AJindal
SRE
Argo AI
Biography
Anshul is currently working as SRE II at Argo AI, where he is responsible for building Terraform based modules automation for AWS and Azure Cloud for different teams. This also involves building Kubernetes platform using EKS and AKS with GitOps functionality using Flux and GitHub. He has also built CI/CD piplelines using GitHub actions with self-hosted runners. Furthermore, he is also responsible for building and maintaining monitoring stack based on Prometheus, Fluentibit, Promtail, Loki, and Thanos.
Before joining Argo, h did a Ph.D. at the Technical University of Munich, where he has built a Function Delivery Network based on Virtual Kubelet, a network of distributed multi-cloud and hybrid-cloud serverless platforms providing Function-Delivery-as-a-Service (FDaaS), scheduling the serverless functions to the right target platforms based on the required computational and data demand. He has collaborated on various projects related to EdgeAI and AIOps with the Huawei Munich research center and BMW Munich during his Ph.D.
From 2014 to 2016, he worked at Samsung Semiconductors, Bengaluru, India, as a Senior Software Engineer. There, he worked on the development of firmware for NVMe-based PCIe SSDs.
Interests
- DevOps
- SRE
- Cloud Infrastructure
- Serverless Computing
Education
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Ph.D. in Computer Science (Cloud Computing)
Technical University of Munich, Germany
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MSc. in Informatics, 2018
Technical University of Munich, Germany
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B. Tech. in Computer Science and Engineering, 2014
National Institute of Technology Hamirpur, India
Experience
Scientific Employee
Chair of Computer Architecture and Parallel Systems, TU Munich
Responsibilities include:
- Research in the area of extending serverless computing for heterogeneous compute/architecture devices.
- Federated learning using FaaS fabric.
- Anomalies detection, root cause analysis and predictive maintenance for Cloud Infrastructure.
- Performance Modeling of Microservices and FaaS-based functions.
Ph.D. Research Internship
Huawei Munich Research Centre
Responsibilities include:
- Edge AI with focus on DNN Split Computing and Early Exiting for edge-cloud continuum.
- Created automatic deployment infrastructure for EdgeAI with Kubernetes, KubeEdge and Sedna, using Ansible.
Industry Project Undertaken
Huawei Munich Research Centre
Developed multiple ML based algorithms for detecting anomalous hypervisors in the cloud infrastructure. One of the algorithm achieved a F1-Score of 90% on various datasets.
Industry Project Undertaken
BMW
Developed a ML-based framework for automatically detecting and visualizing the anomalies in the BMW’s IT landscape Big data using Spark.
Working Student
Chair of Computer Architecture and Parallel Systems, TU Munich
Responsibilities include:
- Research in the area of Multi-layer (containers and virtual machine level) autoscaling for the cloud-based applications.
- Developed web-based cloud computing lecture exercises automatic correction framework.
Senior Software Engineer
Samsung R & D Institute
Responsibilities include:
- Worked in the memory division, which involved the design and development of the firmware for PCIe based NVMe Solid State Drives.
- Involved in the development of Reservation and Virtualization feature (SR-IOV) for a multi-function/multi controller architecture based Solid State Drive (Samsung’s SSD, PM1725).
- Worked for two months on an onsite project at Memory Division, Samsung Headquarters, South Korea.
- Involved in development of an emulator of a SSD storage controller in C++.
Accomplishments
Selected as the Google Cloud Research Innovator for the year 2022.
Recevied a grant worth of €100,000 for developing project BEHAVE – Behavioral Modeling of Application Functions in Serverless Computing. The automatic management of resources in serverless computing known as Function-as-a-Service (Faas) facilitates application development by offloading resource management to the cloud platform. When extending FaaS to heterogeneous clusters (edge cloud continuum), challenges like communication latencies, function scheduling, and data access patterns grow further. This work focusses on mitigating these challenges by automatically characterizing the behavior of functions across these clusters. This project is part of software campus which is funded by the Federal Ministry of Education and Research (BMBF), with Huawei Munich Reserach Centre as industry partner.
Outstanding paper award
Awarded with outstanding paper award at IEEE Symposium on Cloud and Service Computing conference""
See certificate
Awarded with employee of the month award at Samsung Research Institute, Bangalore.
See certificate
Awarded with a spot award at Samsung Research Institute, Bangalore for his contribution towards the SSD emulator.
See certificate
Recent Posts
Projects
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Recent Publications
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(2022).
SLAM: SLO-Aware Memory Optimization for Serverless Applications.
2022 IEEE 15th International Conference on Cloud Computing (CLOUD).
(2022).
FaDO: FaaS Functions and Data Orchestrator for Multiple Serverless Edge-Cloud Clusters.
2022 IEEE 6th International Conference on Fog and Edge Computing (ICFEC).
(2022).
MAFF: Self-adaptive Memory Optimization for Serverless Functions.
9th European Conference On Service-Oriented And Cloud Computing (ESOCC) 2022.
(2022).
Scalable Infrastructure for Workload Characterization of Cluster Traces.
Proceedings of the 12th International Conference on Cloud Computing and Services Science - CLOSER.
(2022).
TppFaaS: Modeling Serverless Functions Invocations via Temporal Point Processes.
IEEE Access.
(2022).
FedLess: Secure and Scalable Federated Learning Using Serverless Computing.
IEEE International Conference on Big Data (Big Data).
(2021).
Courier: Delivering Serverless Functions Within Heterogeneous FaaS Deployments.
Proceedings of the 14th IEEE/ACM International Conference on Utility and Cloud Computing (UCC).
(2021).
Estimating the Capacities of Function-as-a-Service Functions.
Proceedings of the 14th IEEE/ACM International Conference on Utility and Cloud Computing Companion.
(2021).
DeepEdgeBench: Benchmarking Deep Neural Networks on Edge Devices.
IEEE International Conference on Cloud Engineering (IC2E).
(2021).
Architecture-Specific Performance Optimization of Compute-Intensive FaaS Functions.
IEEE 14th International Conference on Cloud Computing (CLOUD).
(2021).
Poster: Function Delivery Network: Extending Serverless to Heterogeneous Computing.
IEEE 41st International Conference on Distributed Computing Systems (ICDCS).
(2021).
Online Memory Leak Detection in the Cloud-Based Infrastructures.
Service-Oriented Computing – ICSOC 2020 Workshops. ICSOC 2020. Lecture Notes in Computer Science, vol 12632. Springer, Cham..
(2021).
From DevOps to NoOps: Is It Worth It?.
Cloud Computing and Services Science. CLOSER 2020. Communications in Computer and Information Science, vol 1399. Springer, Cham..
(2021).
Function delivery network: Extending serverless computing for heterogeneous platforms.
Software: Practice and Experience.
(2021).
Towards Federated Learning using FaaS Fabric.
Proceedings of the 2020 Sixth International Workshop on Serverless Computing.
(2020).
Windsurfing with APPA: Automating Computational Fluid Dynamics Simulations of Wind Flow using Cloud Computing.
28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP).
(2020).
Scalable Infrastructure and Workflow for Anomaly Detection in an Automotive Industry.
IEEE International Conference on Innovative Trends in Information Technology (ICITIIT).
(2020).
Microservices vs Serverless: A Performance Comparison on a Cloud-native Web Application.
Proceedings of the 10th International Conference on Cloud Computing and Services Science - CLOSER.
(2020).
Performance Evaluation of Container Runtimes.
Proceedings of the 10th International Conference on Cloud Computing and Services Science - CLOSER.
(2019).
Multilayered autoscaling performance evaluation: Can virtual machines and containers co-scale?.
International journal of applied mathematics and computer science.
(2019).
Performance Modeling for Cloud Microservice Applications.
ICPE ‘19, Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering.
(2019).
Forecasting models for self-adaptive cloud applications: A comparative study.
IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO).
(2018).
IaaS Reactive Autoscaling Performance Challenges.
IEEE 11th International Conference on Cloud Computing (CLOUD).
(2018).
Autoscaling Performance Measurement Tool.
ICPE ‘18, Companion of the 2018 ACM/SPEC International Conference on Performance Engineering.
(2018).
Multilayered Cloud Applications Autoscaling Performance Estimation.
IEEE 7th International Symposium on Cloud and Service Computing (SC2).