An autonomous <scp>IoT</scp> service placement methodology in fog computing

Ayoubi, Masoumeh; Ramezanpour, Mohammadreza; Khorsand, Reihaneh

doi:10.1002/spe.2939

Cited by 33 publications

(14 citation statements)

References 34 publications

(42 reference statements)

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“…Similar to ours, Ayoubi et al [1] model equations for service latency, fog utilization, communication and computation costs as their key optimization objectives. He et al [14] categorize edge resources based on shareable (storage) and non-shareable (CPU cycles, bandwidth) resources for optimal provisioning of services and request scheduling.…”

Section: Dynamic Service Placementmentioning

confidence: 96%

“…The TMatrix (𝜏) and edge site capacity (𝑒 𝑐𝑎𝑝 ) constraints are given as input to mipSolver which uses Google's assignment solver 1 . Based on these inputs, we construct constraints for the MIP solver to generate the initial placement solution, mapping one microservice to exactly one server while minimizing the overall cost.…”

Section: Algorithm 34: Misp Algorithmmentioning

confidence: 99%

“…Learning based: Ayoubi et al [1] propose a four-stage MADE strategy which consists of monitoring, analysis, decision making and execution phases. Ouyang et al [20] take an adaptive usercentric approach towards service placement which also considers user-mobility and user's preferences, and which uses an online learning based algorithm.…”

Section: Dynamic Service Placementmentioning

confidence: 99%

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Heuristic and Reinforcement Learning Algorithms for Dynamic Service Placement on Mobile Edge Cloud

Garg¹,

Narendra²,

Tesfatsion³

2021

Preprint

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Edge computing hosts applications close to the end users and enables low-latency real-time applications. Modern applications inturn have adopted the microservices architecture which composes applications as loosely coupled smaller components, or services. This complements edge computing infrastructure that are often resource constrained and may not handle monolithic applications. Instead, edge servers can independently deploy application service components, although at the cost of communication overheads. Dynamic system load in mobile network cause like latency, jitter, and packet loss to fluctuate frequently. Consistently meeting application service level objectives while also optimizing application deployment (placement and migration of services) cost and communication overheads in mobile edge cloud environment is non-trivial. In this paper we propose and evaluate three dynamic placement strategies, two heuristic (greedy approximation based on set cover, and integer programming based optimization) and one learning-based algorithm. Their goal is to satisfy the application constraints, minimize infrastructure deployment cost, while ensuring availability of services to all clients and User Equipment (UE) in the network coverage area. The algorithms can be extended to any network topology and microservice based edge computing applications. For the experiments, we use the drone swarm navigation as a representative application for edge computing use cases. Since access to real-world physical testbed for such application is difficult, we demonstrate the efficacy of our algorithms as a simulation. We also contrast these algorithms with respect to placement quality, utilization of clusters, and level of determinism. Our evaluation not only shows that the learning-based algorithm provides solutions of better quality, it also provides interesting conclusions regarding when the (more traditional) heuristic algorithms are actually better suited.

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Section: Dynamic Service Placementmentioning

confidence: 96%

Section: Algorithm 34: Misp Algorithmmentioning

confidence: 99%

Section: Dynamic Service Placementmentioning

confidence: 99%

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Heuristic and Reinforcement Learning Algorithms for Dynamic Service Placement on Mobile Edge Cloud

Garg¹,

Narendra²,

Tesfatsion³

2021

Preprint

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“…Recently, the Fog Colony notion was introduced [9][10][11][12]. Fog colonies, as described in [9], are small-scale data centers made up from an arbitrary number of Fog Nodes.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, the Fog Colony notion was introduced [9][10][11][12]. Fog colonies, as described in [9], are small-scale data centers made up from an arbitrary number of Fog Nodes. The importance of the Fog Colony is based on the data sharing as well as the services' distribution among the Fog Nodes.…”

Section: Introductionmentioning

confidence: 99%

Context Diffusion in Fog Colonies: Exploring Autonomous Fog Node Operation Using ECTORAS

Nikolopoulos

Νικολαϊδου

Voreakou

et al. 2022

IoT

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In Fog Computing, fog colonies are formed by nodes cooperating to provide services to end-users. To enable efficient operation and seamless scalability of fog colonies, decentralized control over participating nodes should be promoted. In such cases, autonomous Fog Nodes operate independently, sharing the context in which all colony members provide their services. In the paper, we explore different techniques of context diffusion and knowledge sharing between autonomous Fog Nodes within a fog colony, using ECTORAS, a publish/subscribe protocol. With ECTORAS, nodes become actively aware of their operating context, share contextual information and exchange operational policies to achieve self-configuration, self-adaptation and context awareness in an intelligent manner. Two different ECTORAS implementations are studied, one offering centralized control with the existence of a message broker, to manage colony participants and available topics, and one fully decentralized, catering to the erratic topology that Fog Computing may produce. The two schemes are tested as the Fog Colony size is expanding in terms of performance and energy consumption, in a prototype implementation based on Raspberry Pi nodes for smart building management.

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PMRNA: Parameter matching of realtime and non‐realtime applications for resource provisioning in fog‐integrated cloud

Singh,

Vidyarthi

2024

Concurrency and Computation

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SummaryFog computing, an emerging technology, extends Cloud computing services to the network's edge in the proximity of the application request. This extension yields improvement in Bandwidth (BW) utilization, faster responses to Real‐Time (RT) and Internet of Things (IoT) requests, and the provision of the heterogeneous resource services. While extensive work has been conducted on resource allocation for RT and Non‐Real‐Time (NRT) requests separately in Fog as well as Cloud computing, there is limited focus on resource provisioning for mixed RT and NRT requests in the Fog‐integrated Cloud (FiC) environment. Moreover, the majority of the existing provisioning methods primarily consider parameters from the system's perspective, overlooking crucial user aspects such as deadline and request size. To address the gap, this work introduces a resource provisioning method named “Parameter Matching of Realtime and Non‐Realtime Applications (PMRNA),” which considers user parameters and resource information, gathered by the broker. The performance evaluation of the proposed model is done in CloudSim, using various combinations of RT and NRT requests along with diverse Fog and Cloud resource configurations. Evaluation metric includes average Execution Time (ET), average Waiting Time (WT), average Turn Around Time (TAT), and resource utilization. The experimental results demonstrate a significant reduction in both average WT and average TAT for the diverse pool of RT and NRT requests in the FiC compared to the Cloud‐only environment.

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An autonomous IoT service placement methodology in fog computing

Cited by 33 publications

References 34 publications

Heuristic and Reinforcement Learning Algorithms for Dynamic Service Placement on Mobile Edge Cloud

Heuristic and Reinforcement Learning Algorithms for Dynamic Service Placement on Mobile Edge Cloud

Context Diffusion in Fog Colonies: Exploring Autonomous Fog Node Operation Using ECTORAS

PMRNA: Parameter matching of realtime and non‐realtime applications for resource provisioning in fog‐integrated cloud

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