A Distributed Algorithm for Multi-Stage Computation Offloading

Mahn, Tobias; Becker, Dennis; Al-Shatri, Hussein; Klein, Anja

doi:10.1109/cloudnet.2018.8549534

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…(Aberer et al 2006;Aygalinc et al 2016;Becker et al 2019;Caporuscio et al 2010;Escoffier et al 2014;Handte et al 2012). Most of these approaches concentrate on either networked embedded devices (Becker et al 2004(Becker et al , 2003Eisenhauer et al 2010;Kostelník et al 2011) or the Cloud (Brinkschulte et al 2019, Guinard et al 2010, Mahn et al 2018, Naber et al 2019, thus not fulfilling our first requirement, to provide a runtime environment for all device classes. This has changed only recently.…”

Section: Related Workmentioning

confidence: 99%

Elastic AI: system support for adaptive machine learning in pervasive computing systems

Cichiwskyj

Schmeißer

Qian

et al. 2021

CCF Trans. Pervasive Comp. Interact.

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Artificial intelligence (AI) is an important part of today’s pervasive computing systems. Still, there is no end-to-end system platform that allows to deploy, update, manage and execute AI models in pervasive systems. We propose such a system platform in this paper. Most importantly, we reuse concepts and techniques from twenty years of pervasive computing research on how to enable runtime adaptation and apply it to AI. This allows to specify adaptive AI models that are able to react to a multitude of dynamic changes, e.g. with respect to available devices, networking conditions, but also application requirements and sensor data sources. Developers can optimise their applications iteratively, starting with a generic setup and refining it step by step towards their specific pervasive computing scenario. To show the applicability of our platform, we apply it to two pervasive use cases and evaluate them, achieving up to four times faster inference and three times lower energy consumption compared to a classical AI deployment.

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Section: Related Workmentioning

confidence: 99%

Elastic AI: system support for adaptive machine learning in pervasive computing systems

Cichiwskyj

Schmeißer

Qian

et al. 2021

CCF Trans. Pervasive Comp. Interact.

View full text Add to dashboard Cite

show abstract

“…In this case, a continuous state space is defined and a deep deterministic policy gradient (DDPG) agent is adopted to handle the high-dimensional action space. Moreover, in [15], an energy minimization offloading problem with a time constraint is tackled. A game theory approach is used to decompose the problem into two sub-problems such that first the access point (AP) receives the offloading decisions from users, and then it optimizes the communication and computation resources (e.g., channel access time and computation power allocated to each user).…”

Section: B Related Workmentioning

confidence: 99%

Energy-Aware Multi-Server Mobile Edge Computing: A Deep Reinforcement Learning Approach

Naderializadeh¹,

Hashemi

2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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We investigate the problem of computation offloading in a mobile edge computing architecture, where multiple energy-constrained users compete to offload their computational tasks to multiple servers through a shared wireless medium. We propose a multi-agent deep reinforcement learning algorithm, where each server is equipped with an agent, observing the status of its associated users and selecting the best user for offloading at each step. We consider computation time (i.e., task completion time) and system lifetime as two key performance indicators, and we numerically demonstrate that our approach outperforms baseline algorithms in terms of the trade-off between computation time and system lifetime.

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Online Learning in Matching Games for Task Offloading in Multi-Access Edge Computing

Simon,

Mehler,

Klein

2023

ICC 2023 - IEEE International Conference on Communications

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A Distributed Algorithm for Multi-Stage Computation Offloading

Cited by 6 publications

References 10 publications

Elastic AI: system support for adaptive machine learning in pervasive computing systems

Elastic AI: system support for adaptive machine learning in pervasive computing systems

Energy-Aware Multi-Server Mobile Edge Computing: A Deep Reinforcement Learning Approach

Online Learning in Matching Games for Task Offloading in Multi-Access Edge Computing

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