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Mitra, Karan, Assistant ProfessorORCID iD iconorcid.org/0000-0003-3489-7429
Biography [eng]

 Karan Mitra is an Assistant Professor at Luleå University of Technology, Sweden. He received his Dual-badge Ph.D. from Monash University, Australia and Luleå University of Technology in 2013. He received his MIT (MT) and a PGradDipDigComm from Monash University in 2008 and 2006, respectively. He received his BIS (Hons.) from Guru Gobind Singh Indraprastha University, Delhi, India in 2004. His research interests include quality of experience modelling and prediction, context-aware computing, cloud computing and mobile and pervasive computing systems. From January 2012 to December 2013 he worked as a researcher at CSIRO, Canberra, Australia. He is a member of the IEEE and ACM.

Publications (10 of 48) Show all publications
Noor, A., Jha, D. N., Mitra, K., Jayaraman, P. P., Souza, A., Ranjan, R. & Dustdar, S. (2019). A Framework for Monitoring Microservice-Oriented Cloud Applications in Heterogeneous Virtualization Environments. In: 2019 IEEE 12th International Conference on Cloud Computing (CLOUD): . Paper presented at 12th IEEE International Conference on Cloud Computing, CLOUD 2019,8-13 July 2019, Milan, Italy. IEEE
Open this publication in new window or tab >>A Framework for Monitoring Microservice-Oriented Cloud Applications in Heterogeneous Virtualization Environments
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2019 (English)In: 2019 IEEE 12th International Conference on Cloud Computing (CLOUD), IEEE, 2019Conference paper, Published paper (Refereed)
Abstract [en]

Microservices have emerged as a new approach for developing and deploying cloud applications that require higher levels of agility, scale, and reliability. To this end, a microservice-based cloud application architecture advocates decomposition of monolithic application components into independent software components called "microservices". As the independent microservices can be developed, deployed, and updated independently of each other, it leads to complex run-time performance monitoring and management challenges. To solve this problem, we propose a generic monitoring framework, Multi-microservices Multi-virtualization Multi-cloud (M3) that monitors the performance of microservices deployed across heterogeneous virtualization platforms in a multi-cloud environment. We validated the efficacy and efficiency of M3 using a Book-Shop application executing across AWS and Azure.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
microservices, monitoring, container, VM, cloud computing
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-76208 (URN)10.1109/CLOUD.2019.00035 (DOI)978-1-7281-2705-7 (ISBN)
Conference
12th IEEE International Conference on Cloud Computing, CLOUD 2019,8-13 July 2019, Milan, Italy
Available from: 2019-10-02 Created: 2019-10-02 Last updated: 2019-11-05Bibliographically approved
Minovski, D., Åhlund, C., Mitra, K. & Johansson, P. (2019). Analysis and Estimation of Video QoE in Wireless Cellular Networks using Machine Learning. In: 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX): . Paper presented at 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX), 5-7 June 2019,Berlin, Germany. IEEE
Open this publication in new window or tab >>Analysis and Estimation of Video QoE in Wireless Cellular Networks using Machine Learning
2019 (English)In: 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2019Conference paper, Published paper (Refereed)
Abstract [en]

The use of video streaming services are increasing in the cellular networks, inferring a need to monitor video quality to meet users' Quality of Experience (QoE). The so-called no-reference (NR) models for estimating video quality metrics mainly rely on packet-header and bitstream information. However, there are situations where the availability of such information is limited due to tighten security and encryption, which necessitates exploration of alternative parameters for conducting video QoE assessment. In this study we collect real-live in-smartphone measurements describing the radio link of the LTE connection while streaming reference videos in uplink. The radio measurements include metrics such as RSSI, RSRP, RSRQ, and CINR. We then use these radio metrics to train a Random Forrest machine learning model against calculated video quality metrics from the reference videos. The aim is to estimate the Mean Opinion Score (MOS), PSNR, Frame delay, Frame skips, and Blurriness. Our result show 94% classification accuracy, and 85% model accuracy (R 2 value) when predicting the MOS using regression. Correspondingly, we achieve 89%, 84%, 85%, and 82% classification accuracy when predicting PSNR, Frame delay, Frame Skips, and Blurriness respectively. Further, we achieve 81%, 77%, 79%, and 75% model accuracy (R 2 value) regarding the same parameters using regression.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
QoE, QoS, Video, MOS, PSNR, LTE
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing; Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-76619 (URN)10.1109/QoMEX.2019.8743281 (DOI)978-1-5386-8212-8 (ISBN)
Conference
2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX), 5-7 June 2019,Berlin, Germany
Available from: 2019-11-05 Created: 2019-11-05 Last updated: 2019-11-05Bibliographically approved
Alzubaidi, A., Solaiman, E., Patel, P. & Mitra, K. (2019). Blockchain-Based SLA Management in the Context of IoT. IT Professional Magazine, 21(4), 33-40
Open this publication in new window or tab >>Blockchain-Based SLA Management in the Context of IoT
2019 (English)In: IT Professional Magazine, ISSN 1520-9202, E-ISSN 1941-045X, Vol. 21, no 4, p. 33-40Article in journal (Refereed) Published
Abstract [en]

In pursuit of effective service level agreement (SLA) monitoring and enforcement in the context of Internet of Things (IoT) applications, this article regards SLA management as a distrusted process that should not be handled by a single authority. Here, we aim to justify our view on the matter and propose a conceptual blockchain-based framework to cope with some limitations associated with traditional SLA management approaches.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Internet of Things, Monitoring, Cloud computing, Ecosystems, Blockchain, Task analysis, Contracts
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-75585 (URN)10.1109/MITP.2019.2909216 (DOI)000476789400006 ()2-s2.0-85069776772 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-08-19 (johcin)

Available from: 2019-08-19 Created: 2019-08-19 Last updated: 2019-08-19Bibliographically approved
Li, J., Zhang, K., Yang, X., Wei, P., Wang, J., Mitra, K. & Ranjan, R. (2019). Category Preferred Canopy-K-means based Collaborative Filtering algorithm. Future generations computer systems, 93, 1046-1054
Open this publication in new window or tab >>Category Preferred Canopy-K-means based Collaborative Filtering algorithm
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2019 (English)In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 93, p. 1046-1054Article in journal (Refereed) Published
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%.

Place, publisher, year, edition, pages
Elsevier, 2019
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-68939 (URN)10.1016/j.future.2018.04.025 (DOI)000459365800085 ()2-s2.0-85049085827 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-03-27 (inah)

Available from: 2018-05-28 Created: 2018-05-28 Last updated: 2019-03-27Bibliographically approved
Nurgazy, M., Zaslavsky, A., Jayaraman, P., Kubler, S., Mitra, K. & Saguna, S. (2019). CAVisAP: Context-Aware Visualisation of Air Pollution with IoT Platforms. In: : . Paper presented at The International Conference on High Performance Computing & Simulation (HPCS 2019), July 15 – 19, 2019 Dublin, Ireland.
Open this publication in new window or tab >>CAVisAP: Context-Aware Visualisation of Air Pollution with IoT Platforms
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2019 (English)Conference paper (Refereed)
Keywords
air pollution, context-aware, data visualization, environmental monitoring, Internet of Things, location-based
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing; Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-76722 (URN)10.29007/9ld4 (DOI)
Conference
The International Conference on High Performance Computing & Simulation (HPCS 2019), July 15 – 19, 2019 Dublin, Ireland
Available from: 2019-11-15 Created: 2019-11-15 Last updated: 2019-11-15
Alhamazani, K., Ranjan, R., Jayaraman, P., Mitra, K., Liu, C., Rabhi, F., . . . Wang, L. (2019). Cross-Layer Multi-Cloud Real-Time Application QoS Monitoring and Benchmarking As-a-Service Framework (ed.). I E E E Transactions on Cloud Computing, 7(1), 48-61
Open this publication in new window or tab >>Cross-Layer Multi-Cloud Real-Time Application QoS Monitoring and Benchmarking As-a-Service Framework
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2019 (English)In: I E E E Transactions on Cloud Computing, ISSN 2168-7161, Vol. 7, no 1, p. 48-61Article in journal (Refereed) Published
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.  

Place, publisher, year, edition, pages
Los Alamitos: IEEE, 2019
Keywords
Cloud Computing, Benchmarking, Cloud Monitoring, Information technology - Systems engineering, Informationsteknik, Systemteknik
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-5655 (URN)10.1109/TCC.2015.2441715 (DOI)000460668300005 ()2-s2.0-85029735760 (Scopus ID)3d0653ee-adf9-462f-a6bc-f5dcec1c3dc3 (Local ID)3d0653ee-adf9-462f-a6bc-f5dcec1c3dc3 (Archive number)3d0653ee-adf9-462f-a6bc-f5dcec1c3dc3 (OAI)
Note

Validerad;2019;Nivå 2;2019-03-18 (oliekm)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2019-04-12Bibliographically approved
Noor, A., Mitra, K., Solaiman, E., Souza, A., Jha, D. N., Demirbaga, U., . . . Ranjan, R. (2019). Cyber-physical application monitoring across multiple clouds. Computers & electrical engineering, 77, 314-324
Open this publication in new window or tab >>Cyber-physical application monitoring across multiple clouds
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2019 (English)In: Computers & electrical engineering, ISSN 0045-7906, E-ISSN 1879-0755, Vol. 77, p. 314-324Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Cyber-physical system, Monitoring, Linear road benchmark, QoS, Virtualization, Cloud computing
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-75203 (URN)10.1016/j.compeleceng.2019.06.007 (DOI)000483629600024 ()2-s2.0-85067390602 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-07-03 (svasva)

Available from: 2019-07-03 Created: 2019-07-03 Last updated: 2019-09-24Bibliographically approved
Yang, C.-T., Chen, S.-T., Liu, J.-C., Yang, Y.-Y., Mitra, K. & Ranjan, R. (2019). Implementation of a real-time network traffic monitoring service with network functions virtualization. Future generations computer systems, 93, 687-701
Open this publication in new window or tab >>Implementation of a real-time network traffic monitoring service with network functions virtualization
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2019 (English)In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 93, p. 687-701Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Software-defined networking, Network functions virtualization, OpenFlow, Virtualized switch, Network traffic monitoring
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-71009 (URN)10.1016/j.future.2018.08.050 (DOI)000459365800054 ()2-s2.0-85056861664 (Scopus ID)
Note

Validerad;2018;Nivå 2;2018-12-03 (svasva)

Available from: 2018-09-27 Created: 2018-09-27 Last updated: 2019-03-27Bibliographically approved
Schürholz, D., Nurgazy, M., Zaslavsky, A., Jayaraman, P., Kubler, S., Mitra, K. & Saguna, S. (2019). MyAQI: Context-aware Outdoor Air Pollution Monitoring System. In: International Conference on the Internet of Things: . Paper presented at 9th International Conference on the Internet of Things (IoT 2019), October 22-25 2019, Bilbao, Spain..
Open this publication in new window or tab >>MyAQI: Context-aware Outdoor Air Pollution Monitoring System
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2019 (English)In: International Conference on the Internet of Things, 2019Conference paper, Published paper (Refereed)
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing; Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-76721 (URN)
Conference
9th International Conference on the Internet of Things (IoT 2019), October 22-25 2019, Bilbao, Spain.
Available from: 2019-11-15 Created: 2019-11-15 Last updated: 2019-11-15
Araujo, V., Mitra, K., Saguna, S. & Åhlund, C. (2019). Performance evaluation of FIWARE: A cloud-based IoT platform for smart cities. Journal of Parallel and Distributed Computing, 132, 250-261
Open this publication in new window or tab >>Performance evaluation of FIWARE: A cloud-based IoT platform for smart cities
2019 (English)In: Journal of Parallel and Distributed Computing, ISSN 0743-7315, E-ISSN 1096-0848, Vol. 132, p. 250-261Article in journal (Refereed) Published
Abstract [en]

As the Internet of Things (IoT) becomes a reality, millions of devices will be connected to IoT platforms in smart cities. These devices will cater to several areas within a smart city such as healthcare, logistics, and transportation. These devices are expected to generate significant amounts of data requests at high data rates, therefore, necessitating the performance benchmarking of IoT platforms to ascertain whether they can efficiently handle such devices. In this article, we present our results gathered from extensive performance evaluation of the cloud-based IoT platform, FIWARE. In particular, to study FIWARE’s performance, we developed a testbed and generated CoAP and MQTT data to emulate large-scale IoT deployments, crucial for future smart cities. We performed extensive tests and studied FIWARE’s performance regarding vertical and horizontal scalability. We present bottlenecks and limitations regarding FIWARE components and their cloud deployment. Finally, we discuss cost-efficient FIWARE deployment strategies that can be extremely beneficial to stakeholders aiming to deploy FIWARE as an IoT platform for smart cities.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Benchmarking, Cloud computing, Internet of things, Middleware, Quality of service, Smart cities
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-75060 (URN)10.1016/j.jpdc.2018.12.010 (DOI)000476580400021 ()2-s2.0-85066430784 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-07-08 (johcin)

Available from: 2019-06-27 Created: 2019-06-27 Last updated: 2019-10-03Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0003-3489-7429

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