Aloe: An Elastic Auto-Scaled and Self-stabilized Orchestration Framework for IoT Applications

Chattopadhyay, Subhrendu; Chatterjee, Soumyajit; Nandi, Sukumar; Chakraborty, Sandip

doi:10.1109/infocom.2019.8737656

Cited by 8 publications

(4 citation statements)

References 15 publications

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“…Chattopadhyay et al [ 88 ] propose a self-scaling orchestration model for IoT applications called “Aloe”. This framework dynamically deploys lightweight controller instances close to IoT devices (which are resource-constrained) to ensure a high availability and low set-up time.…”

Section: Literature Review and Analysismentioning

confidence: 99%

Self-* Capabilities of Cloud-Edge Nodes: A Research Review

S-Julián

Lacalle

Vaño

et al. 2023

Sensors

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Most recent edge and fog computing architectures aim at pushing cloud-native traits at the edge of the network, reducing latency, power consumption, and network overhead, allowing operations to be performed close to data sources. To manage these architectures in an autonomous way, systems that materialize in specific computing nodes must deploy self-* capabilities minimizing human intervention across the continuum of computing equipment. Nowadays, a systematic classification of such capabilities is missing, as well as an analysis on how those can be implemented. For a system owner in a continuum deployment, there is not a main reference publication to consult to determine what capabilities do exist and which are the sources to rely on. In this article, a literature review is conducted to analyze the self-* capabilities needed to achieve a self-* equipped nature in truly autonomous systems. The article aims to shed light on a potential uniting taxonomy in this heterogeneous field. In addition, the results provided include conclusions on why those aspects are too heterogeneously tackled, depend hugely on specific cases, and shed light on why there is not a clear reference architecture to guide on the matter of which traits to equip the nodes with.

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Section: Literature Review and Analysismentioning

confidence: 99%

Self-* Capabilities of Cloud-Edge Nodes: A Research Review

S-Julián

Lacalle

Vaño

et al. 2023

Sensors

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“…Compared to the switch-level RD, request-level RD enables request dispatching to be performed at a fined-grained level. The benefits of request-level RD have also been demonstrated in [3], [9], [44]. Therefore, this paper will focus on requestlevel RD.…”

Section: B Request-level Request Dispatchingmentioning

confidence: 99%

Multi-Agent Deep Reinforcement Learning for Request Dispatching in Distributed-Controller Software-Defined Networking

Huang,

Chen,

2021

Preprint

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Recently, distributed controller architectures have been quickly gaining popularity in Software-Defined Networking (SDN). However, the use of distributed controllers introduces a new and important Request Dispatching (RD) problem with the goal for every SDN switch to properly dispatch their requests among all controllers so as to optimize network performance. This goal can be fulfilled by designing an RD policy to guide distribution of requests at each switch. In this paper, we propose a Multi-Agent Deep Reinforcement Learning (MA-DRL) approach to automatically design RD policies with high adaptability and performance. This is achieved through a new problem formulation in the form of a Multi-Agent Markov Decision Process (MA-MDP), a new adaptive RD policy design and a new MA-DRL algorithm called MA-PPO. Extensive simulation studies show that our MA-DRL technique can effectively train RD policies to significantly outperform man-made policies, modelbased policies, as well as RD policies learned via single-agent DRL algorithms.

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“…The algorithm stabilizes within O(d 2 n), where d is the network diameter and n is the number of nodes. Chattopadhyay et al [11] integrate an SDN control plane with the in-network processing infrastructure that can offload IoT services. They use a single centralized service deployment controller and lightweight SDN micro-controllers (µC).…”

Section: Related Workmentioning

confidence: 99%

A Self-stabilizing Control Plane for the Edge and Fog Ecosystems

Georgiou,

Pallis

et al. 2020

Preprint

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Fog Computing is now emerging as the dominating paradigm bridging the compute and connectivity gap between sensing devices (a.k.a. "things") and latency-sensitive services. However, as fog deployments scale by accumulating numerous devices interconnected over highly dynamic and volatile network fabrics, the need for self-configuration and self-healing in the presence of failures is more evident now than ever. Using the prevailing methodology of self-stabilization, we propose a fault-tolerant framework for distributed control planes that enables fog services to cope and recover from a very broad fault model. Specifically, our model considers network uncertainties, packet drops, node fail-stop failures and violations of the assumptions according to which the system was designed to operate, such as an arbitrary corruption of the system state. Our self-stabilizing algorithms guarantee automatic recovery within a constant number of communication rounds without the need for external (human) intervention. To showcase the framework's effectiveness, the correctness proof of the proposed self-stabilizing algorithmic process is accompanied by a comprehensive evaluation featuring an open and reproducible testbed utilizing realworld data from the intelligent transportation domain. Results show that our framework ensures a fog ecosystem recovery from faults in constant time, analytics are computed correctly, while the overhead to the system's control plane scales linearly towards the IoT load.

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Aloe: An Elastic Auto-Scaled and Self-stabilized Orchestration Framework for IoT Applications

Cited by 8 publications

References 15 publications

Self-* Capabilities of Cloud-Edge Nodes: A Research Review

Self-* Capabilities of Cloud-Edge Nodes: A Research Review

Multi-Agent Deep Reinforcement Learning for Request Dispatching in Distributed-Controller Software-Defined Networking

A Self-stabilizing Control Plane for the Edge and Fog Ecosystems

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