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Published in Unpublished work, 2017
Smart grid are supposed to provide an efficient means for energy trading using blockchain technology. They are implemented to address the problems related to security and privacy in transactions. Smart grids may aim to handle the pricing of energy, the computation of the amount of energy used or both in a secure and private manner. However there exist several implementations of smart grids that are decentralized using blockchain technology such as those using consortium blockchains, multi signatures and ethereum smart contracts. The concept of smart meters and charging pile sharing to improve load distribution on the grid are also discussed. In this paper we aim to identify the advantages and challenges that the various implementations of blockchain in smart grids present to the users and the energy market.
Recommended citation: Santosh Desai, Amit Dua, Ranadive Sahil, Neeraj Kumar, Ashok Kumar Das, Joel J. P. C. Rodrigues. A systematic survey of blockchain application in Smart Grid. http://sahilranadive.github.io/files/BlockChain.pdf
Published in 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC), 2020
Load balancing is an important part of a distributed computing environment which ensures that all devices or processors perform the same amount of work in an equal amount of time. Most load balancing algorithms assume similar processing power and workload for all the processors. However, now systems have become more complex and can have processors of different capabilities, workload, and configurations. In this paper, we propose an alternative algorithm for scheduling tasks. We configure the clusters dedicated to a particular type of task(real-time, dataintensive, etc.). Labels have been defined for each job to classify them into these categories. Then we modify the algorithm to introduce load balancing techniques using task migration.
Recommended citation: Dua, Amit & Ranadive, Sahil & Agarwal, Aditi & Kumar, Neeraj. (2020). Efficient Load balancing to serve Heterogeneous Requests in Clustered Systems using Kubernetes. 1-2. 10.1109/CCNC46108.2020.9045136. https://ieeexplore.ieee.org/document/9045136
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Undergraduate course, Birla Institute of Technology and Science, Pilani, 2019
Placed in the 95th percentile in the Neural Networks course resulting in the appointment as Lead Teaching Assistant. Coordinated with a group of 10 teaching assistants to organize assignments, projects, workshops and quizzes. Instructed workshops on Python, Keras and TensorFlow and mentored student groups in their term projects