@inproceedings{Latzko2402:Accelerator,

title = {Accelerator-Aware Computation Offloading under Timing Constraints},

author = {Vincent {Latzko} and Christian Leonard {Vielhaus} and Mahshid {Mehrabi} and Frank H. P. {Fitzek}},

year  = {2024},

date = {2024-02-19},

urldate = {2024-02-01},

booktitle = {2024 International Conference on Computing, Networking and Communications 

(ICNC): Edge Computing, Cloud Computing and Big Data (ICNC'24 ECB)},

pages = {5},

address = {Big Island, USA},

abstract = {The rise of chiplets in personal and high performance computing is mirrored 

in System on Chip (SOC) in mobile devices. Both paradigms allow vendors and 

designers to integrate dedicated circuitry for accelerating computation. 

Implementations like cryptographic or vector engines are well known, and 

nowadays Machine Learning (ML) blocks are often included to accelerate Deep 

Neural Network (DNN) inference. The shift toward diverse device 

architectures, as exemplified by RISC-V, is poised to gain momentum. The 

widespread integration of accelerators in smartphones, tablets, SoCs, and 

dedicated server systems, is opening up exciting new innovations. In this 

short paper we present computation offloading for specific workloads in the 

framework of Multi-Access Edge Computing (MEC) and energy optimisation. We 

honour inter- task dependency through use of a Directed Acyclic Graph 

(DAG). Our system model with multiple mobile users, Device- to-Device (D2D) 

links between User Equipments (UEs), and edge servers enables computational 

and communication cooperation. The system's energy efficiency is 

significantly improved by introducing accelerators to the UEs and the MEC. 

We study the capabilities of the devices (accelerators) and propose an 

effective solution.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Latzko2402:Experiment_as_Code,

title = {Experiment as Code - An Automated Testbed for Cloud Scaling and Operations},

author = {Vincent {Latzko} and Zhenzhen {Jia} and Christian Leonard {Vielhaus} and Mahshid {Mehrabi} and Frank H. P. {Fitzek} },

doi = {10.1109/ACDSA59508.2024.10468018},

year  = {2024},

date = {2024-02-01},

urldate = {2024-02-01},

booktitle = {International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA)},

pages = {5},

address = {Mahe, Seychelles},

abstract = {Cloud Computing has been widely adopted in industry, administrations, as well as research. At the same time, development and testing of new technologies is troubled by inconsistent environments. Exchanging modules of a system during research becomes a major obstacle. As a result, baselines for comparisons are lacking, and evaluating new developments may lead to heavy time cost. Big industrial players typically circumvent this problem by relying on the scale of their datacenters, experiments, and problems, which makes them inherently interesting. 

We propose a softwarised testbed that abstracts away inevitable hardware differences. Its design closely follows the established philosophy to separate workloads and declare expressive interfaces between them. The result is a coherent testbed, consisting of interchangeable, but state of the art, components. It establishes Experiment-as-Code, where an experiment is declared to load generators programmatically. In sum, the testbed yields reproducible results. We verify networking, computational and interacting components by reproducing known results. We illustrate one use case of scaling under uncertainty the applicability for research. The whole system is hosted on-premise, with minimal hardware requirements, which enables especially academia to contribute. The low-effort in setup also allows handover to students and staff with the goal to ensure knowledge transfer.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Latz2306:Analysing,

title = {Analysing and Learning Low-Latency Network Coding Schemes},

author = {Vincent {Latzko} and Christian Leonard {Vielhaus} and Mahshid {Mehrabi} and Frank H. P. {Fitzek}},

doi = {10.1109/WiMob58348.2023.10187820},

year  = {2023},

date = {2023-07-26},

urldate = {2023-06-20},

booktitle = {International Conference on Wireless and Mobile Computing, 

Networking and Communications (WiMob)},

address = {Montreal, Canada},

abstract = {Forward Error Correction (FEC) has become an integral part of communication technology to address expected losses during transfer of data. Among 

various layer three block codes, Random Linear Network Coding (RLNC) has emerged as an adaptable, powerful approach. However, its most straight-forward implementation, Full Vector Coding (FVC), introduces too much delay for widespread adoption. Handcrafted schemes were introduced to optimise coding delay, while keeping resilience reasonably high. These works have resulted in tailRLNC and PACE. For the first time we analyse their respective behaviours in a fair and comparable manner, as non-recovered packets were statistically ignored previously. 

We then introduce an environment that uses a consistent, unbiased simulator and interface it with a Deep Reinforcement Learning (DRL) agent. This is the first time RLNC is joined with DRL. Our deep Q-network (DQN) based agent effectively uses an optimisation loop and utilises a customisable, expressive and extendable parametric loss function to learn a protocol. We demonstrate our agent recovers hand-tailored schemes and achieves state of the art.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Viel2307:Estimating,

title = {Estimating Link Capacity With Uncertainty Bounds in Cellular Networks},

author = {Christian Leonard {Vielhaus} and Osel {Lhamo} and Johannes V. S. {Busch} and Justus {Rischke} and Vincent {Latzko} and Sandra {Zimmermann} and Peter {Sossalla} and Daniel Fabian {K\"{u}lzer} and Frank H. P. {Fitzek}},

doi = {10.1109/BlackSeaCom58138.2023.10299770},

year  = {2023},

date = {2023-07-04},

urldate = {2023-07-04},

booktitle = {IEEE International Black Sea Conference on Communications and 

Networking (BlackSeaCom)},

pages = {117-122},

publisher = {IEEE},

address = {Istanbul, Turkiye},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Latzko2301:Offloading,

title = {Energy-Aware and Fair Multi-User Multi-Task Computation Offloading},

author = {Vincent {Latzko} and Osel {Lhamo} and Mahshid {Mehrabi} and Christian Leonard {Vielhaus} and Frank H. P. {Fitzek}},

doi = {10.1109/ICNC57223.2023.10074189},

year  = {2023},

date = {2023-02-01},

urldate = {2023-02-01},

booktitle = {2023 International Conference on Computing, Networking and Communications (ICNC 2023)},

address = {Honolulu, Hawaii},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Viel2210:Handover,

title = {Handover Predictions as an Enabler for Anticipatory Service Adaptations in Next-Generation Cellular Networks},

author = {Christian Leonard {Vielhaus} and Johannes V. S. {Busch} and Philipp {Geuer} and Alexandros {Palaios} and Justus {Rischke} and Daniel Fabian {K\"{u}lzer} and Vincent {Latzko} and Frank H. P. {Fitzek}},

doi = {https://doi.org/10.1145/3551660.3560913},

year  = {2022},

date = {2022-10-24},

urldate = {2022-10-01},

booktitle = {The 20th ACM International Symposium on Mobility Management and Wireless 

Access (ACM MobiWac 2022)},

address = {Montreal, Canada},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Mehr2020:Energyc,

title = {Energy-Aware Cooperative Offloading Framework for Inter-dependent and Delay-sensitive Tasks},

author = {Mahshid {Mehrabi} and Shiwei {Shen} and Vincent {Latzko} and Yuanfei {Wang} and Frank H. P. {Fitzek}},

year  = {2020},

date = {2020-12-07},

booktitle = {2020 IEEE Global Communications Conference: Selected Areas in Communications: Cloud \& Fog/Edge Computing, Networking and Storage (Globecom2020 SAC CCNS)},

address = {Taipei, Taiwan},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}