Subhajit Sidhanta


DIstributed Systems, Edge Computing, Artifiicial Intelligence, Data Science

Subhajit is actively seeking for collaborations with the industry and academia. The preferred mode of collaboration is through a joint funded proposal to begin with. Any other forms of joint projects, workshops, or exchange programs are also most welcome.

Subhajit is always in the lookout for enthusiastic and serious PhD, MTech, and BTech student to work in the areas of Artificial Intelligence (AI), Machine Learning (ML), Cloud Computing, and Internet of Things (IoT). Interested Ph.Dstudents are requested to apply formally through the IIT Bhilai online application system

ABOUT ME:

Subhajit is an Assistant Professor with the Department of Electrical Engineering and Computer Science at Indian Institute of Technology, Bhilai.

Before joinning IIT Bhilai Subhajit was an Assistant Professor with the Department of Computer Science and Engineering at Indian Institute of Technology, Jodhpur. Before this, Subhajit was a Postdoctoral Researcher working with the Distributed Systems research group at INESC-ID affiliated with University of Lisbon. He was working on a project funded by the European Research Consortium to develop theroretical model of consistency and isolation levels of distributed systems under the supervision of Professor Rodrigo Rodriguez. Subhajit completed his Phd in Computer Science from Louisiana State University in Ausgust 2016. His dissertation was tittled "The performance trade off and SLA awareness in cloud computing and distributed datastores".

Subhajit publishes in top conferences and journals in Computer Science. His papers titled ''OptEx: A Deadline-Aware Cost Optimization Model for Spark'' and ''OptCon: An Adaptable SLA-Aware Consistency Tuning Framework for Quorum-based Stores'' were accepted in the 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2016) 2016.

He also worked as a Research Intern for Robert Bosch GmbH, RTC, Bangalore, India in the summer of 2014, with the Text Analytics research team.

Subhajit also has 4 years of experience as software developer at IBM and PWC India, in java, databases, and internet technologies.

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News

Subhajit is the PC co-chair for IndoSys 2019

Subhajit is the PC member of IEEE Services 2019

Paper titled "Dyn-YCSB: Benchmarking Adaptive Frameworks" accepted in IEEE Service Computing (IEEE SCC) 2019: Authors: S. Sidhanta, W. Golab, S. Mukhopadhyay.

Paper titled "Deadline-Aware Cost Optimization for Spark" accepted in IEEE Transactions on Big Data Authors: S. Sidhanta, W. Golab, S. Mukhopadhyay.

Paper titled "Consistify: Preserving Correctness and SLA Under Weak Consistency" accepted in ICDCN 2019 Authors: S. Sidhanta, W. Golab, S. Mukhopadhyay.

Subhajit is the recepient of the NetApp Student Fellowship (NSF) Award for the term 2018-2019 for a project about data consistency in IoT platforms.

RESEARCH AREAS:

  • Edge/Fog Computing
  • Artificial Intelligence
  • Internet of Things
  • Distributed storage
  • Data consistency and isolation levels.
  • Client centric performance of distributed systems
  • Performance modelling/optimization of big data processing systems
  • Resource optimization in cloud and fog

AWARDS

  • NetApp Faculty Fellowship (2018-2019)
  • AWS Research Grant of $15000 (2016-2017)
  • AbroadToComsnets award to attend COMSNETS 2013
  • Economic Development Assistantship, Louisiana State University (2011-2016)

Journal Papers

  1. [2019]Subhajit Sidhanta, Wojciech Golab, Supratik Mukhopadhyay, Deadline Aware Cost Optimization Model for Spark, To appear in IEEE Transactions on BigData (TBD), pages 1–12.
  2. [2017]Subhajit Sidhanta, Wojciech Golab, Supratik Mukhopadhyay, Saikat Basu, Adaptable SLA-Aware Consistency Tuning Framework for Quorum-based Stores, IEEE Transactions on BigData (TBD), pages 1–14


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Conference Papers

  1. [2019] Subhajit Sidhanta, Supratik Mukhopadhyay, Wojciech Golab Dyn-YCSB: Benchmarking Adaptive Systems Accepted In 16th IEEE International Congress on Services Computing (SCC) 2019, Core-A , pages 1–2, Acceptance rate 17%.
  2. [2019] Subhajit Sidhanta, Wojciech Golab, Consistify: preserving correctness and SLA under weak consistency, 20th International Conference on Distributed Computing and Networking (ICDCN) 2019, Core-B, pages 1–10, Acceptance rate 25%.
  3. [2017] Subhajit Sidhanta, Supratik Mukhopadhyay, SynAdapt: Automated Synthesis of Adaptive Agents, Accepted in 14th IEEE International Congress on Services Computing (SCC) 2017,Core-A, pages 1–4, acceptance rate 17%.
  4. [2016] Subhajit Sidhanta, Wojciech Golab, Supratik Mukhopadhyay, Saikat Basu, OptCon: An Adaptable SLA-Aware Consistency Tuning Framework for Quorum-based Stores, 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) 2016, Core-A , pages 1–10, Acceptance rate 20%.
  5. [2016] Subhajit Sidhanta, Wojciech Golab, Supratik Mukhopadhyay, OptEx: A Deadline-Aware Cost Optimization Modelfor Spark, 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) 2016, Core-A, pages 1–10, Acceptance rate 20%.
  6. [2016] Subhajit Sidhanta, Supratik Mukhopadhyay, Infra: SLO Aware Elastic Auto Scaling in the Cloud for Cost Reduction, 5th IEEE International Congress on Big Data (BigData Congress) 2016, pages 1–8.
  7. [2014] Kaliappa Ravindran, Supratik Mukhopadhyay, Subhajit Sidhanta, Ali Sabbir, Managing Shared Contexts in Distributed Multi-player Game Systems, , 6th International Conference on COMmunication Systems & NETworkS (COMSNETS) 2014, pages 1-10, acceptance rate 17.62%.
  8. [2013] Subhajit Sidhanta, Supratik Mukhopadhyay, An Ad-hoc Distributed Execution Environment for Multi-Agent Systems, 5th International Conference on COMmunication Systems & NETworkS (COMSNETS) 2013, pages 1-10, acceptance rate 26.4%.
  9. [2012] Subhajit Sidhanta, Supratik Mukhopadhyay, Managing A Cloud for Multi-agent Systems on Ad-hoc Networks, 5th IEEE International Conference on Cloud Computing (CLOUD) 2012, Core-B, pages 996-997 , acceptance rate 18%.

RESEARCH STATEMENT

  • Because of the exponentially increasing complex nature and intensity of the operations being performed by the modern generation of software applications, traditional hosting environments like workstations, grids, and in some cases, even supercomputers are proving to be more and more insufficient. These circumstances are prompting organisations to look towards moving their operations base from traditional in-house servers and grids to public and private cloud platforms, like Amazon AWS and Microsoft Azure. On the other hand, we are witnessing a similar transformation in the frontier of data storage backends, where the traditional relational database (i.e., RDBMS) solutions are fast making way for distributed storage systems, like Apache Cassandra and Microsoft Azure. RDBMS based systems fail to handle the challenges in storing and querying exponentially increasing workload.

My work aims at: 1) providing organisations with smart tools that estimates the composition of the cloud infrastructure required for hosting the given application workload, and 2) providing automated tuning of the distributed storage systems for meeting the SLA, while maximizing the throughput. I plan to attack the problem using two approaches: 1) develop empirical performance models, and 2) use machine learning making predictions about optimal configuration required in running a given workload.