Reinforcing Cloud Environments via Index Policy for Bursty Workloads

Venkatraman, B.; Aloqaily, Moayad; Tunde-Onadele, Olufogorehan; Yang, Zhengyu; Reisslein, Martin

doi:10.1109/noms47738.2020.9110417

Cited by 7 publications

(2 citation statements)

References 15 publications

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“…Compared to single cluster migration, multi-cloud migration requires various network configurations which also rely on distinct use-cases. For instance, the bursty traffic related use-case in [28] allows to decide the triggering of migration process. There are still limitations to solve specific to dynamic management of resources while keeping them alive and facilitating an automated management of composed microservice during their whole life cycle, including their instantiation, migration and termination.…”

Section: Related Workmentioning

confidence: 99%

Live migration of containerized microservices between remote Kubernetes Clusters

Kaur,

Guillemin,

Sailhan

2023

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

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The recent adoption of cloud native technologies by telecommunication industry is accompanied by the incoming development of Network Functions that are containerized and packaged as light-weighted microservices. In order to efficiently orchestrate Cloud-Native Network Functions (CNFs), thorough migration strategies should be supported to place and migrate the CNFs. In this paper, we present a testbed illustrating the migration of pods between remote K8S cluster. As a use case, we consider the migration of a network function belonging to an opensource 5G core network (namely, Magma).

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Section: Related Workmentioning

confidence: 99%

Live migration of containerized microservices between remote Kubernetes Clusters

Kaur,

Guillemin,

Sailhan

2023

IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

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“…The authors propose a DQN based AQM algorithm in a single-agent environment, wherein the agent decides which packets to serve from the queue and which ones to drop. Other queue-based DRL usages can be seen in the paper of Balasubramanian et al [10], where the agents decide which request traffic instances are to be served first, and in the work of Bachl, Fabini and Zseby [11], where they are tasked with finding the optimal buffer sizes. DRL approaches in the domain of multi-path traffic engineering in general and multi-path TCP specifically are also popular.…”

Section: Related Workmentioning

confidence: 99%

Graph Convolutional Reinforcement Learning for Collaborative Queuing Agents

Fawaz¹,

Lesca²,

Quang³

et al. 2022

Preprint

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In this paper, we explore the use of multi-agent deep learning as well as learning to cooperate principles to meet stringent service level agreements, in terms of throughput and end-to-end delay, for a set of classified network flows. We consider agents built on top of a weighted fair queuing algorithm that continuously set weights for three flow groups: gold, silver, and bronze. We rely on a novel graph-convolution based, multi-agent reinforcement learning approach known as DGN. As benchmarks, we propose centralized and distributed deep Q-network approaches and evaluate their performances in different network, traffic, and routing scenarios, highlighting the effectiveness of our proposals and the importance of agent cooperation. We show that our DGN-based approach meets stringent throughput and delay requirements across all scenarios.

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Resource Management in Cloud Computing Using Deep Reinforcement Learning: A Survey

Feng

Liu

2023

Lecture Notes in Electrical Engineering

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Reinforcing Cloud Environments via Index Policy for Bursty Workloads

Cited by 7 publications

References 15 publications

Live migration of containerized microservices between remote Kubernetes Clusters

Live migration of containerized microservices between remote Kubernetes Clusters

Graph Convolutional Reinforcement Learning for Collaborative Queuing Agents

Resource Management in Cloud Computing Using Deep Reinforcement Learning: A Survey

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