EDGELAB

Group Leader

Subhajit Sidhanta, Assistant Professor, Industrial And Systems Engineering , IIT Kharagpur, Room 211, Extension 84756

Team members

PhD students

Imran Khan, Ph.D student, IIT Kharagpur (jointly supervised with Dr,  Sayak Roychowdhury)

Kolichala Rajsekhar, Ph.D student, CSE, IIT Bhilai (now Pstdoc at University of Innsbruck, Austria)

Gowry Sailaja, PhD student, DSAI, IIT Bhilai (jointly supervised with Dr. Soumajit Pramanik, IIT Bhilai)

Sivananda, Part-time PhD student, IIT Bhilai

MTech students

Swastik Dubey, MTech student, IIT Kharagpur

Ronit Wanare, Dual Degree Btech student, IIT Kharagpur

Vedant Tarole, Dual Degree Btech student, IIT Kharagpur

Shivam Dubey, MTech student, IIT Kharagpur (Aerospace)

BTech students

Vaibhab Arora, BTech, CSE, IIT Bhilai (now PhD student at IST, University of Lisbon, Portugal)

Vamsi Krishna, BTech, DSAI, IIT Bhilai

Chaitanya Sai, BTech, DSAI, IIT Bhilai

Hemanth Gaddey, BTech, EE, IIT Bhilai

Past Members:

• Shashwat Jaiswal, BTech Hons student, EECS, IIT Bhilai now doing Ph.D in UIUC

• Rohit Das, Mtech student, EECS, IIT Bhilai now working with RadiSys

• Saptarshi Mukherjee, BTech Hons student, EECS, IIT Bhilai now with Google India

• Sreechakra Muttareddygar, M.Tech Student, IIT Bhilai now with Delloite

• ASHUTOSH GARG, B.Tech Hons student, IIT Bhilai now with Amazon India

• Chitraksh Sadayat, BTech student, IIT Jodhpur

• Ajat Prabha, BTech student, IIT Jodhpur

• Vaibhabh Arora, PhD student, IST, University of Lisbon

• Ananya Hood, PhD student, MIT

• Chaitanya Bisht

• Sachin Kumar, Project Associate, IIT Bhilai

Collaborators:

• Yogesh Simmhan, Associate Professor, Indian Institute of Science

• Supratik Mukhopadhyay, Professor, Louisiana State University

• Yimin Zhu, Professor, Louisiana State University

• Sushanta Karmakar, Associate Professor, IIT Guwahati

Open Positions

Multiple  Internship positions are open in Distributed Systems, Federated learning, and Reinforcement learning.  Seeking applicants soon. Email subhajit AT iitbhilai DOT ac DOT in.

Ph.D positions available.  Please apply through formal channels.

Projects:

Reinforcement Learning for Performance Optimization in Edge/Fog/Cloud Computing

The explosive growth of IoT devices makes the cloud computing paradigm unsuitable for real-time applications. Instead, edge computing leverages relatively low responses. However, the limited resource capability of edge servers makes the edge computing paradigm more challenging. Further, the mobility of IoT devices and edge servers makes the problem even more difficult. In this paper, we outline the mobility-aware scenario with respect to the application of drone deployment in a disaster area. In such scenarios, we tackle different problems and challenges such as the optimal deployment of a swarm of drones to act as Network Access Points, optimal assignment of IoT data sources to Edge devices in an edge computing cluster, minimizing churn in an edge cluster comprising mobile edge devices, etc. To that end, we leverage reinforcement learning-based solutions.

Federated Learning: Performance and Accuracy Tradeoff:

Owing to the large number of clients, synchronous FL imposes considerable waiting time at the parameter server where the parameter server has to wait until all or sampled clients are ready. Further, this wait also introduces the staleness in the model as all the devices are not able to participate in FL.   In this scenario, Asynchronous Federated Learning (AFL) is leveraged, where the parameter server does not wait for the clients and performs fashioning as soon as it receives the updates. FedAvg can cope with the not non-independent and identically distributed (Non-IID) data to a certain degree, but a lot of research has indicated that a deterioration in the accuracy of FL is almost inevitable on Non-IID or heterogeneous data. We are developing a library/package for enabling developers and users to develop FL applications that can provide different degrees of accuracy and performance guarantees while working in an asynchronous manner with non-IID data.

Automated Tuning of Distributed NOSQL Databases

 Users and developers who employ distributed databases (NOSQL) for storing data, especially replicated databases, are burdened with the choice of a suitable client-centric tuning of configurations of the database for each storage operation. The above tuning is difficult to reason about as it requires deliberating about the tradeoff between different performance metrics and the consistency of the data.   The performance and consistency of a given operation depend on the client-centric database setting applied, as well as dynamic parameters such as the current workload and network condition. We are developing a reinforcement learning-based predictive tool chain, that can automate the choice of database setting under user-specified thresholds given in the service level agreement (SLA).

Power, temperature, and performance tradeoff in Edge Computing and IoT

Given the recent advances in IoT and cyberphysical systems, the computing tasks involved in the above monitoring activities are performed on edge devices such as Jetson Nano/Raspberry Pi onboard the drone. The temperature of the monitored environment can become quite high such that it may result in a violation of application-level correctness as well as substantial degradation of observed performance for computing tasks performed on the onboard devices. We propose to develop optimal path-planning algorithms which can operate under very high temperatures in the vicinity of bushfires and forest fires.  We are working towards development of models for safe temperature ranges for edge computing worked to run on exposed systems such as Jetson Nano/Raspberry Pi. For example, we are developing a library that can model the performance-temperature tradeoff, and predict the performance estimated for a particular workload run on the device under a given temperature. 

Edge Resource MANAGEMENT: MIGRATION AND AUTO SCALING