Fog Node Self-Control Middleware: Enhancing context awareness towards autonomous decision making in Fog Colonies

Nikolopoulos, Vasileios; Νικολαϊδου, Μάρα; Voreakou, Maria; Aagnostopoulos, Dimosthenis

doi:10.1016/j.iot.2022.100549

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…[ 41 ] refers to the capability of self-deciding to offload tasks within a cooperative network of nodes in a vehicular computing continuum. Concepts such as fog colonies have appeared to describe the self-controlled, context-aware grouping of heterogeneous nodes by sharing contextual information and policy rulebooks in a decentralized approach [ 42 ].…”

Section: Terminology and Taxonomymentioning

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: Terminology and Taxonomymentioning

confidence: 99%

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

S-Julián

Lacalle

Vaño

et al. 2023

Sensors

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“…The fog computing architecture consists of three layers [6]: the Edge layer, the Fog layer, and the cloud layer (cf. Figure 1).…”

Section: Fog Computing: Architecture and Characteristicsmentioning

confidence: 99%

“…The importance of execution time metrics while building an optimal task scheduling solution is considered in [21,[31][32][33][34][35][36]38,47,51,68,72,73]. Response time is used for scheduling decisions in [3,6,11,19,20,[25][26][27]29]. A few papers [3,4,6,18,28,30,31] take into account completion time metric.…”

Section: Literature Mapping Optimization Criteriamentioning

confidence: 99%

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An Analysis of Methods and Metrics for Task Scheduling in Fog Computing

Misirli,

Casalicchio

2023

Future Internet

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The Internet of Things (IoT) uptake brought a paradigm shift in application deployment. Indeed, IoT applications are not centralized in cloud data centers, but the computation and storage are moved close to the consumers, creating a computing continuum between the edge of the network and the cloud. This paradigm shift is called fog computing, a concept introduced by Cisco in 2012. Scheduling applications in this decentralized, heterogeneous, and resource-constrained environment is challenging. The task scheduling problem in fog computing has been widely explored and addressed using many approaches, from traditional operational research to heuristics and machine learning. This paper aims to analyze the literature on task scheduling in fog computing published in the last five years to classify the criteria used for decision-making and the technique used to solve the task scheduling problem. We propose a taxonomy of task scheduling algorithms, and we identify the research gaps and challenges.

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