Biography

Anshul Jindal is a Ph.D. student at Technical University of Munich supervised by Prof. Gerndt since December 2018. His research interests include cloud computing, specifically focussing on serverless computing for heterogeneous systems, edge computing, and AIOps. He has collaborated on various projects related to EdgeAI and AIOps with the Huawei Munich research center and BMW Munich during his Ph.D. He is working in the direction of building Function Delivery Network, a network of distributed heterogeneous platforms providing Function-Delivery-as-a-Service (FDaaS), delivering the function to the right target platform based on the required computational and data demand.

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.

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Interests
  • Cloud Computing
  • Serverless Computing
  • EdgeAI
  • AIOps
Education
  • Ph.D. in Computer Science (Cloud Computing)

    Technical University of Munich, Germany

  • MSc. in Informatics, 2018

    Technical University of Munich, Germany

  • B. Tech. in Computer Science and Engineering, 2014

    National Institute of Technology Hamirpur, India

Experience

 
 
 
 
 
Chair of Computer Architecture and Parallel Systems, TU Munich
Scientific Employee
Chair of Computer Architecture and Parallel Systems, TU Munich
Dec 2018 – Present 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.
 
 
 
 
 
Huawei Munich Research Centre
Ph.D. Research Internship
Huawei Munich Research Centre
Aug 2021 – Oct 2021 Munich

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.
 
 
 
 
 
Huawei Munich Research Centre
Industry Project Undertaken
Huawei Munich Research Centre
Mar 2020 – Oct 2020 Munich
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.
 
 
 
 
 
BMW
Industry Project Undertaken
BMW
May 2019 – Nov 2019 Munich
Developed a ML-based framework for automatically detecting and visualizing the anomalies in the BMW’s IT landscape Big data using Spark.
 
 
 
 
 
Chair of Computer Architecture and Parallel Systems, TU Munich
Working Student
Chair of Computer Architecture and Parallel Systems, TU Munich
Mar 2017 – Nov 2018 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.
 
 
 
 
 
Samsung R \& D Institute
Senior Software Engineer
Samsung R & D Institute
Jul 2014 – Aug 2016 Bangalore, India

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""
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Awarded with employee of the month award at Samsung Research Institute, Bangalore.
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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). MAFF: Self-adaptive Memory Optimization for Serverless Functions. 9th European Conference On Service-Oriented And Cloud Computing (ESOCC) 2022.

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(2022). Scalable Infrastructure for Workload Characterization of Cluster Traces. Proceedings of the 12th International Conference on Cloud Computing and Services Science - CLOSER.

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(2022). TppFaaS: Modeling Serverless Functions Invocations via Temporal Point Processes. IEEE Access.

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(2022). FedLess: Secure and Scalable Federated Learning Using Serverless Computing. IEEE International Conference on Big Data (Big Data).

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(2021). Courier: Delivering Serverless Functions Within Heterogeneous FaaS Deployments. Proceedings of the 14th IEEE/ACM International Conference on Utility and Cloud Computing (UCC).

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(2021). Estimating the Capacities of Function-as-a-Service Functions. Proceedings of the 14th IEEE/ACM International Conference on Utility and Cloud Computing Companion.

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(2021). DeepEdgeBench: Benchmarking Deep Neural Networks on Edge Devices. IEEE International Conference on Cloud Engineering (IC2E).

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(2021). Architecture-Specific Performance Optimization of Compute-Intensive FaaS Functions. IEEE 14th International Conference on Cloud Computing (CLOUD).

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(2021). Poster: Function Delivery Network: Extending Serverless to Heterogeneous Computing. IEEE 41st International Conference on Distributed Computing Systems (ICDCS).

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(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..

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(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..

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(2021). Function delivery network: Extending serverless computing for heterogeneous platforms. Software: Practice and Experience.

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(2021). Towards Federated Learning using FaaS Fabric. Proceedings of the 2020 Sixth International Workshop on Serverless Computing.

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(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).

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(2020). Scalable Infrastructure and Workflow for Anomaly Detection in an Automotive Industry. IEEE International Conference on Innovative Trends in Information Technology (ICITIIT).

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(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.

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(2020). Performance Evaluation of Container Runtimes. Proceedings of the 10th International Conference on Cloud Computing and Services Science - CLOSER.

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(2019). Multilayered autoscaling performance evaluation: Can virtual machines and containers co-scale?. International journal of applied mathematics and computer science.

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(2019). Performance Modeling for Cloud Microservice Applications. ICPE ‘19, Proceedings of the 2019 ACM/SPEC International Conference on Performance Engineering.

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(2019). Forecasting models for self-adaptive cloud applications: A comparative study. IEEE 12th International Conference on Self-Adaptive and Self-Organizing Systems (SASO).

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(2018). IaaS Reactive Autoscaling Performance Challenges. IEEE 11th International Conference on Cloud Computing (CLOUD).

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(2018). Autoscaling Performance Measurement Tool. ICPE ‘18, Companion of the 2018 ACM/SPEC International Conference on Performance Engineering.

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(2018). Multilayered Cloud Applications Autoscaling Performance Estimation. IEEE 7th International Symposium on Cloud and Service Computing (SC2).

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