FogBus2

Deng, Qifan; Goudarzi, Mohammad; Buyya, Rajkumar

doi:10.1145/3460866.3461768

Cited by 27 publications

(11 citation statements)

References 10 publications

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“…Tuli et al [34] introduced FogBus framework to harness edge/Fog and remote Cloud resources for the placement of applications developed as a collection of inter-connected modules. Deng et al [16] proposed FogBus2, a resource management framework for the deployment of containerised applications across edge and Cloud resources that are interconnected to each other using a VPN network. Wang et al [35] improved Fog-Bus2 and integrated container orchestration capabilities to the framework using Kubernetes.…”

Section: Existing Fog Frameworkmentioning

confidence: 99%

“…Afterwards, the CE starts processing the PRs using the placement algorithm (lines [16][17][18][19]. As the CE can support integration of placement algorithms either by extending the existing CE or as an external microservice, the algorithm can be configured as a property of the CE.…”

Section: Pr Processing Flow Of Microfog-cementioning

confidence: 99%

“…Due to the lack of such platforms for Fog computing, Fog application placement policies are primarily evaluated using numerical evaluations and simulators. Although several real-world frameworks are available to manage Fog resources [16,17], they have limitations related to Microservices-based IoT application placement. They lack support for the dynamic composition of microservices across federated Fog and Cloud data centres, easy integration of distributed placement policies, compatibility with open-source cloud-native technologies, support for heterogeneous microservices-based applications, ease of setup and prototyping support, etc.…”

Section: Introductionmentioning

confidence: 99%

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MicroFog: A Framework for Scalable Placement of Microservices-based IoT Applications in Federated Fog Environments

Pallewatta¹,

Kostakos²,

Buyya³

2023

Preprint

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MicroService Architecture (MSA) is gaining rapid popularity for developing large-scale IoT applications for deployment within distributed and resource-constrained Fog computing environments. As a cloudnative application architecture, the true power of microservices comes from their loosely coupled, independently deployable and scalable nature, enabling distributed placement and dynamic composition across federated Fog and Cloud clusters. Thus, it is necessary to develop novel placement algorithms that utilise these microservice characteristics to improve the performance of the applications. However, existing Fog computing frameworks lack support for integrating such placement policies due to their shortcomings in multiple areas, including MSA application placement and deployment across multi-fog multi-cloud environments, dynamic microservice composition across multiple distributed clusters, scalability of the framework to operate within federated environments, support for deploying heterogeneous microservice applications, etc. To this end, we design and implement MicroFog, a Fog computing framework compatible with cloud-native technologies such as Docker, Kubernetes and Istio. MicroFog provides an extensible and configurable control engine that executes placement algorithms and deploys applications across federated Fog environments. Furthermore, MicroFog provides a sufficient abstraction over container orchestration and dynamic microservice composition, thus enabling users to easily incorporate new placement policies and evaluate their performance. The capabilities of the MicroFog framework, such as the scalability and flexibility of the design and deployment architecture of MicroFog and its ability to ensure the deployment and composition of microservices across distributed fog-cloud environments, are validated using multiple use cases. Experiments also demonstrate MicroFog's ability to integrate and evaluate novel placement policies and load-balancing techniques. To this end, we integrate multiple microservice placement policies to demonstrate MicroFog's ability to support horizontally scaled placement, service discovery and load balancing of microservices across federated environments, thus reducing the application service response time up to 54%.

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Section: Existing Fog Frameworkmentioning

confidence: 99%

Section: Pr Processing Flow Of Microfog-cementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MicroFog: A Framework for Scalable Placement of Microservices-based IoT Applications in Federated Fog Environments

Pallewatta¹,

Kostakos²,

Buyya³

2023

Preprint

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“…Besides, the connection establishment stage of the FL requires various interaction models and online information about the whole system. To satisfy these requirements, the FogBus2 framework 31 is chosen as the underlying resource management framework.…”

Section: Background and Related Workmentioning

confidence: 99%

FLight: A lightweight federated learning framework in edge and fog computing

Zhu,

Goudarzi,

Buyya

2023

Softw Pract Exp

Self Cite

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The number of Internet of Things (IoT) applications, especially latency‐sensitive ones, have been significantly increased. So, cloud computing, as one of the main enablers of the IoT that offers centralized services, cannot solely satisfy the requirements of IoT applications. Edge/fog computing, as a distributed computing paradigm, processes, and stores IoT data at the edge of the network, offering low latency, reduced network traffic, and higher bandwidth. The edge/fog resources are often less powerful compared to cloud, and IoT data is dispersed among many geo‐distributed servers. Hence, Federated Learning (FL), which is a machine learning approach that enables multiple distributed servers to collaborate on building models without exchanging the raw data, is well‐suited to edge/fog computing environments, where data privacy is of paramount importance. Besides, to manage different FL tasks on edge/fog computing environments, a lightweight resource management framework is required to manage different incoming FL tasks while does not incur significant overhead on the system. Accordingly, in this article, we propose a lightweight FL framework, called FLight, to be deployed on a diverse range of devices, ranging from resource‐limited edge/fog devices to powerful cloud servers. FLight is implemented based on the FogBus2 framework, which is a containerized distributed resource management framework. Moreover, FLight integrates both synchronous and asynchronous models of FL. Besides, we propose a lightweight heuristic‐based worker selection algorithm to select a suitable set of available workers to participate in the training step to obtain higher training time efficiency. The obtained results demonstrate the efficiency of the FLight. The worker selection technique reduces the training time of reaching 80% accuracy by 34% compared to sequential training, while asynchronous one helps to improve synchronous FL training time by 64%.

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“…The internet of things (IoT) can generally work on multiple platforms and can be used in various applications (Deng et al, 2021). It has inspired the development of numerous technologies, including water quality monitoring systems (Gulati et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Water Quality Monitoring System for Temperature, pH, Turbidity, DO, BOD, and COD Parameters Based on Internet of Things in Garang Watershed

Syafrudin,

Sarminingsih,

Juliani

et al. 2024

Ecol. Eng. Environ. Technol.

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Water has recently become a final disposal site for wastewater. Land use has evolved with the global population growth and is generally transformed into settlements and industrial areas. These land use changes could potentially increase wastewater generation from both domestic and non-domestic activities. The Garang watershed, one of the watersheds in Central Java, flows through the Semarang Regency, Kendal Regency, and Semarang City. This study analyzed the water quality conditions in the Garang watershed and designed a real-time water quality monitoring system. The methods used in this study included SWMM modeling, the national sanitation foundation water quality index (NSF-WQI), and the internet of things. Samples were collected from 10 points in the Garang Watershed, with a sampling frequency of five times at each point. The results of the data analysis demonstrated that the differences in land use resulted in varying water parameter levels. The results of the SWMM modeling demonstrated an acceptable model value (NOF between 0 and 1). On the other hand, the WQI analysis results demonstrated that the quality status at the Garang watershed is "medium" at nearly all location points. The designed real-time water quality sensor tool successfully transmitted water quality data online and in real-time, particularly for temperature, pH, turbidity, and DO. This water quality monitoring system offers a variable percentage error value, with the pH sensor ranging between 0.16% and 9.86% and the temperature sensor ranging between 0.64% and 1.72%.

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FogBus2

Cited by 27 publications

References 10 publications

MicroFog: A Framework for Scalable Placement of Microservices-based IoT Applications in Federated Fog Environments

MicroFog: A Framework for Scalable Placement of Microservices-based IoT Applications in Federated Fog Environments

FLight: A lightweight federated learning framework in edge and fog computing

Water Quality Monitoring System for Temperature, pH, Turbidity, DO, BOD, and COD Parameters Based on Internet of Things in Garang Watershed

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