Chinmaya Dehury
Lecturer of Distributed Systems, Mobile & Cloud Lab
University of Tartu, Estonia
chinmaya.dehury(at)ut.ee,(at)ymail.com
http://blogchinmaya.blogspot.com
Address:
Institute of Computer Science
University of Tartu
Narva mnt 18 - 3040
Tartu 51009, Estonia
Telephone: +372 737 6419
IEEE Member: 96481829
ACM India Member: 8242857

About Me

Publication

Courses

Supervised Dissertations

Research Interest

Project

Professional Activities

Others



Edge computing resources

Call for Paper

Computer Communications, Elsevier, Special Issue on Ambient Intelligence in Communication, Computation and Networking for Future Internet of Things

Electronics, MDPI, Special Issue on Artificial Intelligence Technologies and Applications CFP PDF

Call for Book Chapter

Predictive Analytics in Cloud, Fog, and Edge Computing: Perspectives and Practices of Blockchain, IoT, and 5G
[FULL VERSION]

About Me


In Fall 2013, I joined Future Ubiquitous Networking (FUN) Lab at Chang Gung University as a Master student under Prof. Prasan Kumar Sahoo. The lab is now changed to Artificial intelligence and Big data Computing (ABC) Lab.
In Fall 2014, I upgrade the Master program to PhD program at the Department of Computer Science and Information Engineering at CGU. Now I have joined at Mobile & Cloud Lab, Institute of Computer Science, University of Tartu as a Researcher.

Education


Sept 2013 ~ Jan 2019 : Ph.D. in Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan
Thesis title: Scheduling and Resource Management Algorithms for Cloud Computing.
Supervisor: Prof. Prasan Kumar Sahoo
Aug 2010 ~ July 2013 : Master in Computer Application, Biju Patnaik University of Technology (BPUT), Odisha, India
Thesis title: Implementation of Radix sort in binary number system.
Supervisor: Dr. Rudramohan Tripathy
Aug 2006 ~ June 2009 : Bachelor in computer Application, Sambalpur University, Odisha , India

Honours & Awards


Publication

Courses

2021/22 Fall

- LTAT.06.015 - DevOps: Automating Software Delivery and Operations
- MTAT.03.280 - Mobile and Cloud Computing Seminar (I have handled few student topics.)

2020/21 Spring

- LTAT.06.008 - Cloud Computing (I delivered few lectures in this course. I am not the supervisor in-charge of this course.)
- MTAT.03.280 - Mobile and Cloud Computing Seminar (I have handled few student topics.)

Supervised Dissertations

Master Thesis Bachelor Thesis

Research Interest

Edge - Cloud Computing: My research mainly focused on Virtual Network Embedding (VNE) problem, where I have studied the optimization problem of resource utilization research issue. I focus on the application of different mathematical tools such as graph theory, Hidden Markov Model (HMM) to improve the embedding solution. Besides I focus on resource allocation, network-aware resource scheduling, resource-intensive job scheduling, virtual resource migration cost analysis, server load balancing, power management, fault tolerance analysis.
Edge Intelligence: Integration of fog and cloud computing. Computation offloading to Cloud, job migration between cloud and fog computing. Edge Intelligence, Cluster Edge Intelligence (CEI), Edge intelligence with Blockchain (#Blockchain-lite, #Blockchain@Edge)
Internet of Things: In conjunction with cloud computing, I am focusing on the combination of IoT world and the cloud environment. latency-aware job scheduling, bandwidth-aware job scheduling, IoT service management in cloud are some of the research issues I have studied.
Application of AI in Edge-Cloud computing: AI enabled Cloud resource management, resource demand prediction in cloud, , Pre-VNE based on workload prediction. pro-active AI enabled resource failure prediction in Fog and Cloud.
Smart Environment = Smart home + Smart building + smart city


Project

Intelligence discoverability and observability in edge infrastructure

It is predicted by Gartner that 50% of the enterprise-generated data will be created and processed in the edge infrastructure comprised of ever-increasing billions of edge devices. Instead of implementing the intelligence in the cloud environment giving the full responsibility to manage a large number of edge devices, a minimal and required intelligence is imposed on the edge devices, enabling the edge intelligence that works in a master-worker approach. The major problem with this approach is that an edge device relies mostly on the data collected by the onboard sensors and the built-in or cloud-instructed intelligence, resulting in little scope for cooperation and collaboration among peers and hence no device-to-device knowledge transfer.
Unlike the traditional approach to cluster the IoT devices based on the location and other meta information, a new research direction focuses on clustering the intelligence that the edge infrastructure is equipped with, also known as Clustered Edge Intelligence (CEI). Intelligence discoverability and observability are some of the primary challenges that need to be addressed to realize the full capacity of CEI. This Ph.D. project will focus on designing and developing dynamic intelligence clustering methodology with the discoverability and observability feature for large-scale edge infrastructure.

Professional Activities

Guest Editor

- Computer Communications - SI title: Ambient Intelligence in Communication, Computation and Networking for Future Internet of Things
- Electronics, MDPI, Special Issue on Artificial Intelligence Technologies and Applications   (pdf)

Journal/Conference Reviewer





Session Chair

PC Member

Technical Committee Member

- International Conference on Electronic Information Technology and Computer Science, China

Invited Talks/Workshops/Training programs

- Keynote Speaker, Guest of Honor, TEQIP Sponsored Training Program on “Fundamentals of Openstack and Containers in Cloud Technology”, Poojya Doddappa Appa (PDA) College of Engineering, Kalaburagi, Karnataka, 23 March 2021
- Resource person, TEQIP Sponsored Training Program on “Fundamentals of Openstack and Containers in Cloud Technology”, Poojya Doddappa Appa (PDA) College of Engineering, Kalaburagi, Karnataka, 23-25 March 2021

Members

- Memeber of Research Data Alliance (RDA)
- IEEE Member
- ACM India Member

Professional Badges


- FIRST TEACHING EXCELLENCE TRAINING FOR ACADEMIC STAFF 2022 (PDF)

Webinars

- Webinar on RADON Data Pipeline: Automating data movement in cloud (organised by me)
    (advt link) (Github repo) (Recorded video) (Extended demo video)

Important Links (complete list)