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Title
Abstract
Figures
Introduction
Related Computing Paradigms
Orchestration on Fog Computing Related Areas
Related Work
Research Methodology
Research Questions
Data Collection
Results
Conclusion
References
All Topics
Computer Science
Distributed Systems

Orchestration in Fog Computing: A Comprehensive Survey

Profile image of Leonardo Rebouças de CarvalhoLeonardo Rebouças de Carvalho

2023, ACM Computing Surveys

https://doi.org/10.1145/3486221
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34 pages

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Abstract

Fog computing is a paradigm that brings computational resources and services to the network edge in the vicinity of user devices, lowering latency and connecting with cloud computing resources. Unlike cloud computing, fog resources are based on constrained and heterogeneous nodes whose connectivity can be unstable. In this complex scenario, there is a need to define and implement orchestration processes to ensure that applications and services can be provided, considering the settled agreements. Although some publications have dealt with orchestration in fog computing, there are still some diverse definitions and functional intersection with other areas, such as resource management and monitoring. This article presents a systematic review of the literature with focus on orchestration in fog computing. A generic architecture of fog orchestration is presented, created from the consolidation of the analyzed proposals, bringing to light the essential functionalities addressed in the lit...

Figures (14)
In the literature review carried out for the develo ble to note that there was some confusion about fo ecess Edge Computing (MEC) paradigms. Some ynonymous [42, 123]. MEC can also be interpreted pment of this article, for example, it was pos- g computing, the edge computing and Multi- authors consider fog and edge computing as as a niche implementation of a fog environ- lent [154] that is focused on mobility [140]. This happens due to the similarities between these »mputational paradigms, such as proximity to edge devices and also the need for integration with oud computing to complement the computationa ary [157]. resources required by applications, if neces-
In the literature review carried out for the develo ble to note that there was some confusion about fo ecess Edge Computing (MEC) paradigms. Some ynonymous [42, 123]. MEC can also be interpreted pment of this article, for example, it was pos- g computing, the edge computing and Multi- authors consider fog and edge computing as as a niche implementation of a fog environ- lent [154] that is focused on mobility [140]. This happens due to the similarities between these »mputational paradigms, such as proximity to edge devices and also the need for integration with oud computing to complement the computationa ary [157]. resources required by applications, if neces-
3.1.2. Container Orchestration. In an environment such as fog computing, composed potentiall of resource-restricted devices [157], the use of containers, a lightweight and small footprint forr of virtualization [55], is referred to as being more appropriate than the use of virtual machines a execution environments. There are several container runtimes and Docker [29] is the most wel known one. Besides the runtime itself, a container orchestration solution, i.e. Kubernetes [67 Docker Swarm [30], Nomad [100] and Marathon on Mesos [77], can be used to manage containe life cycle, scale them up or down, do self-healing, migrate them and manage a heterogeneou infrastructure, abstracting the physical devices where they execute. Thus, container orchestratio: solutions can be adopted to implement and automate some of the essential characteristics of Fo Computing (Section 2): heterogeneity, geographic distribution, and scalability. Ti: Cee a ees: Dene” Glace ‘Bescon: Wee Sento nen Blwdk econ lex: Bheooenx 211 wcsinccdteedd ex lew Dace wax mbwousktnns
3.1.2. Container Orchestration. In an environment such as fog computing, composed potentiall of resource-restricted devices [157], the use of containers, a lightweight and small footprint forr of virtualization [55], is referred to as being more appropriate than the use of virtual machines a execution environments. There are several container runtimes and Docker [29] is the most wel known one. Besides the runtime itself, a container orchestration solution, i.e. Kubernetes [67 Docker Swarm [30], Nomad [100] and Marathon on Mesos [77], can be used to manage containe life cycle, scale them up or down, do self-healing, migrate them and manage a heterogeneou infrastructure, abstracting the physical devices where they execute. Thus, container orchestratio: solutions can be adopted to implement and automate some of the essential characteristics of Fo Computing (Section 2): heterogeneity, geographic distribution, and scalability. Ti: Cee a ees: Dene” Glace ‘Bescon: Wee Sento nen Blwdk econ lex: Bheooenx 211 wcsinccdteedd ex lew Dace wax mbwousktnns
Table 1. Related Work Comparison
Table 1. Related Work Comparison
Fig. 4. Steps of SLR methodology used [65] and [108]. Vv denotes comprehensive discussion about the item. 0 denotes partial or superficial discussion about the item.
Fig. 4. Steps of SLR methodology used [65] and [108]. Vv denotes comprehensive discussion about the item. 0 denotes partial or superficial discussion about the item.
5.5 Data Collection
5.5 Data Collection
Table 2. Most Frequent Goals Cited by Analyzed Papers pAUI], AMG TIP eee UIC COMVELECIICE OL INE V, Wo dll POR [190}. Knowing the goals of a specific proposal can help the researcher to better understand the techni- cal choices and architectural decisions made by the authors of that proposal. Analyzing the results summarized in Table 2 it is possible to understand why fog orchestration proposals have RM as the main functionality (48 of 50 analyzed proposals have implemented it, as can be seen in Section 5.5). RM as goal have appeared more frequently in years 2017 and 2018 [16, 118, 119, 148, 155, 158] and SLA/SLM, in 2019 and 2020 [20, 28, 47, 58, 59, 95]. This finding can suggest a changing in fog orchestration perspective, in the period of time comprehended by this analysis, from mainly managing fog infrastructure to run the services to a management and coordination function, man- aging mainly user relationship with the services provided. As the period of analysis is short, it is important to monitor the literature to see if this is an actual trend.
Table 2. Most Frequent Goals Cited by Analyzed Papers pAUI], AMG TIP eee UIC COMVELECIICE OL INE V, Wo dll POR [190}. Knowing the goals of a specific proposal can help the researcher to better understand the techni- cal choices and architectural decisions made by the authors of that proposal. Analyzing the results summarized in Table 2 it is possible to understand why fog orchestration proposals have RM as the main functionality (48 of 50 analyzed proposals have implemented it, as can be seen in Section 5.5). RM as goal have appeared more frequently in years 2017 and 2018 [16, 118, 119, 148, 155, 158] and SLA/SLM, in 2019 and 2020 [20, 28, 47, 58, 59, 95]. This finding can suggest a changing in fog orchestration perspective, in the period of time comprehended by this analysis, from mainly managing fog infrastructure to run the services to a management and coordination function, man- aging mainly user relationship with the services provided. As the period of analysis is short, it is important to monitor the literature to see if this is an actual trend.
Fig. 9. Orchestrator’s control topology. (a) Centralized; (b) Decentralized; (c) Distributed. Adapted from [79].
Fig. 9. Orchestrator’s control topology. (a) Centralized; (b) Decentralized; (c) Distributed. Adapted from [79].
Fig. 10. Fog architecture layers where orchestration operates (inside red lines): (a) only Fog Layer; (b) Fog and Cloud Layers; (c) all Layers.
Fig. 10. Fog architecture layers where orchestration operates (inside red lines): (a) only Fog Layer; (b) Fog and Cloud Layers; (c) all Layers.
Table 3. Analyzed Papers’ Answers to Research Questions
Table 3. Analyzed Papers’ Answers to Research Questions

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Internet of Things (IoT) is the developing technology that enables devices to communicate without human interaction. IoT utilizes cloud computing services to collect and process data for IoT devices and to manage the device remotely. Cloud computing is not efficient enough to handle the fast stream of data produced by the IoT, therefore scaling up IoT applications to meet demands of high peak becomes easier and highly automated in fog computing. Containers are mostly used as virtualization solutions for IoT in fog computing. It enables the execution of small microservices to large applications. However, the rise of many lightweight containers has resulted in new application architectures and fundamentally changing how applications are deployed and visualized. Due to this change, container orchestration tools were proposed. These tools allow users to coordinate and manage containers. However, container orchestration tools need to meet the requirements of IoT applications and constraints imposed on the nodes in fog. This paper presents a systematic literature review on the selection of orchestration tools for the efficient deployment of IoT applications in fog computing. Moreover, the performance of IoT applications must be considered by applying different metrics. This paper aims to propose potential research directions to address identified gaps in the selection of orchestration tools.

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Fog Computing Service Orchestration Mechanisms for 5G Networks
Stojan Kitanov

Journal of Internet Technology, 2018

5G network will enable new future Internet of Services paradigms such as Anything as a Service, where devices, terminals, machines, also smart things and robots will become innovative tools that will produce and will use applications, services and data. However, the emerging applications in the context of the Internet of Everything introduce high mobility, high scalability, real-time, and low latency requirements that raise new challenges on the services being provided to the users. Fortunately, Fog Computing or briefly Fog, which extends Cloud Computing to the edge of the network, with its service orchestration mechanisms offers virtually unlimited dynamic resources for computation, storage and service provision, that will effectively cope with the requirements of the forthcoming services. 5G in the fog computing environment will create opportunities for companies to deploy many new real-time services that cannot be delivered over current mobile and wireless networks. This paper ev...

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Systematic Mapping on Orchestration of Container-based Applications in Fog Computing
Rubens S Matos

2019 15th International Conference on Network and Service Management (CNSM), 2019

There is an increasing number of Internet of Things (IoT) devices in the border of computer networks, requiring local processing and lightweight virtualization to deal with issues such as heterogeneity, Quality of Service (QoS) management, scalability, mobility, federation, and interoperability. Fog computing can provide the computational resources required by IoT devices to process their data. Low energy consumption and total cost of ownership are among the desirable properties for auxiliar infrastructures such as those deployed for fog computing, which do not require large computational power though. There is a noteworthy trend of undergoing research efforts towards the definition of software and hardware architectures for fog computing in this context. In this sense, this paper presents a Systematic Literature Mapping with the purpose of understanding and identifying metrics and gaps in current literature about orchestration of container-based applications, especially those hosted in clusters of Single Board Computer (SBC) platforms, such as Raspberry Pi, which have been used for deploying fog computing environments.

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Related topics

  • Computer Science
  • Distributed Computing
  • Data Science
  • Edge Computing

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    Distributed Agent-Based Orchestrator Model for Fog Computing
    Agnius Liutkevicius

    Sensors

    Fog computing is an extension of cloud computing that provides computing services closer to user end-devices at the network edge. One of the challenging topics in fog networks is the placement of tasks on fog nodes to obtain the best performance and resource usage. The process of mapping tasks for resource-constrained devices is known as the service or fog application placement problem (SPP, FAPP). The highly dynamic fog infrastructures with mobile user end-devices and constantly changing fog nodes resources (e.g., battery life, security level) require distributed/decentralized service placement (orchestration) algorithms to ensure better resilience, scalability, and optimal real-time performance. However, recently proposed service placement algorithms rarely support user end-device mobility, constantly changing the resource availability of fog nodes and the ability to recover from fog node failures at the same time. In this article, we propose a distributed agent-based orchestrator...

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    UAV Support for Mission Critical Services
    sławomir kukliński

    Energies

    Mission critical solutions are essential for providing communications and services in the case of the troubles with connectivity that are often found in infrastructure-based solutions. Such solutions are typically used in the case of disasters, lack of energy, etc. There exist several narrowband solutions that provide countrywide coverage in certain countries. In recent years, the activities related to creating mission-critical broadband solutions based on Long Term Evolution (LTE) have led to the definition of LTE Mission Critical (LTE-MC). Both solutions ignore virtualization and require dedicated mobile terminals as a part of the mission-critical communication solution. This paper describes the opportunities, open issues and a proposal of a solution that exploits Unmanned Aerial Vehicles (UAVs) and network virtualization for mission-critical services. The presented approach combines Cloud/Edge and Fog orchestration to efficiently use all the available resources, including virtual...

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    An Optimized, Dynamic, and Efficient Load-Balancing Framework for Resource Management in the Internet of Things (IoT) Environment
    Dr. Shadab Alam

    Electronics, 2023

    This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

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    Computational Resource Allocation in Fog Computing: A Comprehensive Survey
    Breno Costa

    ACM Computing Surveys

    Fog computing is a paradigm that allows the provisioning of computational resources and services at the edge of the network, closer to the end devices and users, complementing cloud computing. The heterogeneity and large number of devices are challenges to obtaining optimized resource allocation in this environment. Over time, some surveys have been presented on resource management in fog computing. However, they now lack a broader and deeper view about this subject, considering the recent publications. This article presents a systematic literature review with a focus on resource allocation for fog computing, and in a more comprehensive way than the existing works. The survey is based on 108 selected publications from 2012 to 2022. The analysis has exposed their main techniques, metrics used, evaluation tools, virtualization methods, architecture, and domains where the proposed solutions were applied. The results show an updated and comprehensive view about resource allocation in fo...

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