Data and Computation Movement in Fog Environments: The DITAS Approach

Plebani, Pierluigi; García-Pérez, David; Anderson, Maya; Bermbach, David; Cappiello, Cinzia; Kat, Ronen I.; Marinakis, Achilleas; Moulos, Vrettos; Pallas, Frank; Tai, Stefan; Vitali, Monica

doi:10.1007/978-3-319-94890-4_12

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…Furthermore, this is the first model of its kind to introduce a concrete methodology for WSN, extended in collaboration with the fog environment. As pointed [36][37][38][39][40][41] and Fernando et al [29], optimization in IoT applications is an intriguing research challenge. Here, this proposed scheme utilizes novel techniques, such as a novel checkpoint (CP) technique, along with the novel particle bee optimization.…”

Section: Resultsmentioning

confidence: 96%

Opportunities of IoT in Fog Computing for High Fault Tolerance and Sustainable Energy Optimization

Reyana,

Kautish,

Alnowibet

et al. 2023

Sustainability

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Today, the importance of enhanced quality of service and energy optimization has promoted research into sensor applications such as pervasive health monitoring, distributed computing, etc. In general, the resulting sensor data are stored on the cloud server for future processing. For this purpose, recently, the use of fog computing from a real-world perspective has emerged, utilizing end-user nodes and neighboring edge devices to perform computation and communication. This paper aims to develop a quality-of-service-based energy optimization (QoS-EO) scheme for the wireless sensor environments deployed in fog computing. The fog nodes deployed in specific geographical areas cover the sensor activity performed in those areas. The logical situation of the entire system is informed by the fog nodes, as portrayed. The implemented techniques enable services in a fog-collaborated WSN environment. Thus, the proposed scheme performs quality-of-service placement and optimizes the network energy. The results show a maximum turnaround time of 8 ms, a minimum turnaround time of 1 ms, and an average turnaround time of 3 ms. The costs that were calculated indicate that as the number of iterations increases, the path cost value decreases, demonstrating the efficacy of the proposed technique. The CPU execution delay was reduced to a minimum of 0.06 s. In comparison, the proposed QoS-EO scheme has a lower network usage of 611,643.3 and a lower execution cost of 83,142.2. Thus, the results show the best cost estimation, reliability, and performance of data transfer in a short time, showing a high level of network availability, throughput, and performance guarantee.

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Section: Resultsmentioning

confidence: 96%

Opportunities of IoT in Fog Computing for High Fault Tolerance and Sustainable Energy Optimization

Reyana,

Kautish,

Alnowibet

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…Lin et al [28] Global Data Plane [65] fog-ready IPFS [9][10][11] PathStore [34][35][36] Naas et al [37,38] FogStore [19,20,30] Virtual Data Containers [47] Psaras et al [48] Griffin [61] Custom Data Management…”

Section: Global Mappingmentioning

confidence: 99%

“…Plebani et al [47] introduce the idea of Virtual Data Containers that group coherent data items. These containers can then be moved around depending on where data is needed, providing a flexible interface for controlled data movement through the fog.…”

Section: Fog-nativementioning

confidence: 99%

Managing Data Replication and Distribution in the Fog with FReD

Pfandzelter¹,

Nils²,

Schirmer³

et al. 2023

Preprint

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The heterogeneous, geographically distributed infrastructure of fog computing poses challenges in data replication, data distribution, and data mobility for fog applications. Fog computing is still missing the necessary abstractions to manage application data, and fog application developers need to re-implement data management for every new piece of software. Proposed solutions are limited to certain application domains, such as the IoT, are not flexible in regard to network topology, or do not provide the means for applications to control the movement of their data.In this paper, we present FReD, a data replication middleware for the fog. FReD serves as a building block for configurable fog data distribution and enables low-latency, high-bandwidth, and privacy-sensitive applications. FReD is a common data access interface across heterogeneous infrastructure and network topologies, provides transparent and controllable data distribution, and can be integrated with applications from different domains. To evaluate our approach, we present a prototype implementation of FReD and show the benefits of developing with FReD using three case studies of fog computing applications.

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“…Plebani et al 52 introduce the idea of Virtual Data Containers that group coherent data items. These containers can then be moved around depending on where data is needed, providing a flexible interface for controlled data movement through the fog.…”

Section: Related Workmentioning

confidence: 99%

Managing data replication and distribution in the fog with FReD

et al. 2023

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SummaryThe heterogeneous, geographically distributed infrastructure of fog computing poses challenges in data replication, data distribution, and data mobility for fog applications. Fog computing is still missing the necessary abstractions to manage application data, and fog application developers need to re‐implement data management for every new piece of software. Proposed solutions are limited to certain application domains, such as the IoT, are not flexible in regard to network topology, or do not provide the means for applications to control the movement of their data. In this paper, we present FReD, a data replication middleware for the fog. FReD serves as a building block for configurable fog data distribution and enables low‐latency, high‐bandwidth, and privacy‐sensitive applications. FReD is a common data access interface across heterogeneous infrastructure and network topologies, provides transparent and controllable data distribution, and can be integrated with applications from different domains. To evaluate our approach, we present a prototype implementation of FReD and show the benefits of developing with FReD using three case studies of fog computing applications.

show abstract

Data and Computation Movement in Fog Environments: The DITAS Approach

Cited by 6 publications

References 19 publications

Opportunities of IoT in Fog Computing for High Fault Tolerance and Sustainable Energy Optimization

Opportunities of IoT in Fog Computing for High Fault Tolerance and Sustainable Energy Optimization

Managing Data Replication and Distribution in the Fog with FReD

Managing data replication and distribution in the fog with FReD

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