HANs. The mobile devices can learn from the prior network conditions and users' QoE to make timely and proactive QoE-aware handoffs.In this thesis, we propose, develop and validate a novel method, CaQoEM-Context-aware Quality of Experience, Modelling, Measurement and Prediction. CaQoEM is based on Bayesian networks and utility theory. It provides a straightforward and efficient way of dealing with a plethora of parameters to model, measure and predict QoE under uncertainty on a single scale. We validate CaQoEM using a number of case studies, user tests and simulations performed in OPNET. Our results validate that CaQoEM can efficiently model, measure and predict users' QoE. It achieves an average QoE prediction accuracy of 98.93% in stochastic wireless network conditions such as wireless signal fading, handoffs and wireless network congestion. We further extend our CaQoEM to develop SCaQoEM-Sequential Context-aware Quality of Experience Measurement and Prediction where we use dynamic Bayesian networks and utility theory to model, measure and predict users’ QoE over time. We performed a case study and our results validate the efficiency of SCaQoEM.In this thesis, we also propose, develop and validate a novel approach called PRONET-Proactive Context-aware Mobility Support in HANs. PRONET incorporates a novel method for QoE estimation and prediction using hidden Markov models and Multi-homed Mobile IP. It also incorporates a method for QoE-aware handoffs using Q-learning function. Using extensive simulations and experimental analysis, we show that PRONET achieves an average QoE prediction accuracy of 97%. Further, PRONET can maximize users’ QoE by reducing the average number of handoffs by 60.65%, compared to the state-of-the-art methods. The outcomes of this thesis have resulted in eleven peer-reviewed conference, workshop and journal papers along with three technical reports.

  • 14.
    Mitra, Karan
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Kodikara, Ruwini
    Monash University, Melbourne, VIC.
    Zaslavsky, Arkady
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A context-aware architecture to facilitate vertical handoffs in 4G networks using multi-homed mobile IP2009In: The First International Conference on Next Generation Wireless Systems, 2009Conference paper (Refereed)
  • 15.
    Mitra, Karan
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Saguna, Saguna
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Åhlund, Christer
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A Mobile Cloud Computing System for Emergency Management2014In: I E E E Cloud Computing, ISSN 2325-6095, Vol. 1, no 4, p. 30-38, article id 7057583Article in journal (Refereed)
    Abstract [en]

    Emergency management systems deal with the dynamic processing of data, where response teams must continuously adapt to the changing conditions at the scene of the emergency. Response teams must make critical decisions in highly demanding situations using large volumes of sensor data. Mobile devices have limited processing, storage, and battery resources; therefore, the sensed data from the scene of the emergency must be transmitted and processed quickly using the best available networks and clouds. Mobile cloud computing (MCC) is expected to play a critical role in the computation and storage offloading of sensor data to the best available clouds. However, applications running on mobile devices using clouds and heterogeneous access networks such as Wi-Fi and 3G are prone to unpredictable cloud workloads, network congestion, and handoffs. This article presents M2C2, a system for mobility management in MCC, that supports mechanisms for multihoming, cloud and network probing, and cloud and network selection. Through a prototype implementation and experiments, the authors show that M2C2 supports mobility efficiently in MCC scenarios such as emergency management.

  • 16.
    Mitra, Karan
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Saguna, Saguna
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Åhlund, Christer
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Granlund, Daniel
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    M2C2: A Mobility Management System for Mobile Cloud Computing2015In: IEEE Wireless Communications and Networking Conference, 2015: WCNC 2015, 9-12 Mars 2015, New Orleans, LA, Piscataway, NJ: IEEE Communications Society, 2015, p. 1608-1613Conference paper (Refereed)
    Abstract [en]

    Mobile devices have become an integral part of our daily lives. Applications running on these devices may avail storage and compute resources from the cloud(s). Further, a mobile device may also connect to heterogeneous access networks (HANs) such as WiFi and LTE to provide ubiquitous network connectivity to mobile applications. These devices have limited resources (compute, storage and battery) that may lead to servicedisruptions. In this context, mobile cloud computing enables offloading of computing and storage to the cloud. However, applications running on mobile devices using clouds and HANs are prone to unpredictable cloud workloads, network congestion and handoffs. To run these applications efficiently the mobile device requires the best possible cloud and network resources while roaming in HANs. This paper proposes, develops and validates a novel system called M2C2 which supports mechanismsfor: i.) multihoming, ii.) cloud and network probing, and iii.) cloudand network selection. We built a prototype system and performed extensive experimentation to validate our proposed M2C2. Our results analysis shows that the proposed system supports mobility efficiently in mobile cloud computing.

  • 17.
    Mitra, Karan
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Saguna, Saguna
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Åhlund, Christer
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Ranjan, Rajiv
    Newcastle University.
    ALPINE: A Bayesian System For Cloud Performance Diagnosis And Prediction2017In: 2017 IEEE International Conference on Services Computing (SCC), Piscataway, NJ: IEEE, 2017, p. 281-288, article id 8034996Conference paper (Refereed)
    Abstract [en]

    Cloud performance diagnosis and prediction is a challenging problem due to the stochastic nature of the cloud systems. Cloud performance is affected by a large set of factors such as virtual machine types, regions, workloads, wide area network delay and bandwidth. Therefore, necessitating the determination of complex relationships between these factors. The current research in this area does not address the challenge of modeling the uncertain and complex relationships between these factors. Further, the challenge of cloud performance prediction under uncertainty has not garnered sufficient attention. This paper proposes, develops and validates ALPINE, a Bayesian system for cloud performance diagnosis and prediction. ALPINE incorporates Bayesian networks to model uncertain and complex relationships between several factors mentioned above. It handles missing, scarce and sparse data to diagnose and predict stochastic cloud performance efficiently. We validate our proposed system using extensive real data and show that it predicts cloud performance with high accuracy of 91.93%.

  • 18.
    Mitra, Karan
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zaslavsky, Arkady
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Åhlund, Christer
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A probabilistic context-aware approach for quality of experience measurement in pervasive systems2011In: Proceedings of the 26th Annual ACM Symposium on Applied Computing 2011: Taichung, Taiwan, March 21 - 24, 2011, New York: ACM Digital Library, 2011, p. 419-424Conference paper (Refereed)
    Abstract [en]

    In this paper, we develop a novel context-aware approach for quality of experience (QoE) modeling, reasoning and inferencing in mobile and pervasive computing environments. The proposed model is based upon a state-space approach and Bayesian networks for QoE modeling and reasoning. We further extend this context model to incorporate influence diagrams for efficient QoE inferencing. Our approach accommodates user, device and quality of service (QoS) related context parameters to determine the overall QoE of the user. This helps in user-related media, network and device adaptation. We perform experimentation to validate the proposed approach and the results verify its modeling and inferencing capabilities.

  • 19.
    Mitra, Karan
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zaslavsky, Arkady
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Åhlund, Christer
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Context-Aware QoE Modelling, Measurement and Prediction in Mobile Computing Systems2015In: IEEE Transactions on Mobile Computing, ISSN 1536-1233, E-ISSN 1558-0660, Vol. 14, no 5, p. 920-936Article in journal (Refereed)
    Abstract [en]

    Quality of Experience (QoE) as an aggregate of Quality of Service (QoS) and human user-related metrics will be the key success factor for current and future mobile computing systems. QoE measurement and prediction are complex tasks as they may involve a large parameter space such as location, delay, jitter, packet loss and user satisfaction just to name a few. These tasks necessitate the development of practical context-aware QoE models that efficiently determine relationships between user context and QoE parameters. In this paper, we propose, develop and validate a novel decision-theoretic approach called CaQoEM for QoE modelling, measurement and prediction. We address the challenge of QoE measurement and prediction where each QoE parameter can be measured on a different scale and may involve different units of measurement. CaQoEM is context-aware and uses Bayesian networks and utility theory to measure and predict users' QoE under uncertainty. We validate CaQoEM using extensive experimentation, user studies and simulations. The results soundly demonstrate that CaQoEM correctly measures range-defined QoE using a bipolar scale. For QoE prediction, an overall accuracy of 98.93\% was achieved using 10-fold cross validation in multiple diverse network conditions such as vertical handoffs, wireless signal fading and wireless network congestion.

  • 20.
    Mitra, Karan
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zaslavsky, Arkady
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Åhlund, Christer
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Dynamic Bayesian networks for sequential quality of experience modelling and measurement2011In: Smart Spaces and Next Generation Wired/Wireless Networking: 11th International Conference, NEW2AN 2011, and 4th Conference on Smart Spaces, ruSMART 2011, St. Petersburg, Russia, August 22-25, 2011. Proceedings / [ed] Sergey Balandin; Yevgeni Koucheryavy; Honglin Hu, Encyclopedia of Global Archaeology/Springer Verlag, 2011, p. 135-146Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel context-aware methodology for modelling and measuring user-perceived quality of experience (QoE) over time. In particular, we create a context-aware model for QoE modelling and measurement using dynamic Bayesian networks (DBN) and a context-aware state-space approach. The proposed model is then used to infer and determine users’ QoE in a sequential manner. We performed experimentation to validate the proposed model. The results prove thatit can efficiently model, reason and measure QoE of the users’.

  • 21.
    Mitra, Karan
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zaslavsky, Arkady
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Åhlund, Christer
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Measuring quality of experience in pervasive systems using probabilistic context-aware approach2012In: Mobile and Ubiquitous Systems: Computing, Networking, and Services: 7th International ICST Conference, MobiQuitous 2010, Sydney, Australia, December 6-9, 2010, Revised Selected Papers, Heidelberg: Encyclopedia of Global Archaeology/Springer Verlag, 2012, p. 330-331Conference paper (Refereed)
    Abstract [en]

    In this paper, we pioneer a context-aware approach for quality of experience (QoE) modeling, reasoning and inferencing in mobile and pervasive computing environments. The proposed model is based upon Context Spaces Theory (CST) and influence diagrams (IDs) to handle uncertain and hidden complex inter-dependencies between user-perceived and network level QoS and to calculate overall QoE of the users. This helps in user-related media, network and device adaptation, creating user-level SLAs and minimizing network churn. We perform experimentation to validate the proposed approach and the results verify its modeling and inferencing capabilities.

  • 22.
    Mitra, Karan
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zaslavsky, Arkady
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Åhlund, Christer
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    PRONET: proactive context-aware support for mobility in heterogenous access networks2010In: 2009 IEEE 34th Conference on Local Computer Networks: LCN 2009 ; Zurich, Switzerland, 20 - 23 October 2009, Piscataway, NJ: IEEE Communications Society, 2010, p. 675-676Conference paper (Refereed)
    Abstract [en]

    This paper presents a blueprint for proactive context-aware mobility support architecture for heterogeneous access networks called PROET. In particular, we leverage upon the principles of cognitive networking to support proactive contextawareness for user-centric application adaptation via quality-ofexperience (QoE) provisioning. Our proposed architecture is built upon Port-based Multi-homed Mobile IPv6 (PM-MIPv6) solution to support several applications via path diversity. In this paper our contributions are two-fold. Firstly, we identify and present gaps in our research domain related to mobility, QoE, cognitive networks and cross-layer design. We then present our architecture for providing seamless mobility in heterogeneous access networks. Currently, we are in the process of collecting results via our test bed and prototype implementation for 802.11g and HSDPA wireless networks

  • 23.
    Mitra, Karan
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Åhlund, Christer
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zaslavsky, Arkady
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A decision-theoretic approach for quality of experience measurement and prediction2011In: 2011 IEEE International Conference on Multimedia and Expo, (ICME 2011): Barcelona, Spain, 11 - 15 July 2011 / [ed] Irene Cheng, Piscataway, NJ: IEEE Communications Society, 2011Conference paper (Refereed)
    Abstract [en]

    This paper presents a pioneering context-aware approach for quality of experience (QoE) measurement and prediction. The proposed approach incorporates an intuitive context-aware framework and decision theory. It is capable of incorporating several QoE related classes and context information to correctly measure and predict the overall QoE on a single scale. Our approach can be used in measuring and predicting QoE in both lab and living-lab settings based on user, device and network related context parameters. The predicted QoE can be beneficial for network operators to minimize network churn and can help application developers to build smart user-centric applications. We perform extensive experimentation and the results validate our approach.

  • 24.
    Mitra, Karan
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Åhlund, Christer
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zaslavsky, Arkady
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Performance evaluation of a decision-theoretic approach for quality of experience measurement in mobile and pervasive computing scenarios2012In: 2012 IEEE Wireless Communications and Networking Conference (WCNC), Piscataway, NJ: IEEE Communications Society, 2012, p. 2418-2423Conference paper (Refereed)
    Abstract [en]

    Measuring and predicting users quality of experience (QoE) in dynamic network conditions is a challenging task. This paper presents results related to a decision-theoretic methodology incorporating Bayesian networks (BNs) and utility theory for quality of experience (QoE) measurement and prediction in mobile computing scenarios. In particular, we show how both generative and discriminative BNs can be used to measure and predict users QoE accurately for voice applications under several wireless network conditions such as wireless signal fading, vertical handoffs, wireless network congestion and normal hotspot traffic. Through extensive simulation studies and results analysis, we show that our proposed methodology can achieve an average accuracy of 98.70% using three different types of Bayesian network.

  • 25.
    Mitra, Karan
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Åhlund, Christer
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zaslavsky, Arkady
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    QoE estimation and prediction using hidden Markov models in heterogeneous access networks2012In: 2012 Australasian Telecommunication Networks and Applications Conference: (ATNAC 2012) : Brisbane 7 November - 9 November 2012, Piscataway, NJ: IEEE Communications Society, 2012Conference paper (Refereed)
    Abstract [en]

    Quality of Experience (QoE) based handoffs inheterogeneous access networks (HAN) necessitates accurate QoEestimation and prediction. The current approaches to QoE-awarehandoffs are limited. These approaches either lack the availabilityof underlying probing mechanism or lack the availability ofQoE estimation and prediction mechanism. In this paper, wepropose, develop and validate a novel method for QoE estimationand prediction using passive probing mechanisms. Ourmethod is based on hidden Markov models and multi-homedmobility management protocol. Using extensive simulations andexperimental studies, we show that our method achieves QoEestimation accuracy of 100% and prediction accuracy of 97% inHAN without using additional probe packets for QoE estimationand prediction.

  • 26. Nanda, Rohan
    et al.
    Saguna, Saguna
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Åhlund, Christer
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    BayesForSG: A Bayesian Model for Forecasting Thermal Load in Smart Grids2016In: SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing, New York: ACM Digital Library, 2016, p. 2135-2141Conference paper (Refereed)
    Abstract [en]

    Forecasting the thermal load demand for residential buildings assists in optimizing energy production and developing demand response strategies in a smart grid system. However, the presence of a large number of factors such as outdoor temperature, district heating operational parameters, building characteristics and occupant behavior, make thermal load forecasting a challenging task. This paper presents an efficient model for thermal load forecast in buildings with different variations of heat load consumption across both winter and spring seasons using a Bayesian Network. The model has been validated by utilizing the realistic district heating data of three residential buildings from the district heating grid of the city of Skellefteå, Sweden over a period of four months. The results from our model shows that the current heat load consumption and outdoor temperature forecast have the most influence on the heat load forecast. Further, our model outperforms state-of-the-art methods for heat load forecasting by achieving a higher average accuracy of 77.97% by utilizing only 10% of the training data for a forecast horizon of 1 hour.

  • 27. Ngo, Khoi
    et al.
    Saguna, Saguna
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Åhlund, Christer
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    IReHMo: An efficient IoT-Based Remote health Monitoring System for Smart Regions2016In: 2015 17th International Conference on E-Health Networking, Application and Services, HealthCom 2015: Boston, United States, 13 - 17 October 2015, Piscataway, NJ: IEEE Communications Society, 2016, p. 563-568, article id 7454565Conference paper (Refereed)
    Abstract [en]

    The ageing population worldwide is constantly rising, both in urban and regional areas. There is a need for IoT-based remote health monitoring systems that take care of the health of elderly people without compromising their convenience and preference of staying at home. However, such systems may generate large amounts of data. The key research challenge addressed in this paper is to efficiently transmit healthcare data within the limit of the existing network infrastructure, especially in remote areas. In this paper, we identified the key network requirements of a typical remote health monitoring system in terms of real-time event update, bandwidth requirements and data generation. Furthermore, we studied the network communication protocols such as CoAP, MQTT and HTTP to understand the needs of such a system, in particular the bandwidth requirements and the volume of generated data. Subsequently, we have proposed IReHMo - an IoT-based remote health monitoring architecture that efficiently delivers healthcare data to the servers. The CoAP-based IReHMo implementation helps to reduce up to 90% volume of generated data for a single sensor event and up to 56% required bandwidth for a healthcare scenario. Finally, we conducted a scalability analysis to determine the feasibility of deploying IReHMo in large numbers in regions of north Sweden.

  • 28.
    Noor, Ayman
    et al.
    Newcastle University, Newcastle upon Tyne, UK. Taibah University, Madinah, Saudi Arabia.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Solaiman, Ellis
    Newcastle University, Newcastle upon Tyne, UK.
    Souza, Arthur
    Federal University of Rio Grande do Norte, Natal, Brazil.
    Jha, Devki Nandan
    Newcastle University, Newcastle upon Tyne, UK.
    Demirbaga, Umit
    Newcastle University, Newcastle upon Tyne, UK. Bartin University, Bartin, Turkey.
    Jayaraman, Prem Prakash
    Swinburne University of Technology, Melbourne, Australia.
    Cacho, Nelio
    Federal University of Rio Grande do Norte, Natal, Brazil.
    Ranjan, Rajiv
    Newcastle University, Newcastle upon Tyne, UK.
    Cyber-physical application monitoring across multiple clouds2019In: Computers & electrical engineering, ISSN 0045-7906, E-ISSN 1879-0755, Vol. 77, p. 314-324Article in journal (Refereed)
    Abstract [en]

    Cyber-physical systems (CPS) integrate cyber-infrastructure comprising computers and networks with physical processes. The cyber components monitor, control, and coordinate the physical processes typically via actuators. As CPS are characterized by reliability, availability, and performance, they are expected to have a tremendous impact not only on industrial systems but also in our daily lives. We have started to witness the emergence of cloud-based CPS. However, cloud systems are prone to stochastic conditions that may lead to quality of service degradation. In this paper, we propose M2CPA - a novel framework for multi-virtualization, and multi-cloud monitoring in cloud-based cyber-physical systems. M2CPA monitors the performance of application components running inside multiple virtualization platforms deployed on multiple clouds. M2CPA is validated through extensive experimental analysis using a real testbed comprising multiple public clouds and multi-virtualization technologies.

  • 29.
    Palm, Emanuel
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Saguna, Saguna
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Åhlund, Christer
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    A Bayesian system for cloud performance diagnosis and prediction2017In: Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom, Piscataway, NJ: IEEE Computer Society, 2017, p. 371-374, article id 7830706Conference paper (Refereed)
    Abstract [en]

    The stochastic nature of the cloud systems makes cloud quality of service (QoS) performance diagnosis and prediction a challenging task. A plethora of factors including virtual machine types, data centre regions, CPU types, time-of-the-day, and day-of-the-week contribute to the variability of the cloud QoS. The state-of-the-art methods for cloud performance diagnosis do not capture and model complex and uncertain inter-dependencies between these factors for efficient cloud QoS diagnosis and prediction. This paper presents ALPINE, a proof-of-concept system based on Bayesian networks. Using a real-life dataset, we demonstrate that ALPINE can be utilised for efficient cloud QoS diagnosis and prediction under stochastic cloud conditions

  • 30.
    Ranjan, Rajiv
    et al.
    CSIRO, Australia.
    Mitra, Karan
    Georgakopoulos, Dimitrios
    CSIRO, Australia.
    MediaWise Cloud Content Orchestrator2013In: Journal of Internet Services and Applications, ISSN 1867-4828, Vol. 4, no 2Article in journal (Refereed)
    Abstract [en]

    The growing ubiquity of Internet and cloud computing is having significant impact on media-related industries. These industries are using the Internet and cloud as a medium to enable creation, search, management and consumption of their content. Primarily, Content Delivery Networks (CDNs) are deployed for distributing multimedia content to the end-users. However, existing approaches to architecting CDNs have several limitations. Firstly, they do not harness multiple public cloud services for optimizing cost to performance ratio. Secondly, they lack support for dynamic and personalized content creation and distribution. Finally, they do not support end-to-end content lifecycle operations (production, deployment, consumption, personalization, and distribution).To overcome these limitations, in this paper, we propose, develop and validate a novel system called MediaWise Cloud Content Orchestrator (MCCO). MCCO expands the scope of existing CDNs with novel multi-cloud deployment. It enables content personalization and collaboration capabilities. Further, it facilitates do-it-yourself creation, search, management, and consumption of multimedia content. It inherits the pay-as-you-go models and elasticity that are offered by commercially available cloud services.In this paper, we discuss our vision, the challenges and the research objectives pertaining to MCCO for supporting next generation streamed, interactive, and collaborative high resolution multimedia content. We validated our system thorugh MCCO prototype implementation. Further, we conducted a set of experiments to demonstrate the functionality of MCCO. Finally, we compare the content orchestration features supported by MCCO to existing CDNs against the envisioned objectives of MCCO.

  • 31.
    Ranjan, Rajiv
    et al.
    CSIRO, Australia.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Saha, Suhit
    CSIRO, Australia.
    Georgakopoulos, Dimitrios
    CSIRO, Australia.
    Zaslavsky, Arkady
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Do-it-yourself content delivery network orchestrator2012In: Web Information Systems Engineering - WISE 2012: 13th International Conference, Paphos, Cyprus, November 28-30, 2012. Proceedings, Piscataway, NJ: Encyclopedia of Global Archaeology/Springer Verlag, 2012, p. 789-791Conference paper (Refereed)
    Abstract [en]

    Content delivery networks (CDNs) [1] provide fast and reliable content access to the end-users. CDN providers (e.g., Akamai [2]), either own the entire infrastructure or it is outsourced to a single Cloud provider. Content owners (e.g., clients and end-users) need to establish expensive contracts with third party ISPs or CDN providers. Hence, existing CDN services are out of reach for all but large enterprises. Current CDNs do not provide services that allow an end-user to create dynamic content such as combining music videos from an existing content source on the Internet. Finally, the content owners do not have low-level control over the orchestration operations such as, multiple Cloud provider selection and resource management for hosting content. Hence, the content owners are dependent on their CDN providers to perform these operations behind the scene.

  • 32.
    Ranjan, Rajiv
    et al.
    China University of Geosciences, Wuhan.
    Wang, Lizhe
    China University of Geosciences, Wuhan.
    Prakash Jayaraman, Prem
    Swinburne University of Technology, Melbourne.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Georgakopoulos, Dimitros
    Swinburne University of Technology, Melbourne.
    Special issue on Big Data and Cloud of Things (CoT)2017In: Software, practice & experience, ISSN 0038-0644, E-ISSN 1097-024X, Vol. 47, no 3, p. 345-347Article in journal (Refereed)
  • 33.
    Shah, Tejal
    et al.
    The University of New South Wales, Australia.
    Ali, Yavari
    School of Computer Science, RMIT university, Australia.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Saguna, Saguna
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Jayaraman, Prem Prakash
    School of Software and Electrical Engineering, Swinburne University of Technology, Australia.
    Rabhi, Fethi
    School of Computer Science and Engineering, The University of New South Wales, Sydney, Australia.
    Ranjan, Rajiv
    School of Computing, Newcastle University, Newcastle, UK.
    Remote health care cyber-physical system: quality of service (QoS) challenges and opportunities2016In: IET Cyber-Physical Systems: Theory & Applications, ISSN 2398-3396, Vol. 1, no 1, p. 40-48Article in journal (Refereed)
    Abstract [en]

    There is a growing emphasis to find alternative non-traditional ways to manage patients to ease the burden on health care services largely fuelled by a growing demand from sections of population that is ageing. In-home remote patient monitoring applications harnessing technological advancements in the area of Internet of things (IoT), semantic web, data analytics, and cloud computing have emerged as viable alternatives. However, such applications generate large amounts of real-time data in terms of volume, velocity, and variety thus making it a big data problem. Hence, the challenge is how to combine and analyse such data with historical patient data to obtain meaningful diagnoses suggestions within acceptable time frames (considering quality of service (QoS)). Despite the evolution of big data processing technologies (e.g. Hadoop) and scalable infrastructure (e.g. clouds), there remains a significant gap in the areas of heterogeneous data collection, real-time patient monitoring, and automated decision support (semantic reasoning) based on well-defined QoS constraints. In this study, the authors review the state-of-the-art in enabling QoS for remote health care applications. In particular, they investigate the QoS challenges required to meet the analysis and inferencing needs of such applications and to overcome the limitations of existing big data processing tools.

  • 34.
    Shah, Tejal
    et al.
    School of Computer Science and Engineering, University of New South Wales.
    Yavari, Ali
    Royal Melbourne Institute of Technology, Melbourne.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Saguna, Saguna
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Jayaraman, Prem Prakash
    RMIT University, Melbourne.
    Rabhi, Fethi
    University of New South Wales.
    Ranjan, Rajiv
    School of Computing Science, Newcastle University, Newcastle upon Tyne.
    Remote Healthcare Big Data Processing in Real-Time: Quality of Service Challenges and Opportunities2016In: Handbook on Big Data Technologies, Springer-Verlag GmbH , 2016Chapter in book (Refereed)
  • 35.
    Solaiman, Ellis
    et al.
    Newcastle University, UK.
    Ranjan, Rajiv
    Newcastle University, UK.
    Jayaraman, Prem Prakash
    Swinburne University of Technology, Australia.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Failure Monitoring in the Internet of Things Application Ecosystems2016In: IT Professional Magazine, ISSN 1520-9202, E-ISSN 1941-045X, Vol. 18, no 5, p. 8-11, article id 7579107Article in journal (Refereed)
    Abstract [en]

    For Internet of Things (IoT) application ecosystems to excel, end-to-end components including the cloud, network, and edge devices must be highly dependable and resilient. This dependability must be verifiable by continuously monitoring the constituent components for conformance to defined behavior in terms of functional and nonfunctional requirements. However, the authors contend that current techniques and frameworks for monitoring the performance of hardware and application resources in distributed systems are not capable of monitoring and detecting root causes of failure and performance degradation for entire end-to-end IoT ecosystems. Motivated by this finding, they discuss their vision of future research into developing formal approaches for monitoring end-to-end IoT ecosystems.

  • 36.
    Wang, Chen
    et al.
    CSIRO, Australia.
    Ranjan, Rajiv
    CSIRO, Australia.
    Zhou, Xiangmin
    CSIRO, Australia.
    Mitra, Karan
    Saha, Suhit
    CSIRO, Australia.
    Meng, Meng
    CSIRO, Australia.
    Georgakopopulos, Dimitrios
    CSIRO, Australia.
    Wang, Lizhe
    Center for Earth Observation & Digital Earth, Chinese Academy of Sciences.
    Thew, Peter
    CSIRO, Australia.
    A Cloud-based Collaborative Video Story Authoring and Sharing Platform2012In: CSI Journal of Computing, ISSN 2277-7091, Vol. 1, no 3, p. 66-76Article in journal (Refereed)
  • 37.
    Wang, Meisong
    et al.
    Research School of Computer Science, ANU, Canberra.
    Jayaraman, Prem Prakash
    Caulfield School of Information Technology, Monash University, Monash University, Melbourne, VIC, CSIRO, Canberra.
    Ranjan, Rajiv
    CSIRO Computational Informatics, Canberra.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zhang, Miranda
    CSIRO Computational Informatics, Canberra.
    Li, Eddie
    Research School of Computer Science, ANU, Canberra.
    Khan, Samee Ullah
    North Dakota State University, Fargo.
    Pathan, Risat Mahmud
    Chalmers University of Technology, Telstra Corporation, Melbourne.
    Georgeakopoulos, Dimitros
    Royal Melbourne Institute of Technology, Melbourne.
    An Overview of Cloud Based Content Delivery Networks: Research Dimensions and State-of-the-Art2015In: Transactions on Large-Scale Data- and Knowledge-Centered Systems XX: Special Issue on Advanced Techniques for Big Data Management, Berlin: Encyclopedia of Global Archaeology/Springer Verlag, 2015, p. 131-158Chapter in book (Refereed)
    Abstract [en]

    Content distribution networks (CDNs) using cloud resources such as storage and compute have started to emerge. Unlike traditional CDNs hosted on private data centers, cloud-based CDNs take advantage of the geographical availability and the pay-as-you-go model of cloud platforms. The Cloud-based CDNs (CCDNs) promote content-delivery-as-a-service cloud model. Though CDNs and CCDNs share similar functionalities, introduction of cloud impose additional challenges that have to be addressed for a successful CCDN deployment. Several papers have tried to address the issues and challenges around CDN with varying degree of success. However, to the best of our knowledge there is no clear articulation of issues and challenges problems within the context of cloud-based CDNs. Hence, this paper aims to identify the open challenges in cloud-based CDNs. In this regard, we present an overview of cloud-based CDN followed by a detailed discussion on open challenges and research dimensions. We present a state-of-the-art survey on current commercial and research/academic CCDNs. Finally, we present a comprehensive analysis of current CCDNs against the identified research dimensions

  • 38.
    Yang, Chao-Tung
    et al.
    Department of Computer Science, Tunghai University, Taichung City, Taiwan, ROC.
    Chen, Shuo-Tsung
    Artificial Intelligence Recognition Industry Service Research Center (AIR-IS Research Center), National Yunlin University of Science and Technology, Yunlin, Taiwan, ROC. College of Future, Bachelor Program in Interdisciplinary Studies, National Yunlin University of Science and Technology, Taiwan, ROC.
    Liu, Jung-Chun
    Department of Computer Science, Tunghai University, Taichung City, Taiwan, ROC.
    Yang, Yao-Yu
    Department of Computer Science, Tunghai University, Taichung City, Taiwan, ROC.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Ranjan, Rajiv
    School of Computer, China University of Geosciences, China. School of Computing Science, Newcastle University, United Kingdom.
    Implementation of a real-time network traffic monitoring service with network functions virtualization2019In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 93, p. 687-701Article in journal (Refereed)
    Abstract [en]

    The Network Functions Virtualization (NFV) extends the functionality provided by Software-Defined Networking (SDN). It is a virtualization technology that aims to replace the functionality provided by traditional networking hardware using software solutions. Thereby, enabling cheaper and efficient network deployment and management. The use of NFV and SDN is anticipated to enhance the performance of Infrastructure-as-a-Service (IaaS) clouds. However, due to the presence of a large number of network devices in IaaS clouds offering a plethora of networked services, there is need to develop a traffic monitoring system for the efficient network. This paper proposes and validates an extensible SDN and NFV-enabled network traffic monitoring system. Using extensive experiments, we show that the proposed system can closely match the performance of traditional networks at cheaper costs and by adding more flexibility to network management tasks.

  • 39.
    Zhalgasbekova, Aigerim
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zaslavsky, Arkady
    CSIRO, Melbourne.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Saguna, Saguna
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Jayaraman, Prem Prakash
    Swinburne University of Technology, Melbourne.
    Opportunistic Data Collection for IoT-Based Indoor Air Quality Monitoring2017In: Internet of Things, Smart Spaces, and Next Generation Networks and Systems: 17th International Conference, NEW2AN 2017, 10th Conference, ruSMART 2017, Third Workshop NsCC 2017, St. Petersburg, Russia, August 28–30, 2017, Proceedings / [ed] Galinina O., Andreev S., Balandin S., Koucheryavy Y., Cham: Springer, 2017, p. 53-65Conference paper (Refereed)
    Abstract [en]

    Opportunistic sensing advance methods of IoT data collection using the mobility of data mules, the proximity of transmitting sensor devices and cost efficiency to decide when, where, how and at what cost collect IoT data and deliver it to a sink. This paper proposes, develops, implements and evaluates the algorithm called CollMule which builds on and extends the 3D kNN approach to discover, negotiate, collect and deliver the sensed data in an energy- and cost-efficient manner. The developed CollMule software prototype uses Android platform to handle indoor air quality data from heterogeneous IoT devices. The CollMule evaluation is based on performing rate, power consumption and CPU usage of single algorithm cycle. The outcomes of these experiments prove the feasibility of CollMule use on mobile smart devices.

  • 40.
    Åhlund, Andreas
    et al.
    Umeå University, Department of Computer Science.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Johansson, Dan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Åhlund, Christer
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zaslavsky, Arkady
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Context-aware application mobility support in pervasive computing environments2009In: Proceedings of the 6th International Conference on Mobile Technology, Application & Systems : 2009, Nice, France, September 02 - 04, 2009, New York: ACM Digital Library, 2009Conference paper (Refereed)
    Abstract [en]

    In the future, application mobility can play a crucial role and prove to be an enabler for next generation distributed applications. Application mobility lets an application follow the users while they roam between networks using several devices. In order to achieve seamless application mobility, several issues need to be considered such as device heterogeneity, GUI-adaptation and application loss. Thus, in this paper our contributions are two-fold. Firstly, we present our novel architecture called the Application Mobility Manager (A2M) which provides seamless application mobility. The proposed architecture is context-aware and decentralized. Finally, we present a novel application called the Mobile YouTube Player which is capable of moving between heterogeneous devices and provide users with seamless video experience. We validate the proposed system through rigorous experimentation and user studies based on the real-world test bed and prototype implementation. The results clearly validate that the proposed system can support seamless application mobility.

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