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Publications (10 of 41) Show all publications
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
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)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: 2019-07-03Bibliographically 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
Palm, E., Mitra, K., Saguna, S. & Åhlund, C. (2017). A Bayesian system for cloud performance diagnosis and prediction. In: Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom: . Paper presented at 8th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2016, Luxembourg, 12-15 December 2016 (pp. 371-374). Piscataway, NJ: IEEE Computer Society, Article ID 7830706.
Open this publication in new window or tab >>A Bayesian system for cloud performance diagnosis and prediction
2017 (English)In: 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, Published 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

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Computer Society, 2017
Series
International Conference on Cloud Computing Technology and Science, ISSN 2330-2194
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-62202 (URN)10.1109/CloudCom.2016.0065 (DOI)000398536300049 ()2-s2.0-85013025438 (Scopus ID)9781509014453 (ISBN)
Conference
8th IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2016, Luxembourg, 12-15 December 2016
Available from: 2017-02-28 Created: 2017-02-28 Last updated: 2019-04-03Bibliographically approved
Mitra, K., Saguna, S., Åhlund, C. & Ranjan, R. (2017). ALPINE: A Bayesian System For Cloud Performance Diagnosis And Prediction. In: 2017 IEEE International Conference on Services Computing (SCC): . Paper presented at 14th IEEE International Conference on Services Computing (SCC), Honolulu, HI, USA, 25-30 June 2017 (pp. 281-288). Piscataway, NJ: IEEE, Article ID 8034996.
Open this publication in new window or tab >>ALPINE: A Bayesian System For Cloud Performance Diagnosis And Prediction
2017 (English)In: 2017 IEEE International Conference on Services Computing (SCC), Piscataway, NJ: IEEE, 2017, p. 281-288, article id 8034996Conference paper, Published 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%.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2017
Keywords
Bayesian Network, Cloud Computing, Diagnosis, Quality of Service, Prediction
National Category
Computer and Information Sciences Computer Sciences Other Computer and Information Science Computer Systems
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-64458 (URN)10.1109/SCC.2017.43 (DOI)000425931600036 ()2-s2.0-85032332116 (Scopus ID)978-1-5386-2005-2 (ISBN)
Conference
14th IEEE International Conference on Services Computing (SCC), Honolulu, HI, USA, 25-30 June 2017
Available from: 2017-06-25 Created: 2017-06-25 Last updated: 2018-06-28Bibliographically approved
Belyakhina, T., Zaslavsky, A., Mitra, K., Saguna, S. & Jayaraman, P. P. (2017). DisCPAQ: Distributed Context Acquisition and Reasoning for Personalized Indoor Air Quality Monitoring in IoT-Based Systems. In: Galinina O., Andreev S., Balandin S., Koucheryavy Y. (Ed.), 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. Paper presented at 17th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2017, 10th Conference on Internet of Things and Smart Spaces, ruSMART 2017 and 3rd International Workshop on Nano-scale Computing and Communications, NsCC 2017, St. Petersburg, Russia, August 28–30, 2017 (pp. 75-86). Cham: Springer
Open this publication in new window or tab >>DisCPAQ: Distributed Context Acquisition and Reasoning for Personalized Indoor Air Quality Monitoring in IoT-Based Systems
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2017 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
Cham: Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10531
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-66097 (URN)10.1007/978-3-319-67380-6_7 (DOI)2-s2.0-85031425522 (Scopus ID)978-3-319-67379-0 (ISBN)978-3-319-67380-6 (ISBN)
Conference
17th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2017, 10th Conference on Internet of Things and Smart Spaces, ruSMART 2017 and 3rd International Workshop on Nano-scale Computing and Communications, NsCC 2017, St. Petersburg, Russia, August 28–30, 2017
Available from: 2017-10-12 Created: 2017-10-12 Last updated: 2019-04-03Bibliographically approved
Zhalgasbekova, A., Zaslavsky, A., Mitra, K., Saguna, S. & Jayaraman, P. P. (2017). Opportunistic Data Collection for IoT-Based Indoor Air Quality Monitoring. In: Galinina O., Andreev S., Balandin S., Koucheryavy Y. (Ed.), 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. Paper presented at 17th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2017, 10th Conference on Internet of Things and Smart Spaces, ruSMART 2017 and 3rd International Workshop on Nano-scale Computing and Communications, NsCC 2017, St. Petersburg, Russia, August 28–30, 2017 (pp. 53-65). Cham: Springer
Open this publication in new window or tab >>Opportunistic Data Collection for IoT-Based Indoor Air Quality Monitoring
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2017 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
Cham: Springer, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10531
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-66098 (URN)10.1007/978-3-319-67380-6_5 (DOI)2-s2.0-85031411967 (Scopus ID)978-3-319-67379-0 (ISBN)978-3-319-67380-6 (ISBN)
Conference
17th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and Systems, NEW2AN 2017, 10th Conference on Internet of Things and Smart Spaces, ruSMART 2017 and 3rd International Workshop on Nano-scale Computing and Communications, NsCC 2017, St. Petersburg, Russia, August 28–30, 2017
Available from: 2017-10-12 Created: 2017-10-12 Last updated: 2019-04-03Bibliographically approved
Ranjan, R., Wang, L., Prakash Jayaraman, P., Mitra, K. & Georgakopoulos, D. (2017). Special issue on Big Data and Cloud of Things (CoT). Software, practice & experience, 47(3), 345-347
Open this publication in new window or tab >>Special issue on Big Data and Cloud of Things (CoT)
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2017 (English)In: Software, practice & experience, ISSN 0038-0644, E-ISSN 1097-024X, Vol. 47, no 3, p. 345-347Article in journal, Editorial material (Refereed) Published
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-61505 (URN)10.1002/spe.2475 (DOI)000394957500001 ()2-s2.0-85011798146 (Scopus ID)
Available from: 2017-01-18 Created: 2017-01-18 Last updated: 2018-09-14Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-3489-7429

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