Hi, I’m Pranav
Bhandari.

Developed a block storage system stressor using the C++ cache engine CacheLib to cache the data and libaio to transfer data to and from the backing store. Replayed diverse production workloads across different types of storage server that add up to more than 10 years of compute time to derive insights about sizing multiple cache tiers, selecting storage devices, and improving cost efficiency.

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A python library to analyze and sample block storage traces. Implemented random spatial sampling and augmented it to improve performance for block storage traces.

GitHub

PyMimircache is an open source cache simulation framework developed by Junchen Yang as part of the Emory SimBioSys Lab. I implemented miniature-simulations of a workload based on paper in FAST'15: Efficient MRC Construction with SHARDS by Carl A. Waldspurger, Nohhyun Park, Alexander Garthwaite, and Irfan Ahmad, CloudPhysics, Inc.

GitHub

Used a Convolutional Neural Network (CNN) to classify block traces. Each trace was converted into access plot images with block addresses on the y-axis and time on the x-axis. These images are later classified by the CNN.

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Using performance data from block trace replay, we used random forest regression to predict the optimal split of budget between DRAM and SSD for multi-tier caching.

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Developed an in-memory, FUSE filesystem to cache data from Kubernetes volumes. Allows users to customize replacement policy and selectively cache directories.

GitHub PDF

Being an inexpensive approach compared to other network model, wireless mesh networks(WMN) are a perfect tool to connect underprivileged areas to the global network. We analyze how we can improve connectivity of WMNs using limited multi-radio nodes which are expensive.

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Created a website to extract features from file system traces and display them. Trace files were stored in S3, processed in chunks using Lambda functions, and the metadata was stored in DynamoDB. The trace features were visualized using d3.js.