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Digitale Infotage für Schüler*innen, 7.-11. Februar, 15-19 Uhr, Anmeldungen unter Bildinformationen anzeigen

Digitale Infotage für Schüler*innen, 7.-11. Februar, 15-19 Uhr, Anmeldungen unter

Foto: Universität Paderborn

Asif Hasnain, M.Sc.

 Asif Hasnain, M.Sc.


Mitglied - Wissenschaftlicher Mitarbeiter

+49 5251 60-1757
+49 5251 60-5377

Donnerstag: 12:30-13:30

Pohlweg 51
33098 Paderborn
Bachelor/Master Thesis and Project Groups

I am looking for bachelor/ master students to implement datacenter networked systems. If you are interested, drop me an email.

Completed Thesis

  • Joint coflow scheduling and routing using deep reinforcement learning (Master's thesis, 2021)
  • Dynamic Network Resource Allocation for Data-Center Networks: A Heuristic and its Evaluation (Bachelor's thesis, 2019)
  • Microservice-based Execution Environment for Service Compositions (Bachelor's thesis, 2017)

Completed Project Groups (PG)

  • AICON: Artificial Intelligence for Computer Networks (2020/2021)
  • Resource management in multi-application data centres (2018/2019)

You may visit our group page for more open thesis topics​ and student project groups​.


I am currently involved in the following project:

SFB 901 On-The-Fly Computing Subproject C4: On-The-Fly Compute Center II: Execution of Composed Services in Configurable Compute Centers

Research Area
  • Scheduling in Datacenter Networks
  • Networked systems
  • NFV & SDN
  • Reinforcement Learning

Liste im Research Information System öffnen


Learning Flow Scheduling

A. Hasnain, H. Karl, in: 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC), IEEE Computer Society, 2021

Datacenter applications have different resource requirements from network and developing flow scheduling heuristics for every workload is practically infeasible. In this paper, we show that deep reinforcement learning (RL) can be used to efficiently learn flow scheduling policies for different workloads without manual feature engineering. Specifically, we present LFS, which learns to optimize a high-level performance objective, e.g., maximize the number of flow admissions while meeting the deadlines. The LFS scheduler is trained through deep RL to learn a scheduling policy on continuous online flow arrivals. The evaluation results show that the trained LFS scheduler admits 1.05x more flows than the greedy flow scheduling heuristics under varying network load.

    Learning Coflow Admissions

    A. Hasnain, H. Karl, in: IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE Communications Society, 2021

    Data-parallel applications are developed using different data programming models, e.g., MapReduce, partition/aggregate. These models represent diverse resource requirements of application in a datacenter network, which can be represented by the coflow abstraction. The conventional method of creating hand-crafted coflow heuristics for admission or scheduling for different workloads is practically infeasible. In this paper, we propose a deep reinforcement learning (DRL)-based coflow admission scheme -- LCS -- that can learn an admission policy for a higher-level performance objective, i.e., maximize successful coflow admissions, without manual feature engineering. LCS is trained on a production trace, which has online coflow arrivals. The evaluation results show that LCS is able to learn a reasonable admission policy that admits more coflows than state-of-the-art Varys heuristic while meeting their deadlines.


    Coflow Scheduling with Performance Guarantees for Data Center Applications

    A. Hasnain, H. Karl, in: 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), IEEE Computer Society, 2020

    Data-parallel applications run on cluster of servers in a datacenter and their communication triggers correlated resource demand on multiple links that can be abstracted as coflow. They often desire predictable network performance, which can be passed to network via coflow abstraction for application-aware network scheduling. In this paper, we propose a heuristic and an optimization algorithm for predictable network performance such that they guarantee coflows completion within their deadlines. The algorithms also ensure high network utilization, i.e., it's work-conserving, and avoids starvation of coflows. We evaluate both algorithms via trace-driven simulation and show that they admit 1.1x more coflows than the Varys scheme while meeting their deadlines.

      Liste im Research Information System öffnen

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