In our Serverless Computing research, our goal was to characterise and optimize the serverless computing platforms, making them adaptive and improving their performance.

In our last research in Performance and Available Computing Systems (PACS) Lab at York University, we were building accurate and tractable performance models for metric-based serverless computing platforms like Knative and Google Cloud Run that can estimate cost and performance of a given workload under different conditions accurately. Our paper titled “Performance Modeling of Metric-Based Autoscaling in Serverless Computing” is still being pending review, but our research has shown promising results, both in terms of accuracy and applicability.

You can check out our latest publications on our website.

In a previous research, we used Queuing Theory and Semi-Markov Processes to build a performance model for serverless computing platforms, with adequate fidelity and tractability to be used for modelling large-scale deployments. Our paper in this research called “Performance Modeling of Serverless Computing Platforms’’ was published in IEEE Transactions on Cloud Computing (TCC). In that paper, we were able to successfully model the performance metrics of AWS Lambda. We were also able to identify a key configuration in serverless computing platforms that can be used to optimize the overall performance of the system.

In a previous study we modelled the transient aspect of the key performance metrics of modern serverless computing platforms and showed its accuracy on AWS Lambda. This work was published in the Sixth Workshop on Serverless Computing (WoSC'20) as part of the ACM Middleware conference. This work was an extension to our steady-state performance modelling paper which was published in IEEE Transactions on Cloud Computing (TCC).

In another previous study in Dependable and Distributed Systems Lab (DDSL) at the University of Alberta, we used workload profiling and machine learning to build an adaptive function placement algorithm for serverless computing platforms. The resulting algorithm was able to achieve a better performance than the state of the art using similar hardware to perform the computing tasks.

My latest research in Computer Vision in Signal and Speech Processing Research Lab (SPRL) was on Multi-Target Object Tracking to produce high-quality object trajectories while maintaining low computational costs in order to be applicable for live implementation (e.g., autonomous vehicles). This research led to my master’s thesis and a journal paper titled “Multi-target tracking using CNN-based features: CNNMTT’’ published in Multimedia Tools and Applications. In this work we used the mid-layer output of custom-made Convolutional Neural Networks (CNN) as visual queues which combined with formal tracjectory models created smooth and stable tracking results. The resulting method was one of the top 4 methods in MOT Challenge Benchmark at the time.

In a previous research we used computer vision in Control of Multi-Vehicle Systems (CMVS) Lab in the field of robotics. Some of the projects completed between 2012-2017 included using computer vision for controlling Unmanned Ground Vehicles (UGVs) and Quadcopters and robust hand tracking using LBP features and Kalman Filters.