Yogesh Simmhan
Assistant Professor
Yogesh's Mugshot Department of Computational and Data Sciences (CDS)
Indian Institute of Science (IISc), Bangalore
simmhan@cds.iisc.ac.in | skype:simmhan | @dreamlabin
Work:+91 80 2293 3421 | Cell: +91 9790 750137
CDS 206 | Nilgiri Marg, Mathikere, Bangalore 560012 | Map
Yogesh Simmhan is a tenured Assistant Professor at the Department of Computational and Data Sciences at the Indian Institute of Science, Bangalore. His research explores abstractions, algorithms and applications on distributed systems. These span Cloud and Edge Computing, Distributed Graph Processing Platforms and Elastic Stream Processing to support emerging Big Data and Internet of Things (IoT) applications. He has published over 90 peer-reviewed papers, and won the IEEE/ACM Supercomputing HPC Storage Challenge Award in 2008, the IEEE TCSC SCALE Challenge Award in 2012, and the Distinguished Paper award at EuroPar 2018. He is an Associate Editor in Chief of the Journal of Parallel and Distributed Systems (JPDC), served as an Associate Editor of IEEE Transactions on Cloud Computing, and was a member of the IEEE Future Directions Initiative on Big Data.
Yogesh has a Ph.D. in Computer Science from Indiana University, Bloomington, and was previously a Research Assistant Professor at the University of Southern California (USC), Los Angeles and a Postdoc at Microsoft Research, San Francisco. He is a Senior Member of IEEE and ACM.

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Research

My research is on distributed and scalable data platforms to support Big Data and Internet of Things (IoT) applications on novel computing infrastructure, such as Clouds and Edge devices. I lead the DREAM:Lab - Distributed Research on Emerging Applications and Machines - at CDS. Details of various research activities of the lab is available at the lab webpage

Teaching

A new course on DS256: Scalable Systems for Data Science (3:1) is being offered in the Jan semester starting from 2016 at the CDS department. The course covers platforms and tools required for developing algorithms, and programming and analyzing Big Data. A programming project is an essential part of the course, with students working over real-world, large datasets, and using Big Data platforms at scale.

The SE252: Introduction to Cloud Computing (3:1) is occasionally offered as an elective course in the Aug semester. The course covers topics on parallel and distributed computing; IaaS/PaaS/SaaS Clouds; Big Data processing patterns on Clouds; Runtime execution models on Clouds; and Performance evaluation of Cloud applications.

I co-teach DS221: Introduction to Scalable Systems (3:0), which is a new core course that blends various systems concepts for students new to data science and those without a formal Computer Science under-graduate degree.

Earlier, I taught the DS286: Data Structures and Programming (2:1) core course in the Aug semester, sometimes with Prof. Venkatesh Babu. I also co-taught the SE292: High Performance Computing (3:0) core course in the Aug 2014 semester, along with Prof. Govindarajan. Both of these have been discontinued, and topics absorbed into DS221.

Honors

Recent Refereed Publications (2017 – )

ORCID: 0000-0003-4140-7774 | Google Scholar | DBLP

  1. Cost-effective Sharing of Streaming Dataflows for IoT Applications, Chaturvedi, S., Tyagi, S. and Simmhan, Y., IEEE Transactions on Cloud Computing (TCC), 2019, pp. 1-17 (To appear)
  2. A Manifesto for Future Generation Cloud Computing: Research Directions for the Next Decade, Buyya, R., Srirama, S. N., Casale, G., Calheiros, R., Simmhan, Y., Varghese, B., Gelenbe, E., Javadi, B., Vaquero, L. M., Netto, M. A. S., Toosi, A. N., Rodriguez, M. A., Llorente, I. M., Vimercati, S. D. C. D., Samarati, P., Milojicic, D., Varela, C., Bahsoon, R., Assuncao, M. D. D., Rana, O., Zhou, W., Jin, H., Gentzsch, W., Zomaya, A. Y. and Shen, H., ACM Computing Surveys (CSUR), 2019, Vol. 51(5), pp. 1-38,
  3. Adaptive Partition Migration for Irregular Graph Algorithms on Elastic Resources, Dindokar, R. and Simmhan, Y., IEEE International Conference on Cloud Computing (CLOUD), 2019,
  4. A Partition-centric Distributed Algorithm for Identifying Euler Circuits in Large Graphs, Jaiswal, S. D. and Simmhan, Y., IEEE International Workshop on High-Performance Big Data, Deep Learning, and Cloud Computing (HPBDC), Co-located with IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2019, pp. 1-8,
  5. Dynamic Scaling of Video Analytics for Wide-area Tracking in Urban Spaces, Khochare, A., Ramachandra, S., Ramesh, S. and Simmhan, Y., IEEE International Scalable Computing Challenge (SCALE), 2019,
  6. ElfStore: A Resilient Data Storage Service for Federated Edge and Fog Resources, Monga, S. K., Sheshadri K, R and Simmhan, Y., IEEE International Conference on Web Services (ICWS), 2019, pp. 1-9,
  7. Characterizing Application Scheduling on Edge, Fog and Cloud Computing Resources, Varshney, P. and Simmhan, Y., Software: Practice and Experience, 2019, pp. 1-40,
  8. VIoLET: A Large-scale Virtual Environment for Internet of Things, Badiger, S., Baheti, S. and Simmhan, Y., International European Conference on Parallel and Distributed Computing (EuroPar), 2018, pp. 1-16, (Distinguished Paper Award)
  9. Adaptive Energy-aware Scheduling of Dynamic Event Analytics across Edge and Cloud Resources, Ghosh, R., Reddy, S. P. and Simmhan, Y., IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), 2018, pp. 1-11,
  10. Scalable Graph Processing Frameworks: A Taxonomy and Open Challenges, Heidari, S., Simmhan, Y., Calheiros, R. N. and Buyya, R., ACM Computing Surveys (CSUR), 2018, Vol. 51(3), pp. 1-53,
  11. Toward Reliable and Rapid Elasticity for Streaming Dataflows on Clouds, Shukla, A. and Simmhan, Y., IEEE International Conference on Distributed Computing Systems (ICDCS), 2018, pp. 1-11,
  12. Model-driven Scheduling for Distributed Stream Processing Systems, Shukla, A. and Simmhan, Y., Journal of Parallel and Distributed Computing (JPDC), 2018, Vol. 117, pp. 98-114,
  13. Encyclopedia of Big Data Technologies, Simmhan, Y., Chapter Big Data and Fog Computing, Sakr, S. & Zomaya, A. Y. (ed.), Springer, 2018,
  14. Towards a Data-driven IoT Software Architecture for Smart City Utilities, Simmhan, Y., Ravindra, P., Chaturvedi, S., Hegde, M. and Ballamajalu, R., Software: Practice and Experience, 2018, Vol. 48(7), pp. 1390-1416,
  15. AutoBoT: Resilient and Cost-effective Scheduling of a Bag of Tasks on Spot VMs, Varshney, P. and Simmhan, Y., IEEE Transactions on Parallel and Distributed Systems (TPDS), 2018, pp. 1-16,
  16. Collaborative Reuse of Streaming Dataflows in IoT Applications, Chaturvedi, S., Tyagi, S. and Simmhan, Y., IEEE International Conference on eScience, 2017, pp. 1-10,
  17. Characterization of Vertex-centric Breadth First Search for Lattice Graphs, Dindokar, R. and Simmhan, Y., IEEE International Workshop on Foundations in Big Data Computing (BigDF), Co-located with HiPC, 2017, pp. 1-8,
  18. Distributed Scheduling of Event Analytics across Edge and Cloud, Ghosh, R. and Simmhan, Y., ACM Transactions on Cyber-Physical Systems (TCPS), 2017,
  19. ARM Wrestling with Big Data: A Study of Commodity ARM64 Server for Big Data Workloads, Kalyanasundaram, J. and Simmhan, Y., IEEE International Conference on High Performance Computing, Data, and Analytics (HiPC), 2017, pp. 1-10 (Best Paper Finalist)
  20. Distributed Video Analytics across Edge and Cloud using ECHO, Khochare, A., Ravindra, P., Reddy, S. P. and Simmhan, Y., International Conference on Service-Oriented Computing (ICSOC) Demo, 2017, pp. 1-6,
  21. ECHO: An Adaptive Orchestration Platform for Hybrid Dataflows across Cloud and Edge, Ravindra, P., Khochare, A., Reddy, S. P., Sharma, S., Varshney, P. and Simmhan, Y., International Conference on Service-Oriented Computing (ICSOC), 2017, pp. 1-16,
  22. RIoTBench: An IoT Benchmark for Distributed Stream Processing Systems, Shukla, A., Chaturvedi, S. and Simmhan, Y., Concurrency and Computation: Practice and Experience, 2017, Vol. 29(21), pp. 1-22,
  23. IoT Analytics Across Edge and Cloud Platforms, Simmhan, Y., IEEE Internet of Things Newsletter, 2017,
  24. Demystifying Fog Computing: Characterizing Architectures, Applications and Abstractions, Varshney, P. and Simmhan, Y., IEEE International Conference on Fog and Edge Computing (ICFEC), 2017, pp. 1-10,

All Publications

Students and Staff

Full-time IISc Students

Staff

Lab Alumni

Former Staff and Interns at IISc

Former Students at USC

Prospective Students & Projects

Applied systems research requires hands-on programming skills to validate research ideas. Students interested in working with my lab should have demonstrated experience with programming and algorithms, preferably in Java, through online coding contest and Hackathons, such as CodeChef, SPOJ, HackerEarth, etc. Knowledge of Big Data platforms like Hadoop/Spark/Storm, Cloud fabrics like OpenStack and experience with Open Source projects will help too.

I welcome Ph.D. and M.Sc. research students at CDS interested in working with me. You are encouraged to view the lab's research activities, review relevant literature and our recent papers, and contact me with specific research areas and problems that you are passionate about.

Projects on state-of-the-art topics on Big Data and Cloud+Edge systems are available for M.Tech. by coursework students. These projects will place an emphasis on innovative research ideas as well as practical grounding through software prototyping and benchmarking on Cloud and distributed clusters. Students will be expected to publish a research paper as a project outcome. Sample projects topics are available from our lab's webpage. If you are interested, send me an email to schedule a meeting by Dec/Jan. Students must take the SE252:Introduction to Cloud Computing Course or SE256:Scalable Systems for Data Science Course as a pre-requisite.

Limited undergraduate summer internships are available to highly motivated students interested in pursuing research as part of their final year project. Students must be able to spend a semester in addition to summer (9-12 months) at IISc. Applications are open on a rolling basis starting the December before the internship term (e.g. Dec 2014 for May-Dec 2015).

Research associate and project staff positions are available in the area of Cloud Computing, Big Data platforms and Internet of Things. The position is for a minimum of 1 year, and offers a chance to work on research projects, collaborate with students, and publish research papers. Applicants should have a Masters' or Ph.D. degree in Computer Science; in exceptional cases, those with just a B.E. or B.Tech. degree in Computer Science will be considered. Demonstrated programming experience, through work on large open source projects, coding challenges (Googe SOCC, CodeChef) or hackathons, is required.

Students interested in applying to IISc should review the admission guidelines for IISc and CDS. IISc offers a world-class research environment, is consistently ranked the best research university in India, by Times Higher Education, ARWU and QS World University Rankings. CDS offers a unique 2-year M.Tech.(Computational and Data Science) program by coursework and project, in addition to research degrees of M.Tech.(Research) and Ph.D. Research students interested in working with me should select the Computer Systems Stream (CDS-CS) and prepare for the interview topics for the DREAM:Lab, as listed in the research admissions brochure. The admission process is highly competitive and requires a GATE exam score for those with a Bachelors' degree, besides onsite interviews.

Recent Professional Service

Journals
  • Associate Editor in Chief, Journal of Parallel and Distributed Computing (JPDC) (2019 - Present)
  • Editorial Board Member, Services Transactions on Internet of Things (STIOT) (2016-Present)
  • Associate Editor, IEEE Transactions on Cloud Computing (2013-2016)
  • Guest Co-Editor, Journal of Parallel and Distributed Computing (JPDC) - Special Issue on Scalable Systems for Big Data Management and Analytics (2013-14)
  • Guest Editor Concurrency and Computation: Practice and Experience Journal (CPE) - Special Issue on Cloud Computing for Data-driven Science and Engineering (2012-2015)
Conferences & Workshops
  • Program Committee Vice-Chair IEEE International Conference on Fog and Edge Computing (ICFEC) (2019)
  • Academic-Research Liaison Co-Chair, IEEE International Parallel and Distributed Processing Symposium (IPDPS) (2015-2016)
  • SCALE Challenge Co-Chair, IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) (2015)
  • Academic Liaison Co-Chair, IEEE International Conference on High Performance Computing (HiPC) (2014-2016)
  • General Co-Chair, Indian Symposium on Computer Systems (IndoSys) (2014-2016)
  • Workshop Vice-Chair IEEE International Parallel and Distributed Processing Symposium (IPDPS) (2014)
  • Workshop Co-Chair IEEE International Conference on High Performance Computing (HiPC) (2013)
  • General Co-Chair Workshop on Scientific Cloud Computing (ScienceCloud), Co-located with HPDC (2012-2013)
  • Doctoral Symposium Co-Chair IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) (2012-2013)
  • Program Committee Member in various years for IEEE International Parallel and Distributed Processing Symposium (IPDPS), IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), International Conference on Supercomputing, IEEE International Conference on Big Data (BigData), IEEE International Conference on eScience (eScience), IEEE International Conference on Web Services (ICWS), IEEE High Performance Computing Conference (HiPC), ACM Symposium on Applied Computing (SAC), IEEE Cloud Computing for Emerging Markets (CCEM), and ACM India Compute Conference (Compute) among others.
Misc
  • Member, IEEE Future Directions Initiative on Big Data (2015-)
  • Chair, Education & Research Task Force, Cloud Computing Innovation Council of India (CCICI) (2014-2015)
  • Invited Expert W3C Provenance Working Group (2011 - 2013)
  • Serving/Served on grant review panels for India's DeitY, US NSF, Austrian FWF and Brazilian FAPESP

Active Grants

Prior Grants

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